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The missing link between genetic association and regulatory function

Noah J Connally, Sumaiya Nazeen, Daniel Lee and 6 more

eLife · 2022 · CC BY

doi:10.7554/elife.74970

Abstract

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis -linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene–trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this ‘missing regulation.’

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 21, 2026. Paragraph numbers are ours, so an answer can point at one.

Introduction

Modern complex trait genetics has uncovered surprises at every turn, including the paucity of associations between traits and coding variants of large effect, and the ‘mystery of missing heritability,’ in which no combination of common and rare variants can explain a large fraction of trait heritability. Further work has revealed unexpectedly high polygenicity for most human traits and very small effect sizes for individual variants. Enrichment analyses have demonstrated that a large fraction of heritability resides in regions with gene regulatory potential, predominantly tissue-specific accessible chromatin and enhancer elements, suggesting that trait-associated variants influence gene regulation. Furthermore, genes in trait-associated loci are more likely to have genetic variants that affect their expression levels (expression quantitative trait loci, or eQTLs), and the variants with the strongest trait associations are more likely also to be associated with transcript abundance of at least one proximal gene. Combined, these observations have led to the inference that most trait-associated variants are eQTLs, and their effects arise from altering transcript abundance, rather than protein sequence. Equivalent sQTL (splice QTL) analyses of exon usage data have revealed a more modest overlap with trait-associated alleles, suggesting that a fraction of trait-associated variants influence splicing, and hence the relative abundance of different transcript isoforms, rather than overall expression levels. The genetic variant causing expression changes may lie outside the locus and involve a knock-on effect on gene regulation, with the variant altering transcript abundances for genes elsewhere in the genome (a trans-eQTL), but the consensus view is that trans-eQTLs are typically mediated by the variant influencing a gene in the region (a cis-eQTL). Thus, a model has emerged in which most trait-associated variants influence proximal gene regulation.

Here we argue that this unembellished model—in which genome-wide association study (GWAS) peaks are mediated by the effects on the homeostatic expression assayed in tissue samples—is the exception rather than the rule. We highlight the challenges of current strategies linking GWAS variants to genes and call for a reevaluation of the basic model in favor of more complex models possibly involving context-specificity with respect to cell types, developmental stages, cell states, or the constancy of expression effects.

Our argument begins with several observations that challenge the unembellished model. One challenge is the difference between spatial distributions of eQTLs, which are dramatically enriched in close proximity to genes, and GWAS peaks, which are usually farther away. Another is that expression levels mediate a minority of complex trait heritability. Finally, many studies have designed tools for colocalization analysis: a test of whether GWAS and eQTL associations are due to the same set of variants, not merely distinct variants in linkage disequilibrium. If the model is correct, most trait associations should also be eQTLs, but across studies, only 5–40% of trait associations colocalize with eQTLs.

Despite the doubts raised, the fact that most GWAS peaks do not colocalize with eQTLs cannot disprove the predominant, unembellished model. In a sense, negative colocalization results are confusing because their hypothesis is too broad. If we predict merely that GWAS peaks will colocalize with some genes’ expression, it is not clear what is meant by a peak’s failure to colocalize with any individual gene’s expression.

Thus, a narrower, more testable hypothesis requires identifying genes we believe a priori are biologically relevant to the GWAS trait. If these trait-linked genes have nearby GWAS peaks and eQTLs, failure to colocalize would be a meaningful negative result. Earlier studies tested all GWAS peaks; when a peak has no colocalization, the model is inconclusive. But trait-linked genes that fail to colocalize reveal that our method for detecting non-coding variation is, with current data, incompatible with our model for understanding it.

With this distinction in mind, we created a set of trait-associated genes capable of supporting or contradicting the model of non-coding GWAS associations acting as eQTLs. For this purpose, the selection of genes becomes extremely important. Because the model attempts to explain the genetic relationship between traits and gene expression, true positives cannot be selected based on measurements of genetic association to traits (GWAS) or expression (eQTL mapping). With this restriction, one source of true positives is to identify genes that are both in loci associated with a complex trait and are also known to harbor coding mutations tied to a related Mendelian trait or the same complex trait. Using a model not based on expression, Mendelian genes are enriched in common-variant heritability for cognate complex traits. The genes and their coding variants may be detected in familial studies of cognate Mendelian disorders or by aggregation in a burden test on the same complex phenotypes as GWAS.

For genes whose coding variants can cause detectable phenotypic change, the strong expectation is that a variant of small effect influences the gene identified by its rare coding variants. As an example, APOE and LDLR are both low-density lipoprotein receptor genes. Coding variants in APOE and LDLR can lead to the Mendelian disorder familial hypercholesterolemia. Even in the absence of a Mendelian coding variant, experiments in animal models have found that the overexpression of these genes reduces cholesterol levels. GWAS on human subjects have found significant associations near APOE and LDLR, so it seems reasonable to suspect that any non-coding effects in these loci may be mediated by these genes. This general relationship between Mendelian and complex traits is supported by several lines of evidence summarized in Appendix 1.

Results

To test the model that trait-associated variants influence baseline gene expression, we assembled a list of putatively causative genes for seven polygenic common traits with available large-scale GWAS data, each of which also has an extreme form in which coding variants of large effect alter one or more genes with well-characterized biology (Table 1). Our selection included four common diseases: type II diabetes (T2D), where early-onset familial forms are caused by rare coding mutations (insulin-independent MODY; neonatal diabetes; maternally inherited diabetes and deafness; familial partial lipodystrophy); ulcerative colitis (UC) and Crohn disease (CD), which have Mendelian pediatric forms characterized by severity of presentation; and breast cancer (BC), where germline coding mutations (e.g., BRCA1) or somatic tissue (e.g., PIK3CA) are sufficient for disease. We also chose three quantitative traits: low- and high-density lipoprotein levels (LDL and HDL); and height. Between known Mendelian genes and those from, our analysis included 220 unique gene-trait pairs (Figure 1).

In well-powered GWAS, even relatively rare large-effect coding alleles (mutations in BRCA1 that cause breast cancer, for instance) may be detectable as an association to common variants, which could make the effect of a coding variant appear to be regulatory instead. To account for this possibility, we computed association statistics in each GWAS locus conditional on coding variants. We applied a direct conditional test to datasets with available individual-level genotype data (height, LDL, HDL); for those studies without available genotype data, we computed conditional associations from summary statistics using COJO (;; ‘Materials and methods’). With both methods, the resulting GWAS associations should reflect only non-coding variants.

After controlling for coding variation, we examined whether these genes are more likely than chance to be in close proximity to variants associated with the polygenic form of each trait. In agreement with existing literature, we observe a significant enrichment for all traits in our combined Mendelian and gene sets (Figure 1—figure supplement 1).

Of our 220 genes, 147 (67%) fell within 1 Mb of a GWAS locus for the cognate complex trait, over three times as many as the 43 predicted by a random null model (95% confidence interval: 31.5–54.5). Our window of 1 Mb represents roughly the upper bound for distances identified between enhancer–promoter pairs, but most pairs are closer, so we would expect enrichment to increase as the window around genes decreases; this proves to be the case. At a distance of 100 kb, we find 104 putatively causative genes (47%), though the null model predicts only 11 (95% CI 4.5–17.0), an order-of-magnitude enrichment (Figure 1—figure supplement 1). Given their known causal roles in the severe forms of each phenotype, these results suggest that the 147 genes near GWAS signals are likely to be the targets of trait-associated non-coding variants. For example, we see a significant GWAS association between breast cancer risk and variants in the estrogen receptor (ESR1) locus even after controlling for coding variation; the baseline expression model would thus predict that non-coding risk alleles alter ESR1 expression to drive breast cancer risk.

We next looked for evidence that the trait-associated variants were also altering the expression of our 147 genes in relevant tissues. Controlling for the number of tests we conducted, 134 (91.1%) of these genes had an eQTL in at least one relevant tissue at a false discovery rate (FDR) of Q < 0.05 (‘Materials and methods’). If these variants act through changes in gene expression, phenotypic associations should be driven by some of the same variants as eQTLs in relevant tissue types. We therefore looked for co-localization between our GWAS signals and eQTLs in relevant tissues (Table 2) drawn from the GTEx Project using three well-documented methods: coloc, JLIM, and eCAVIAR. We found support for the colocalization of trait and eQTL association for only 7 genes out of 147 (4.8%) for coloc; 10/147 (6.8%) for JLIM; and 8/147 (5.4%) for eCAVIAR. Accounting for overlap, this represents only 18/220 putatively causative genes (8.2%) or 18/147 (12.2%) putatively causative genes near GWAS peaks, even without full multiple-hypothesis testing correction (‘Materials and methods’), which is not obviously better than random chance. We note that prior estimates of the fraction of GWAS associations colocalizing with eQTLs (25–40%;;;; ) do not directly evaluate the ability to find causative genes. By contrast, our estimate of the number of putatively causative genes that colocalize with eQTLs tests the consistency of our knowledge, models, and data.

A potential weakness of our approach is the restriction of our search to predefined tissues. We believe this is necessary in order to avoid the disadvantages of testing each gene–trait pair in each tissue—either a large number of false positives or a severe multiple-testing correction that may lead to false negatives. However, restricting to the set of tissues with a known biological role and available expression data almost certainly leaves out tissues with relevance in certain contexts. Some of the tissues we do use have smaller sample sizes, limiting their power to detect eQTLs with smaller effects.

To address potential shortcomings from the available sample of tissue contexts, we incorporated the Multivariate Adaptive Shrinkage Method (MASH). MASH is a Bayesian method that takes genetic association summary statistics measured across a variety of conditions and, by determining patterns of similarity across conditions, updates the summary statistics of each individual condition. In our case, if an eQTL is difficult to find in a tissue of interest, incorporating information from other tissues may help us detect it. Unlike meta-analysis, this method generates summary statistics that still correspond to a specific tissue.

We ran MASH on every locus used in our earlier analysis using data from all non-brain GTEx tissues (‘Materials and methods’). Rerunning coloc with these modified statistics increased the number of GWAS-eQTL colocalizations across all genes by 26% (from 389 to 489). However, the 100 new colocalizations identified only four additional putatively causative genes (Figure 1—figure supplement 2). These results indicate that tissue-type selection was not the limiting factor in our analysis.

Transcriptome-wide association studies (TWAS) are another class of methods applied to identify causative genes under GWAS peaks using gene expression. TWAS measures genetic correlation between traits and is not designed to avoid correlations caused by LD, which gives it higher power in the case of allelic heterogeneity or poorly typed causative variants. However, while sensitive, TWAS analyses typically yield expansive result sets that include many false positives and are sensitive to the number of tissue types. Results from the FUSION implementation of TWAS across all tissues identified our putatively causative genes as likely tied to the GWAS peak in 66/220 loci (30%). However, only 4/220 (1.8%) genes were identified by FUSION when we restricted the analysis to relevant tissues.

Given the paucity of expression-mediated GWAS peaks, we asked whether GWAS variants in our study loci reside in likely regulatory sites. Taking the 128 genes in the Mendelian subset of putatively causative genes, we fine-mapped each nearby GWAS association using the SuSiE algorithm. For 37 of these genes, we identified at least one high-confidence fine-mapped variant (defined as a variant with posterior inclusion probability, or PIP, greater than 0.7) within 100 kb of the transcription start site. We tested whether these fine-mapped variants fall within (i) regulatory DNA marked by DNase I hypersensitive sites (DHS); (ii) a narrowly mapped active histone modification feature (H3K27ac, H3K4me1, or H3K4me3; ); or (iii) sites marked as an ‘enhancer’ by ChromHMM (;; ‘Materials and methods’). As many as 32/37 (86%) genes identified this way have a fine-mapped variants within a candidate regulatory region across all the tissue types examined, or 25/37 (68%) when restricting to trait-relevant tissues (Figure 2); (Supplementary file 1 and Supplementary file 2). Despite this strong evidence that these GWAS associations arise from effects on regulatory DNA, only 5/25 loci (20%) demonstrably correspond to expression effects in our eQTL analysis.

In order to more analogously compare our regulatory feature analysis to our eQTL analysis, we computed ‘activity-by-distance’ (ABD)—a simplification of the 'activity-by-contact' method that provides a measure connecting a distal regulatory region to a target gene (;; 'Materials and methods' Figure 3). Taking each locus’s feature with the highest ABD score, we implicate 5/37 (14%) of our Mendelian subset of genes. As such, even when a GWAS association and trait-relevant gene are in the same locus, they are difficult to link, whether using eQTLs or current approaches to integrating chromatin data with target genes.

Discussion

Overall, our results are strongly consistent with the idea that complex traits are governed by non-coding genetic variants whose effects on phenotype are mediated by their contribution to the regulation of nearby genes. However, these same results are inconsistent with a model that such effects on gene regulation arise from the genetic influences on baseline gene expression that are captured by eQTLs.

The enrichment of putatively causative genes—selected based on existing biological knowledge—near GWAS peaks supports their role in complex traits. Additionally, the enrichment of fine-mapped GWAS variants in likely regulatory regions marked by DHS and other chromatin features lends support to the general model of GWAS associations arising from effects on gene regulation and expression. However, the inability of varied statistical methods to connect GWAS associations and expression argues against the idea that the causative GWAS variants exert their effects via the homeostatic or blended effects on gene expression that are captured by bulk-tissue eQTLs of the sort discovered by broad expression-data collection projects.

Many explanations have been suggested for the limited success of expression-centric methods to explain the mechanisms of GWAS variants. Undirected, broad approaches—including most GWAS-eQTL linking studies—are designed to be largely independent of a priori biological knowledge and hypotheses. This unconstrained focus is ideal for discovery because it delivers the largest number of positive findings, but it is ill-suited for providing explanations for negative results—when you do not know what you were looking for, it is hard to explain why you did not find it. By testing only loci for which there is a strongly suspected contributing gene, we were better able to distinguish which factors might prevent us from identifying GWAS-to-gene links using expression alone.

As a result, we conclude that a number of explanations often considered when evaluating expression-based variant-to-gene methods are not applicable in the context we examined. These include (i) non-expression-mediated mechanisms, (ii) lack of statistical power for GWAS, (iii) the absence of eQTLs for relevant genes, (iv) and underpowered methods for linking expression to GWAS (Table 3).

Our results suggest that there remains to be discovered a major component connecting variants to gene expression, which we term 'missing regulation.' We propose that this currently lacking component could be exposed by considering more nuanced experimental and analytical models of gene expression. One likely model involves context-dependent gene expression—expression whose relevant effects are confined to (i) particular refined cell types or anatomical partitions, or (ii) cell states such as responses to perturbations, whether exogenous (e.g., environmental change) or endogenous (e.g., cell differentiation). A complementary model may incorporate heterogeneity of gene expression or the variance of expression across relatively short time scales. These models and others may depend on or be augmented by thresholding or buffering of variant effects, which may produce changes in gene expression with nonlinear effects on phenotype. A summary of proposed models can be found in Table 4.

We propose that finding the 'missing regulation' will require not only identifying novel eQTLs explaining GWAS peaks, but also explaining the phenotypic irrelevance of 'red herring eQTLs'—that is, eQTLs for putatively causative genes that fall near GWAS peaks but do not colocalize with them. Above, we use the example genes APOE and LDLR. Both these genes harbor coding variants causing Mendelian hypercholesterolemia, and both have non-coding variants that GWAS have tied to LDL levels. Both have eQTLs in trait-relevant tissues. For APOE, these points cohere into an explanation: the LDL association is an eQTL for the lipid-binding gene. But for LDLR—and for most genes—the association, the mechanism, and the gene cannot be tied together.

Importantly, our results do not diminish the importance or general utility of eQTLs. Rather, they suggest that current models are deficient in two respects: (i) they fail to unify trait-associated non-coding variants with known trait-associated genes, and (ii) they fail to explain the non-effects of identified 'red herring' eQTLs. These deficiencies highlight a need for new approaches to the role of gene regulation in complex traits.

One long-standing goal of GWAS has been to discover genes contributing to complex traits, but low rates of positive findings for expression-based variant-to-gene methods have constrained this possibility. Among other challenges, this has limited the benefit of GWAS and expression data for disease-gene mapping and drug discovery. Another practical question raised is the value of current large-scale public datasets. Compared to genotypes, expression data are relatively difficult to collect, particularly from specialized cell contexts. If the most relevant models are shown to depend on effects not observable in homeostatic eQTL mapping, the field may need to consider prioritizing other biological contexts and forms of expression data.

Gene selection

By manual literature search, we selected 128 genes harboring large-effect-size coding variants for one of the seven phenotypes (Table 1; specifically, we selected 128 gene–trait pairs, representing 121 unique genes). These genes were identified using familial studies, rare disease exome-sequencing analyses, and, for breast cancer, using the MutPanning method (citations for each gene are included in Table 1). Review papers, as well as the OMIM database, were generally used as starting points, but an examination of the original literature was needed to confirm genes’ suitability. For example, though SMC3 is known to cause Cornelia de Lange syndrome, which is characterized in part by short stature, SMC3 mutations lead to a milder form of the syndrome, usually without a marked reduction in stature. Several of these phenotypes—height, HDL, cholesterol, breast cancer, and type II diabetes—were also analyzed in, which, through burden testing, identified a total of 110 genes; after accounting for overlaps, this increased our set of putatively causative genes to 220.

The inclusion of genes from Backman et al. ensures that our results are not dependent on an undetected bias in our selection. The set of genes chosen from familial studies offered the advantage that it was selected based on independent methods and data distinct from the large-scale genotyping studies that have characterized the GWAS era. The tradeoff to this was the impossibility of selecting genes through a fully systematic and non-arbitrary process. Because this work was performed in the UKBB, there is some overlap between their data and ours. However, our work did not use exomes, and most of the variants driving their findings are too rare to influence GWAS results. When this is not the case, our decision to condition on coding variants should make the effects used in our work independent from their findings.

Identifying coding variants

Because GWAS sample sizes are large enough to detect the low-frequency coding variants used to select some of our genes, it is possible that a coding variant would distort the association signal of nearby eQTLs. To minimize this concern, we removed the effects of coding variants on GWAS. Many variants can fall within coding sequences in rare splice variants, so it is important to remove only those variants that appear commonly as coding. These coding variants were selected based on the pext (proportion of expression across transcripts) data. Two filters were used. First, we removed genes whose expression in a trait-relevant tissue was below 50% of their maximum expression across tissues. Second, we removed variants that fell within the coding sequence of less than 25% of splice isoforms in that tissue. The remaining variants were used to correct GWAS signal, as explained below. The code for this analysis, and all other quantitative analyses in this paper, can be accessed at https://github.com/NJC12/missing_link_association_function (; copy archived at swh:1:rev:46d9072b7cc13f6532203d1494eec4d0f634e092).

GWAS

For height, LDL cholesterol, and HDL cholesterol, GWAS were performed using genotypic and phenotypic data from the UKBB. In order to avoid confounding, we restricted our sample to the 337K unrelated individuals with genetically determined British ancestry identified by. The GWAS were run using Plink 2.0, with the covariates age, sex, body mass index (for LDL and HDL only), 10 principal components, and coding variants.

Conditional analysis

Because UKBB has limited power for breast cancer, Crohn disease, ulcerative colitis, and type II diabetes, we used publicly available summary statistics. The Conditional and Joint Analysis (COJO) program can condition summary statistics on selected variants—in our case, coding variants—by using an LD reference panel. For this reference, we used TOPMed subjects of European ancestry. The ancestry of these subjects was confirmed with FastPCA, and the relevant data were extracted using bcftools. Our conditional GWAS data are available at doi:10.5061/dryad.612jm644q.

Enrichment analysis

At each distance, the number of Mendelian and non-Mendelian genes within that window around GWAS peaks are counted. p-Values were calculated using Fisher’s exact test (Figure 1, Figure 1—figure supplement 1). Because Mendelian genes may be unusually important beyond our chosen traits, we conduct a set of controls by measuring the enrichment of non-matching Mendelian and complex traits (CD genes and BC GWAS; BC genes and LDL GWAS; LDL genes and UC GWAS; UC genes and height GWAS; height genes and T2D GWAS; T2D genes and HDL GWAS; HDL genes and CD GWAS).

eQTL detection

eQTL summary statistics were taken from GTEx v7. Some methods detect colocalization with variants that are individually significant, but would not pass a genome-wide threshold. Because we tested only a subset of genes, we used the Benjamini–Hochberg method to calculate the FDR based on the number of tests we conducted multiplied by a correction factor to account for variants that are tested in combination with a gene but are not reported (a factor of 20 closely matched the genome-wide FDR results for GTEx). With this method, 204/220 (93%) of our genes displayed an eQTL, including 134/147 genes with a nearby GWAS peak (91%). Even using the FDR statistics of the GTEx project—which are based on the assumption of testing every gene in every tissue—107/220 (49%) of our genes and 76/147 (52%) of genes near GWAS peaks had an eQTL at Q < 0.05.

Colocalization

JLIM was run using GWAS summary statistics and GTEx v7 genotypes and phenotypes for the tissues listed in Table 2. Coloc was run using GWAS and GTEx v7 summary statistics for the same tissues. eCAVIAR was run using GWAS and GTEx v7 summary statistics for these tissues, and a reference dataset of LD from UKBB. MASH was run incorporating data from all non-brain tissues, and coloc was rerun using the adjusted values for the same tissues as before.

MASH

MASH was applied to all GTEx tissues using the mashr R-package. We restricted this model to non-brain tissues—which include all of our trait-selected tissues—due to the known tendency of brain and non-brain tissues to cluster separately in expression analysis.

Fusion (TWAS)

We used the FUSION implementation of TWAS, which accounts for the possibility of multiple cis-eQTLs linked to the trait-associated variant by jointly calling sets of genes predicted to include the causative gene, to interrogate our 220 loci. FUSION included our putatively causative genes in the set identified as likely relevant to the GWAS peak in 66/220 loci (30%). However, interpretation of this TWAS result is difficult. For many complex traits, TWAS returns a large number of findings (e.g., >150 for LDL cholesterol and >4800 for height). This is in part due to the multiple genes jointly returned at a locus and can also be a result of the large number of tissues and cell types included in the implementation of FUSION. Most hits are found in tissues without any clear relevance to the trait and absent in relevant tissues—LDL, for example, has more TWAS associations between expression and eQTL in prostate adenocarcinoma (24 genes associated), brain prefrontal cortex (23 genes associated), and transformed fibroblasts (21 genes associated) than it does in adipose (16 genes associated), blood (11 genes associated), or liver (5 genes associated). Individual genes were often identified as hits in multiple tissues, but with an inconsistent direction of effect—that is, increased gene expression correlated with an increase in the quantitative trait or disease risk in some tissues, but a decrease in others, which suggests that the gene in question may not be the one whose expression contributes to the complex trait. Because of this possibility and the known biological role of many of our genes, we restricted our results to tissues with established relevance to our traits.

Fine-mapping GWAS hits

We fine-mapped the GWAS variants located within ±100 kb of our putatively causative genes by applying the SuSiE algorithm on the unconditional summary statistics from the GWAS of breast cancer, Crohn disease, ulcerative colitis, type II diabetes, height, LDL cholesterol, and HDL cholesterol. An LD reference panel from UKBB subjects of European ancestry was used for this analysis. Fine-mapped variants were annotated using snpEff (v4.3t). Only non-coding variants were kept for further analysis.

Functional genomic annotation of fine-mapped hits

We projected fine-mapped GWAS variants onto active regions of the genome, identified using three alternative approaches: (i) histone modification features, (ii) DHS, and (iii) ChromHMM enhancers.

First, we looked at three histone modification marks, namely, acetylation of histone H3 lysine 27 residues (H3K27ac), mono-methylation of histone H3 lysine 4 residues (H3K4me1), and tri-methylation of lysine 4 residues (H3K4me3) from the Roadmap Epigenomics Project to identify functional enhancers, which are key contributors of tissue-specific gene regulation. We downloaded imputed narrowPeak sets for H3K27ac, H3K4me1, and H3K4me3 from the Roadmap Epigenomics Project ftp site (available here) for 14 different tissue types (Supplementary file 1). For each tissue type, we extracted the narrow peaks that are within ±5 Mb of our putatively causative genes. Then following the approach described in Fulco et al., we extended the 150 bp narrow peaks by 175 bp on both sides to arrive at candidate features of 500 bp in length. All features mapping to blacklisted regions (https://sites.google.com/site/anshul kundaje/projects/blacklists) were removed. The remaining features were re-centered around the peak and overlapping features were merged to give the final set of features per histone modification track. The mean activity/strength of a feature (AF) was calculated by taking the geometric mean of the corresponding peak strengths from H3K27ac, H3K4me1, and H3K4me3 marks. We then combined these activity measurements with the linear distances between the features and the transcription start sites of causative genes to compute ABD scores (a simplified version of ABC scores ) for gene–feature pairs using the following formula:

The ABD score can be thought of as a measure of the contribution of a feature, F to the combined regulatory on gene, G. A high ABD score may serve as a proxy for an increased specificity between a chromatin feature and the gene of interest. We projected the fine-mapped variants onto the chromatin features in different tissue types to assess whether there is an enrichment of likely causal GWAS hits in regulatory features near our putatively causative genes. Both proximity (genomic distance) and specificity (ABD scores) were considered to determine the regulatory contribution of the fine-mapped hits.

Next, we looked at the DHS that are considered to be generic markers of the regulatory DNA and can contain genetic variations associated with traits and diseases. We downloaded the index of human DHS along with biosample metadata from https://www.meuleman.org/research/dhsindex/. The index was in hg38 coordinates, which were converted to hg19 coordinates using the online version of the hgLiftOver package (https://genome.ucsc.edu/cgi-bin/hgLiftOver). We created a DHS index for each tissue type relevant to the traits and diseases we analyzed by including all DHS that are present in at least one biosample from a certain tissue type (Supplementary file 2). We then selected DHS that lie within ±100 kb of the TSS of our putatively causative genes. Since DHS are of variable widths, we recentered the summits in a 350 bp window and merged overlapping sites in the same way as we did for other chromatin marks. We calculated the mean activity (AF) by averaging the strengths of all the merged sites. Next, we calculated the activity by distance score for each DHS and gene pair using the same formula described above. Finally, for each fine-mapped variant, we identified all DHS that fall within ±100 kb of the variant.

Finally, we used in silico chromatin state predictions (chromHMM core 15-state model ) for relevant tissue types (Supplementary file 1) to identify active enhancer regions in the genome. Tissue-specific chromHMM annotations were downloaded from the Roadmap Epigenomics Project ftp site (available here). We considered a fine-mapped variant to fall in an enhancer region if it was housed within a chromHMM segment described as either enhancer, or bivalent enhancer, or genic enhancer. Since chromHMM annotations are not accompanied by activity measurements, the ABD approach could not be applied here.

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    Not in our library10.1002/ajmg.a.61052

  8. 8.

    Aoki Y, Niihori T, Kawame H, Kurosawa K, Ohashi H, Tanaka Y, Filocamo M, Kato K, Suzuki Y, Kure S, Matsubara Y. Germline mutations in HRAS proto-oncogene cause Costello syndrome. Nature Genetics. 2005;37:1038-1040.

    Not in our library10.1038/ng1641

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    Aoki Y, Niihori T, Banjo T, Okamoto N, Mizuno S, Kurosawa K, Ogata T, Takada F, Yano M, Ando T, Hoshika T, Barnett C, Ohashi H, Kawame H, Hasegawa T, Okutani T, Nagashima T, Hasegawa S, Funayama R, Nagashima T, Nakayama K, Inoue S-I, Watanabe Y, Ogura T, Matsubara Y. Gain-Of-Function mutations in RIT1 cause Noonan syndrome, a Ras/MAPK pathway syndrome. American Journal of Human Genetics. 2013;93:173-180.

    Not in our library10.1016/j.ajhg.2013.05.021

  10. 10.

    Arvanitis M, Tayeb K, Strober BJ, Battle A. Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity. American Journal of Human Genetics. 2022;109:223-239.

    Not in our library10.1016/j.ajhg.2022.01.002

  11. 11.

    Aygün N, Liang D, Crouse WL, Keele GR, Love MI, Stein JL. Inferring Cell-Type-Specific Causal Gene Regulatory Networks during Human Neurogenesis. bioRxiv. 2022.

    Not in our library10.1101/2022.04.25.488920

  12. 12.

    Ayling RM, Ross R, Towner P, Von Laue S, Finidori J, Moutoussamy S, Buchanan CR, Clayton PE, Norman MR. A dominant-negative mutation of the growth hormone receptor causes familial short stature. Nature Genetics. 1997;16:13-14.

    Not in our library10.1038/ng0597-13

  13. 13.

    Baca SC, Singler C, Zacharia S, Seo J-H, Morova T, Hach F, Ding Y, Schwarz T, Huang C-CF, Anderson J, Fay AP, Kalita C, Groha S, Pomerantz MM, Wang V, Linder S, Sweeney CJ, Zwart W, Lack NA, Pasaniuc B, Takeda DY, Gusev A, Freedman ML. Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation. Nature Genetics. 2022;54:1364-1375.

    Not in our library10.1038/s41588-022-01168-y

  14. 14.
    Exome sequencing and analysis of 454,787 UK Biobank participants · 2021
    No notice
  15. 15.

    Bader DM, Wilkening S, Lin G, Tekkedil MM, Dietrich K, Steinmetz LM, Gagneur J. Negative feedback buffers effects of regulatory variants. Molecular Systems Biology. 2015;11:785.

    Not in our library10.15252/msb.20145844

  16. 16.

    Baird DA, Liu JZ, Zheng J, Sieberts SK, Perumal T, Elsworth B, Richardson TG, Chen C-Y, Carrasquillo MM, Allen M, Reddy JS, De Jager PL, Ertekin-Taner N, Mangravite LM, Logsdon B, Estrada K, Haycock PC, Hemani G, Runz H, Smith GD, Gaunt TR, AMP-AD eQTL working group. Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome. PLOS Genetics. 2021;17:e1009224.

    Not in our library10.1371/journal.pgen.1009224

  17. 17.

    Balliu B, Carcamo-Orive I, Gloudemans MJ, Nachun DC, Durrant MG, Gazal S, Park CY, Knowles DA, Wabitsch M, Quertermous T, Knowles JW, Montgomery SB. An integrated approach to identify environmental modulators of genetic risk factors for complex traits. American Journal of Human Genetics. 2021;108:1866-1879.

    Not in our library10.1016/j.ajhg.2021.08.014

  18. 18.
    Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics · 2018
    No notice
  19. 19.

    Barnes AM, Duncan G, Weis M, Paton W, Cabral WA, Mertz EL, Makareeva E, Gambello MJ, Lacbawan FL, Leikin S, Fertala A, Eyre DR, Bale SJ, Marini JC. Kuskokwim syndrome, a recessive congenital contracture disorder, extends the phenotype of FKBP10 mutations. Human Mutation. 2013;34:1279-1288.

    Not in our library10.1002/humu.22362

  20. 20.

    Barreda-Bonis AC, Barraza-García J, Parrón M, Pastor I, Heath KE, González-Casado I. Multiple Slc26a2 mutations occurring in a three-generational family. European Journal of Medical Genetics. 2018;61:24-28.

    Not in our library10.1016/j.ejmg.2017.10.007

  21. 21.

    Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, Gilad Y. Genomic variation: impact of regulatory variation from RNA to protein. Science. 2015;347:664-667.

    Not in our library10.1126/science.1260793

  22. 22.
    Genetic effects on gene expression across human tissues · 2017
    Corrected
  23. 23.

    Bayram Y, Pehlivan D, Karaca E, Gambin T, Jhangiani SN, Erdin S, Gonzaga-Jauregui C, Wiszniewski W, Muzny D, Baylor-Hopkins Center for Mendelian Genomics, Elcioglu NH, Yildirim MS, Bozkurt B, Zamani AG, Boerwinkle E, Gibbs RA, Lupski JR. Whole exome sequencing identifies three novel mutations in ANTXR1 in families with GAPO syndrome. American Journal of Medical Genetics Part A. 2014;164:2328-2334.

    Not in our library10.1002/ajmg.a.36678

  24. 24.
    Deep Resequencing of GWAS Loci Identifies Rare Variants in CARD9, IL23R and RNF186 That Are Associated with Ulcerative Colitis · 2013
    No notice
  25. 25.

    Beghini A, Castorina P, Roversi G, Modiano P, Larizza L. Rna processing defects of the helicase gene RecQL4 in a compound heterozygous Rothmund-Thomson patient. American Journal of Medical Genetics. Part A. 2003;120A:395-399.

    Not in our library10.1002/ajmg.a.20154

  26. 26.

    Begue B, Verdier J, Rieux-Laucat F, Goulet O, Morali A, Canioni D, Hugot J-P, Daussy C, Verkarre V, Pigneur B, Fischer A, Klein C, Cerf-Bensussan N, Ruemmele FM. Defective IL10 signaling defining a subgroup of patients with inflammatory bowel disease. The American Journal of Gastroenterology. 2011;106:1544-1555.

    Not in our library10.1038/ajg.2011.112

  27. 27.

    Bengtsson-Ellmark SH, Nilsson J, Orho-Melander M, Dahlenborg K, Groop L, Bjursell G. Association between a polymorphism in the carboxyl ester lipase gene and serum cholesterol profile. European Journal of Human Genetics. 2004;12:627-632.

    Not in our library10.1038/sj.ejhg.5201204

  28. 28.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. 1995;57:289-300.

    Not in our library10.1111/j.2517-6161.1995.tb02031.x

  29. 29.

    Berg MA, Argente J, Chernausek S, Gracia R, Guevara-Aguirre J, Hopp M, Pérez-Jurado L, Rosenbloom A, Toledo SP, Francke U. Diverse growth hormone receptor gene mutations in Laron syndrome. American Journal of Human Genetics. 1993;52:998-1005.

    Not in our library

  30. 30.

    Bertola DR, Yamamoto GL, Almeida TF, Buscarilli M, Jorge AAL, Malaquias AC, Kim CA, Takahashi VNV, Passos-Bueno MR, Pereira AC. Further evidence of the importance of RIT1 in Noonan syndrome. American Journal of Medical Genetics Part A. 2014;164:2952-2957.

    Not in our library10.1002/ajmg.a.36722

  31. 31.

    Bicknell LS, Bongers E, Leitch A, Brown S, Schoots J, Harley ME, Aftimos S, Al-Aama JY, Bober M, Brown PAJ, van Bokhoven H, Dean J, Edrees AY, Feingold M, Fryer A, Hoefsloot LH, Kau N, Knoers N, Mackenzie J, Opitz JM, Sarda P, Ross A, Temple IK, Toutain A, Wise CA, Wright M, Jackson AP. Mutations in the pre-replication complex cause meier-gorlin syndrome. Nature Genetics. 2011a;43:356-359.

    Not in our library10.1038/ng.775

  32. 32.

    Bicknell LS, Walker S, Klingseisen A, Stiff T, Leitch A, Kerzendorfer C, Martin CA, Yeyati P, Al Sanna N, Bober M, Johnson D, Wise C, Jackson AP, O’Driscoll M, Jeggo PA. Mutations in ORC1, encoding the largest subunit of the origin recognition complex, cause microcephalic primordial dwarfism resembling meier-gorlin syndrome. Nature Genetics. 2011b;43:350-355.

    Not in our library10.1038/ng.776

  33. 33.

    Blair DR, Lyttle CS, Mortensen JM, Bearden CF, Jensen AB, Khiabanian H, Melamed R, Rabadan R, Bernstam EV, Brunak S, Jensen LJ, Nicolae D, Shah NH, Grossman RL, Cox NJ, White KP, Rzhetsky A. A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk. Cell. 2013;155:70-80.

    Not in our library10.1016/j.cell.2013.08.030

  34. 34.

    Bodzioch M, Orsó E, Klucken J, Langmann T, Böttcher A, Diederich W, Drobnik W, Barlage S, Büchler C, Porsch-Ozcürümez M, Kaminski WE, Hahmann HW, Oette K, Rothe G, Aslanidis C, Lackner KJ, Schmitz G. The gene encoding ATP-binding cassette transporter 1 is mutated in Tangier disease. Nature Genetics. 1999;22:347-351.

    Not in our library10.1038/11914

  35. 35.

    Bonder MJ, Smail C, Gloudemans MJ, Frésard L, Jakubosky D, D’Antonio M, Li X, Ferraro NM, Carcamo-Orive I, Mirauta B, Seaton DD, Cai N, Vakili D, Horta D, Zhao C, Zastrow DB, Bonner DE, HipSci Consortium, iPSCORE consortium, Undiagnosed Diseases Network, PhLiPS consortium, Wheeler MT, Kilpinen H, Knowles JW, Smith EN, Frazer KA, Montgomery SB, Stegle O. Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics. Nature Genetics. 2021;53:313-321.

    Not in our library10.1038/s41588-021-00800-7

  36. 36.

    Boonen SE, Mackay DJG, Hahnemann JMD, Docherty L, Grønskov K, Lehmann A, Larsen LG, Haemers AP, Kockaerts Y, Dooms L, Vu DC, Ngoc CTB, Nguyen PB, Kordonouri O, Sundberg F, Dayanikli P, Puthi V, Acerini C, Massoud AF, Tümer Z, Temple IK. Transient neonatal diabetes, Zfp57, and hypomethylation of multiple imprinted loci: a detailed follow-up. Diabetes Care. 2013;36:505-512.

    Not in our library10.2337/dc12-0700

  37. 37.

    Borowiec M, Liew CW, Thompson R, Boonyasrisawat W, Hu J, Mlynarski WM, El Khattabi I, Kim SH, Marselli L, Rich SS, Krolewski AS, Bonner-Weir S, Sharma A, Sale M, Mychaleckyj JC, Kulkarni RN, Doria A. Mutations at the blk locus linked to maturity onset diabetes of the young and β-cell dysfunction. PNAS. 2009;106:14460-14465.

    Not in our library10.1073/pnas.0906474106

  38. 38.

    Brickwood S, Bonthron DT, Al-Gazali LI, Piper K, Hearn T, Wilson DI, Hanley NA. Wolcott-rallison syndrome: pathogenic insights into neonatal diabetes from new mutation and expression studies of EIF2AK3. Journal of Medical Genetics. 2003;40:685-689.

    Not in our library10.1136/jmg.40.9.685

  39. 39.

    Briggs MD, Hoffman SM, King LM, Olsen AS, Mohrenweiser H, Leroy JG, Mortier GR, Rimoin DL, Lachman RS, Gaines ES. Pseudoachondroplasia and multiple epiphyseal dysplasia due to mutations in the cartilage oligomeric matrix protein gene. Nature Genetics. 1995;10:330-336.

    Not in our library10.1038/ng0795-330

  40. 40.

    Brooks-Wilson A, Marcil M, Clee SM, Zhang LH, Roomp K, van Dam M, Yu L, Brewer C, Collins JA, Molhuizen HO, Loubser O, Ouelette BF, Fichter K, Ashbourne-Excoffon KJ, Sensen CW, Scherer S, Mott S, Denis M, Martindale D, Frohlich J, Morgan K, Koop B, Pimstone S, Kastelein JJ, Genest J, Hayden MR. Mutations in ABC1 in Tangier disease and familial high-density lipoprotein deficiency. Nature Genetics. 1999;22:336-345.

    Not in our library10.1038/11905

  41. 41.

    Brown MS, Goldstein JL. Analysis of a mutant strain of human fibroblasts with a defect in the internalization of receptor-bound low density lipoprotein. Cell. 1976;9:663-674.

    Not in our library10.1016/0092-8674(76)90130-6

  42. 42.

    Bryois J, Calini D, Macnair W, Foo L, Urich E, Ortmann W, Iglesias VA, Selvaraj S, Nutma E, Marzin M, Amor S, Williams A, Castelo-Branco G, Menon V, De Jager P, Malhotra D. Cell-Type Specific Cis-EQTLs in Eight Brain Cell-Types Identifies Novel Risk Genes for Human Brain Disorders. bioRxiv. 2021.

    Not in our library10.1101/2021.10.09.21264604

  43. 43.

    Buccitelli C, Selbach M. Mrnas, proteins and the emerging principles of gene expression control. Nature Reviews. Genetics. 2020;21:630-644.

    Not in our library10.1038/s41576-020-0258-4

  44. 44.

    Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O’Connell J, Cortes A, Welsh S, McVean G, Leslie S, Donnelly P, Marchini J. Genome-Wide Genetic Data on ~500,000 UK Biobank Participants. bioRxiv. 2017.

    Not in our library10.1101/166298

  45. 45.

    Cao H, Hegele RA. Nuclear lamin A/C R482Q mutation in Canadian kindreds with Dunnigan-type familial partial lipodystrophy. Human Molecular Genetics. 2000;9:109-112.

    Not in our library10.1093/hmg/9.1.109

  46. 46.

    Caputo V, Cianetti L, Niceta M, Carta C, Ciolfi A, Bocchinfuso G, Carrani E, Dentici ML, Biamino E, Belligni E, Garavelli L, Boccone L, Melis D, Andria G, Gelb BD, Stella L, Silengo M, Dallapiccola B, Tartaglia M. A restricted spectrum of mutations in the Smad4 tumor-suppressor gene underlies myhre syndrome. American Journal of Human Genetics. 2012;90:161-169.

    Not in our library10.1016/j.ajhg.2011.12.011

  47. 47.

    Carta C, Pantaleoni F, Bocchinfuso G, Stella L, Vasta I, Sarkozy A, Digilio C, Palleschi A, Pizzuti A, Grammatico P, Zampino G, Dallapiccola B, Gelb BD, Tartaglia M. Germline missense mutations affecting KRAS isoform B are associated with a severe Noonan syndrome phenotype. American Journal of Human Genetics. 2006;79:129-135.

    Not in our library10.1086/504394

  48. 48.

    Cenarro A, Etxebarria A, de Castro-Orós I, Stef M, Bea AM, Palacios L, Mateo-Gallego R, Benito-Vicente A, Ostolaza H, Tejedor T, Martín C, Civeira F. The p.leu167del mutation in ApoE gene causes autosomal dominant hypercholesterolemia by down-regulation of LDL receptor expression in hepatocytes. The Journal of Clinical Endocrinology and Metabolism. 2016;101:2113-2121.

    Not in our library10.1210/jc.2015-3874

  49. 49.
    Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans · 2015
    No notice
  50. 50.

    Chan Y, Salem RM, Hsu Y-HH, McMahon G, Pers TH, Vedantam S, Esko T, Guo MH, Lim ET, GIANT Consortium, Franke L, Smith GD, Strachan DP, Hirschhorn JN. Genome-Wide analysis of body proportion classifies height-associated variants by mechanism of action and implicates genes important for skeletal development. American Journal of Human Genetics. 2015;96:695-708.

    Not in our library10.1016/j.ajhg.2015.02.018

  51. 51.
    Second-generation PLINK: rising to the challenge of larger and richer datasets · 2015
    No notice
  52. 52.

    Chick JM, Munger SC, Simecek P, Huttlin EL, Choi K, Gatti DM, Raghupathy N, Svenson KL, Churchill GA, Gygi SP. Defining the consequences of genetic variation on a proteome-wide scale. Nature. 2016;534:500-505.

    Not in our library10.1038/nature18270

  53. 53.

    Chun S, Casparino A, Patsopoulos NA, Croteau-Chonka DC, Raby BA, De Jager PL, Sunyaev SR, Cotsapas C. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nature Genetics. 2017;49:600-605.

    Not in our library10.1038/ng.3795

  54. 54.

    Clee SM, Loubser O, Collins J, Kastelein JJ, Hayden MR. The LPL S447X csnp is associated with decreased blood pressure and plasma triglycerides, and reduced risk of coronary artery disease. Clinical Genetics. 2001;60:293-300.

    Not in our library10.1034/j.1399-0004.2001.600407.x

  55. 55.

    Craddock N, Hurles ME, Cardin N, Pearson RD, Plagnol V, Robson S, Vukcevic D, Barnes C, Conrad DF, Giannoulatou E, Holmes C, Marchini JL, Stirrups K, Tobin MD, Wain LV, Yau C, Aerts J, Ahmad T, Andrews TD, Arbury H, Attwood A, Auton A, Ball SG, Balmforth AJ, Barrett JC, Barroso I, Barton A, Bennett AJ, Bhaskar S, Blaszczyk K, Bowes J, Brand OJ, Braund PS, Bredin F, Breen G, Brown MJ, Bruce IN, Bull J, Burren OS, Burton J, Byrnes J, Caesar S, Clee CM, Coffey AJ, Connell JMC, Cooper JD, Dominiczak AF, Downes K, Drummond HE, Dudakia D, Dunham A, Ebbs B, Eccles D, Edkins S, Edwards C, Elliot A, Emery P, Evans DM, Evans G, Eyre S, Farmer A, Ferrier IN, Feuk L, Fitzgerald T, Flynn E, Forbes A, Forty L, Franklyn JA, Freathy RM, Gibbs P, Gilbert P, Gokumen O, Gordon-Smith K, Gray E, Green E, Groves CJ, Grozeva D, Gwilliam R, Hall A, Hammond N, Hardy M, Harrison P, Hassanali N, Hebaishi H, Hines S, Hinks A, Hitman GA, Hocking L, Howard E, Howard P, Howson JMM, Hughes D, Hunt S, Isaacs JD, Jain M, Jewell DP, Johnson T, Jolley JD, Jones IR, Jones LA, Kirov G, Langford CF, Lango-Allen H, Lathrop GM, Lee J, Lee KL, Lees C, Lewis K, Lindgren CM, Maisuria-Armer M, Maller J, Mansfield J, Martin P, Massey DCO, McArdle WL, McGuffin P, McLay KE, Mentzer A, Mimmack ML, Morgan AE, Morris AP, Mowat C, Myers S, Newman W, Nimmo ER, O’Donovan MC, Onipinla A, Onyiah I, Ovington NR, Owen MJ, Palin K, Parnell K, Pernet D, Perry JRB, Phillips A, Pinto D, Prescott NJ, Prokopenko I, Quail MA, Rafelt S, Rayner NW, Redon R, Reid DM, Ring SM, Robertson N, Russell E, St Clair D, Sambrook JG, Sanderson JD, Schuilenburg H, Scott CE, Scott R, Seal S, Shaw-Hawkins S, Shields BM, Simmonds MJ, Smyth DJ, Somaskantharajah E, Spanova K, Steer S, Stephens J, Stevens HE, Stone MA, Su Z, Symmons DPM, Thompson JR, Thomson W, Travers ME, Turnbull C, Valsesia A, Walker M, Walker NM, Wallace C, Warren-Perry M, Watkins NA, Webster J, Weedon MN, Wilson AG, Woodburn M, Wordsworth BP, Young AH, Zeggini E, Carter NP, Frayling TM, Lee C, McVean G, Munroe PB, Palotie A, Sawcer SJ, Scherer SW, Strachan DP, Tyler-Smith C, Brown MA, Burton PR, Caulfield MJ, Compston A, Farrall M, Gough SCL, Hall AS, Hattersley AT, Hill AVS, Mathew CG, Pembrey M, Satsangi J, Stratton MR, Worthington J, Deloukas P, Duncanson A, Kwiatkowski DP, McCarthy MI, Ouwehand W, Parkes M, Rahman N, Todd JA, Samani NJ, Donnelly P, Wellcome Trust Case Control Consortium. Genome-wide association study of cnvs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature. 2010;464:713-720.

    Not in our library10.1038/nature08979

  56. 56.
    Transcript expression-aware annotation improves rare variant interpretation · 2020
    Corrected
  57. 57.
    Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression · 2020
    Corrected
  58. 58.
    Twelve years of SAMtools and BCFtools · 2021
    No notice
  59. 59.

    Dar RD, Razooky BS, Singh A, Trimeloni TV, McCollum JM, Cox CD, Simpson ML, Weinberger LS. Transcriptional burst frequency and burst size are equally modulated across the human genome. PNAS. 2012;109:17454-17459.

    Not in our library10.1073/pnas.1213530109

  60. 60.

    De Franco E, Shaw-Smith C, Flanagan SE, Shepherd MH, International NDM Consortium, Hattersley AT, Ellard S. Gata6 mutations cause a broad phenotypic spectrum of diabetes from pancreatic agenesis to adult-onset diabetes without exocrine insufficiency. Diabetes. 2013;62:993-997.

    Not in our library10.2337/db12-0885

  61. 61.

    de Knijff P, van den Maagdenberg AM, Frants RR, Havekes LM. Genetic heterogeneity of apolipoprotein E and its influence on plasma lipid and lipoprotein levels. Human Mutation. 1994;4:178-194.

    Not in our library10.1002/humu.1380040303

  62. 62.

    de Munnik SA, Bicknell LS, Aftimos S, Al-Aama JY, van Bever Y, Bober MB, Clayton-Smith J, Edrees AY, Feingold M, Fryer A, van Hagen JM, Hennekam RC, Jansweijer MCE, Johnson D, Kant SG, Opitz JM, Ramadevi AR, Reardon W, Ross A, Sarda P, Schrander-Stumpel CTRM, Schoots J, Temple IK, Terhal PA, Toutain A, Wise CA, Wright M, Skidmore DL, Samuels ME, Hoefsloot LH, Knoers NVAM, Brunner HG, Jackson AP, Bongers EMHF. Meier-Gorlin syndrome genotype-phenotype studies: 35 individuals with pre-replication complex gene mutations and 10 without molecular diagnosis. European Journal of Human Genetics. 2012;20:598-606.

    Not in our library10.1038/ejhg.2011.269

  63. 63.

    De Paepe A, Nuytinck L, Raes M, Fryns JP. Homozygosity by descent for a COL1A2 mutation in two sibs with severe osteogenesis imperfecta and mild clinical expression in the heterozygotes. Human Genetics. 1997;99:478-483.

    Not in our library10.1007/s004390050392

  64. 64.

    Deardorff MA, Kaur M, Yaeger D, Rampuria A, Korolev S, Pie J, Gil-Rodríguez C, Arnedo M, Loeys B, Kline AD, Wilson M, Lillquist K, Siu V, Ramos FJ, Musio A, Jackson LS, Dorsett D, Krantz ID. Mutations in cohesin complex members SMC3 and SMC1A cause a mild variant of Cornelia de Lange syndrome with predominant mental retardation. American Journal of Human Genetics. 2007;80:485-494.

    Not in our library10.1086/511888

  65. 65.

    Deardorff MA, Wilde JJ, Albrecht M, Dickinson E, Tennstedt S, Braunholz D, Mönnich M, Yan Y, Xu W, Gil-Rodríguez MC, Clark D, Hakonarson H, Halbach S, Michelis LD, Rampuria A, Rossier E, Spranger S, Van Maldergem L, Lynch SA, Gillessen-Kaesbach G, Lüdecke H-J, Ramsay RG, McKay MJ, Krantz ID, Xu H, Horsfield JA, Kaiser FJ. Rad21 mutations cause a human cohesinopathy. American Journal of Human Genetics. 2012;90:1014-1027.

    Not in our library10.1016/j.ajhg.2012.04.019

  66. 66.

    Deeb SS, Fajas L, Nemoto M, Pihlajamäki J, Mykkänen L, Kuusisto J, Laakso M, Fujimoto W, Auwerx J. A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nature Genetics. 1998;20:284-287.

    Not in our library10.1038/3099

  67. 67.

    Diaz-Gallo L-M, Espino-Paisán L, Fransen K, Gómez-García M, van Sommeren S, Cardeña C, Rodrigo L, Mendoza JL, Taxonera C, Nieto A, Alcain G, Cueto I, López-Nevot MA, Bottini N, Barclay ML, Crusius JB, van Bodegraven AA, Wijmenga C, Ponsioen CY, Gearry RB, Roberts RL, Weersma RK, Urcelay E, Merriman TR, Alizadeh BZ, Martin J. Differential association of two PTPN22 coding variants with Crohn’s disease and ulcerative colitis. Inflammatory Bowel Diseases. 2011;17:2287-2294.

    Not in our library10.1002/ibd.21630

  68. 68.

    Dietlein F, Weghorn D, Taylor-Weiner A, Richters A, Reardon B, Liu D, Lander ES, Van Allen EM, Sunyaev SR. Identification of cancer driver genes based on nucleotide context. Nature Genetics. 2020;52:208-218.

    Not in our library10.1038/s41588-019-0572-y

  69. 69.
    Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS · 2018
    No notice
  70. 70.

    Dori-Bachash M, Shema E, Tirosh I. Coupled evolution of transcription and mRNA degradation. PLOS Biology. 2011;9:e1001106.

    Not in our library10.1371/journal.pbio.1001106

  71. 71.

    Duerr RH, Taylor KD, Brant SR, Rioux JD, Silverberg MS, Daly MJ, Steinhart AH, Abraham C, Regueiro M, Griffiths A, Dassopoulos T, Bitton A, Yang H, Targan S, Datta LW, Kistner EO, Schumm LP, Lee AT, Gregersen PK, Barmada MM, Rotter JI, Nicolae DL, Cho JH. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science. 2006;314:1461-1463.

    Not in our library10.1126/science.1135245

  72. 72.

    Durocher F, Faure R, Labrie Y, Pelletier L, Bouchard I, Laframboise R. A novel mutation in the EIF2AK3 gene with variable expressivity in two patients with wolcott-rallison syndrome. Clinical Genetics. 2006;70:34-38.

    Not in our library10.1111/j.1399-0004.2006.00632.x

  73. 73.

    Ellinghaus D, Zhang H, Zeissig S, Lipinski S, Till A, Jiang T, Stade B, Bromberg Y, Ellinghaus E, Keller A, Rivas MA, Skieceviciene J, Doncheva NT, Liu X, Liu Q, Jiang F, Forster M, Mayr G, Albrecht M, Häsler R, Boehm BO, Goodall J, Berzuini CR, Lee J, Andersen V, Vogel U, Kupcinskas L, Kayser M, Krawczak M, Nikolaus S, Weersma RK, Ponsioen CY, Sans M, Wijmenga C, Strachan DP, McArdle WL, Vermeire S, Rutgeerts P, Sanderson JD, Mathew CG, Vatn MH, Wang J, Nöthen MM, Duerr RH, Büning C, Brand S, Glas J, Winkelmann J, Illig T, Latiano A, Annese V, Halfvarson J, D’Amato M, Daly MJ, Nothnagel M, Karlsen TH, Subramani S, Rosenstiel P, Schreiber S, Parkes M, Franke A. Association between variants of PRDM1 and NDP52 and Crohn’s disease, based on exome sequencing and functional studies. Gastroenterology. 2013;145:339-347.

    Not in our library10.1053/j.gastro.2013.04.040

  74. 74.

    Ellis NA, Groden J, Ye TZ, Straughen J, Lennon DJ, Ciocci S, Proytcheva M, German J. The Bloom’s syndrome gene product is homologous to RecQ helicases. Cell. 1995;83:655-666.

    Not in our library10.1016/0092-8674(95)90105-1

  75. 75.

    Elorbany R, Popp JM, Rhodes K, Strober BJ, Barr K, Qi G, Gilad Y, Battle A. Single-Cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation. PLOS Genetics. 2022;18:e1009666.

    Not in our library10.1371/journal.pgen.1009666

  76. 76.

    Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nature Methods. 2012;9:215-216.

    Not in our library10.1038/nmeth.1906

  77. 77.

    Ernst J, Kellis M. Chromatin-state discovery and genome annotation with chromhmm. Nature Protocols. 2017;12:2478-2492.

    Not in our library10.1038/nprot.2017.124

  78. 78.

    Faivre L, Gorlin RJ, Wirtz MK, Godfrey M, Dagoneau N, Samples JR, Le Merrer M, Collod-Beroud G, Boileau C, Munnich A, Cormier-Daire V. In frame fibrillin-1 gene deletion in autosomal dominant weill-marchesani syndrome. Journal of Medical Genetics. 2003;40:34-36.

    Not in our library10.1136/jmg.40.1.34

  79. 79.

    Farh KK-H, Marson A, Zhu J, Kleinewietfeld M, Housley WJ, Beik S, Shoresh N, Whitton H, Ryan RJH, Shishkin AA, Hatan M, Carrasco-Alfonso MJ, Mayer D, Luckey CJ, Patsopoulos NA, De Jager PL, Kuchroo VK, Epstein CB, Daly MJ, Hafler DA, Bernstein BE. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature. 2015;518:337-343.

    Not in our library10.1038/nature13835

  80. 80.

    Findley AS, Monziani A, Richards AL, Rhodes K, Ward MC, Kalita CA, Alazizi A, Pazokitoroudi A, Sankararaman S, Wen X, Lanfear DE, Pique-Regi R, Gilad Y, Luca F. Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions. eLife. 2021;10:e67077.

    Not in our library10.7554/elife.67077

  81. 81.

    Fisher SA, Tremelling M, Anderson CA, Gwilliam R, Bumpstead S, Prescott NJ, Nimmo ER, Massey D, Berzuini C, Johnson C, Barrett JC, Cummings FR, Drummond H, Lees CW, Onnie CM, Hanson CE, Blaszczyk K, Inouye M, Ewels P, Ravindrarajah R, Keniry A, Hunt S, Carter M, Watkins N, Ouwehand W, Lewis CM, Cardon L, Wellcome Trust Case Control Consortium, Lobo A, Forbes A, Sanderson J, Jewell DP, Mansfield JC, Deloukas P, Mathew CG, Parkes M, Satsangi J. Genetic determinants of ulcerative colitis include the ECM1 locus and five loci implicated in Crohn’s disease. Nature Genetics. 2008;40:710-712.

    Not in our library10.1038/ng.145

  82. 82.

    Foucault F, Vaury C, Barakat A, Thibout D, Planchon P, Jaulin C, Praz F, Amor-Guéret M. Characterization of a new BLM mutation associated with a topoisomerase II alpha defect in a patient with Bloom’s syndrome. Human Molecular Genetics. 1997;6:1427-1434.

    Not in our library10.1093/hmg/6.9.1427

  83. 83.

    Fowler EV. Tnf and IL10 SNPs act together to predict disease behaviour in Crohn'’ disease. Journal of Medical Genetics. 2005;42:523-528.

    Not in our library10.1136/jmg.2004.027425

  84. 84.

    Fowler EV, Doecke J, Simms LA, Zhao ZZ, Webb PM, Hayward NK, Whiteman DC, Florin TH, Montgomery GW, Cavanaugh JA, Radford-Smith GL. Atg16L1 T300A shows strong associations with disease subgroups in a large Australian IBD population: further support for significant disease heterogeneity. The American Journal of Gastroenterology. 2008;103:2519-2526.

    Not in our library10.1111/j.1572-0241.2008.02023.x

  85. 85.

    Freund MK, Burch KS, Shi H, Mancuso N, Kichaev G, Garske KM, Pan DZ, Miao Z, Mohlke KL, Laakso M, Pajukanta P, Pasaniuc B, Arboleda VA. Phenotype-Specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits. American Journal of Human Genetics. 2018;103:535-552.

    Not in our library10.1016/j.ajhg.2018.08.017

  86. 86.

    Froguel P, Zouali H, Vionnet N, Velho G, Vaxillaire M, Sun F, Lesage S, Stoffel M, Takeda J, Passa P. Familial hyperglycemia due to mutations in glucokinase: definition of a subtype of diabetes mellitus. The New England Journal of Medicine. 1993;328:697-702.

    Not in our library10.1056/nejm199303113281005

  87. 87.

    Fu J, Keurentjes JJB, Bouwmeester H, America T, Verstappen FWA, Ward JL, Beale MH, de Vos RCH, Dijkstra M, Scheltema RA, Johannes F, Koornneef M, Vreugdenhil D, Breitling R, Jansen RC. System-Wide molecular evidence for phenotypic buffering in Arabidopsis. Nature Genetics. 2009;41:166-167.

    Not in our library10.1038/ng.308

  88. 88.

    Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT, Subramanian V, Grossman SR, Anyoha R, Doughty BR, Patwardhan TA, Nguyen TH, Kane M, Perez EM, Durand NC, Lareau CA, Stamenova EK, Aiden EL, Lander ES, Engreitz JM. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nature Genetics. 2019;51:1664-1669.

    Not in our library10.1038/s41588-019-0538-0

  89. 89.

    Galdzicka M, Patnala S, Hirshman MG, Cai J-F, Nitowsky H, Egeland JA, Ginns EI. A new gene, EVC2, is mutated in Ellis-van Creveld syndrome. Molecular Genetics and Metabolism. 2002;77:291-295.

    Not in our library10.1016/s1096-7192(02)00178-6

  90. 90.

    Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL. Fast principal-component analysis reveals convergent evolution of ADH1B in europe and east asia. American Journal of Human Genetics. 2016a;98:456-472.

    Not in our library10.1016/j.ajhg.2015.12.022

  91. 91.

    Galinsky KJ, Loh PR, Mallick S, Patterson NJ, Price AL. Population structure of UK biobank and ancient eurasians reveals adaptation at genes influencing blood pressure. American Journal of Human Genetics. 2016b;99:1130-1139.

    Not in our library10.1016/j.ajhg.2016.09.014

  92. 92.

    Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, Eyler AE, Denny JC, GTEx Consortium, Nicolae DL, Cox NJ, Im HK. A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics. 2015;47:1091-1098.

    Not in our library10.1038/ng.3367

  93. 93.

    Gamazon ER, Zwinderman AH, Cox NJ, Denys D, Derks EM. Multi-Tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits. Nature Genetics. 2019;51:933-940.

    Not in our library10.1038/s41588-019-0409-8

  94. 94.

    Gasche C, Grundtner P, Zwirn P, Reinisch W, Shaw SH, Zdanov A, Sarma U, Williams LM, Foxwell BM, Gangl A. Novel variants of the IL-10 receptor 1 affect inhibition of monocyte TNF-alpha production. Journal of Immunology. 2003;170:5578-5582.

    Not in our library10.4049/jimmunol.170.11.5578

  95. 95.
    Comparative Analysis of Proteome and Transcriptome Variation in Mouse · 2011
    No notice
  96. 96.
    Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics · 2014
    No notice
  97. 97.

    Giambartolomei C, Zhenli Liu J, Zhang W, Hauberg M, Shi H, Boocock J, Pickrell J, Jaffe AE, Pasaniuc B, Roussos P, CommonMind Consortium. A bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics. 2018;34:2538-2545.

    Not in our library10.1093/bioinformatics/bty147

  98. 98.
    rs1004819 Is the Main Disease-Associated IL23R Variant in German Crohn's Disease Patients: Combined Analysis of IL23R, CARD15, and OCTN1/2 Variants · 2007
    No notice
  99. 99.

    Glastonbury CA, Couto Alves A, El-Sayed Moustafa JS, Small KS. Cell-Type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs. American Journal of Human Genetics. 2019;104:1013-1024.

    Not in our library10.1016/j.ajhg.2019.03.025

  100. 100.

    Glocker EO, Kotlarz D, Boztug K, Gertz EM, Schäffer AA, Noyan F, Perro M, Diestelhorst J, Allroth A, Murugan D, Hätscher N, Pfeifer D, Sykora KW, Sauer M, Kreipe H, Lacher M, Nustede R, Woellner C, Baumann U, Salzer U, Koletzko S, Shah N, Segal AW, Sauerbrey A, Buderus S, Snapper SB, Grimbacher B, Klein C. Inflammatory bowel disease and mutations affecting the interleukin-10 receptor. The New England Journal of Medicine. 2009;361:2033-2045.

    Not in our library10.1056/nejmoa0907206

  101. 101.

    Gloyn AL, Pearson ER, Antcliff JF, Proks P, Bruining GJ, Slingerland AS, Howard N, Srinivasan S, Silva J, Molnes J, Edghill EL, Frayling TM, Temple IK, Mackay D, Shield JPH, Sumnik Z, van Rhijn A, Wales JKH, Clark P, Gorman S, Aisenberg J, Ellard S, Njølstad PR, Ashcroft FM, Hattersley AT. Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit kir6.2 and permanent neonatal diabetes. The New England Journal of Medicine. 2004;350:1838-1849.

    Not in our library10.1056/nejmoa032922

  102. 102.

    Glueck CJ, Fallat RW, Millett F, Steiner PM. Familial hyperalphalipoproteinemia. Archives of Internal Medicine. 1975;135:1025-1028.

    Not in our library

  103. 103.

    Goddard AD, Covello R, Luoh SM, Clackson T, Attie KM, Gesundheit N, Rundle AC, Wells JA, Carlsson LMS. Mutations of the growth hormone receptor in children with idiopathic short stature. New England Journal of Medicine. 1995;333:1093-1098.

    Not in our library10.1056/nejm199510263331701

  104. 104.

    Goel H, Parasivam G. Another case of holoprosencephaly associated with Rad21 loss-of-function variant. Brain. 2020;143:e64.

    Not in our library10.1093/brain/awaa173

  105. 105.

    Goldstein JL, Brown MS. Familial hypercholesterolemia: identification of A defect in the regulation of 3-hydroxy-3-methylglutaryl coenzyme A reductase activity associated with overproduction of cholesterol. PNAS. 1973;70:2804-2808.

    Not in our library10.1073/pnas.70.10.2804

  106. 106.

    Gos M, Fahiminiya S, Poznański J, Klapecki J, Obersztyn E, Piotrowicz M, Wierzba J, Posmyk R, Bal J, Majewski J. Contribution of RIT1 mutations to the pathogenesis of Noonan syndrome: four new cases and further evidence of heterogeneity. American Journal of Medical Genetics Part A. 2014;164:2310-2316.

    Not in our library10.1002/ajmg.a.36646

  107. 107.

    Goto M, Imamura O, Kuromitsu J, Matsumoto T, Yamabe Y, Tokutake Y, Suzuki N, Mason B, Drayna D, Sugawara M, Sugimoto M, Furuichi Y. Analysis of helicase gene mutations in Japanese Werner’s syndrome patients. Human Genetics. 1997;99:191-193.

    Not in our library10.1007/s004390050336

  108. 108.

    Goyette P, Boucher G, Mallon D, Ellinghaus E, Jostins L, Huang H, Ripke S, Gusareva ES, Annese V, Hauser SL, Oksenberg JR, Thomsen I, Leslie S, International Inflammatory Bowel Disease Genetics Consortium, Australia and New Zealand IBDGC, Belgium IBD Genetics Consortium, Italian Group for IBD Genetic Consortium, NIDDK Inflammatory Bowel Disease Genetics Consortium, United Kingdom IBDGC, Wellcome Trust Case Control Consortium, Quebec IBD Genetics Consortium, Daly MJ, Van Steen K, Duerr RH, Barrett JC, McGovern DPB, Schumm LP, Traherne JA, Carrington MN, Kosmoliaptsis V, Karlsen TH, Franke A, Rioux JD. High-Density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis. Nature Genetics. 2015;47:172-179.

    Not in our library10.1038/ng.3176

  109. 109.

    Gradwohl G, Dierich A, LeMeur M, Guillemot F. Neurogenin3 is required for the development of the four endocrine cell lineages of the pancreas. PNAS. 2000;97:1607-1611.

    Not in our library10.1073/pnas.97.4.1607

  110. 110.

    Grarup N, Andreasen CH, Andersen MK, Albrechtsen A, Sandbaek A, Lauritzen T, Borch-Johnsen K, Jørgensen T, Schmitz O, Hansen T, Pedersen O. The -250G > a promoter variant in hepatic lipase associates with elevated fasting serum high-density lipoprotein cholesterol modulated by interaction with physical activity in a study of 16,156 Danish subjects. The Journal of Clinical Endocrinology and Metabolism. 2008;93:2294-2299.

    Not in our library10.1210/jc.2007-2815

  111. 111.

    Griffith E, Walker S, Martin C-A, Vagnarelli P, Stiff T, Vernay B, Al Sanna N, Saggar A, Hamel B, Earnshaw WC, Jeggo PA, Jackson AP, O’Driscoll M. Mutations in pericentrin cause seckel syndrome with defective ATR-dependent DNA damage signaling. Nature Genetics. 2008;40:232-236.

    Not in our library10.1038/ng.2007.80

  112. 112.

    GTEx Consortium. The gtex Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318-1330.

    Not in our library10.1126/science.aaz1776

  113. 113.

    Guernsey DL, Matsuoka M, Jiang H, Evans S, Macgillivray C, Nightingale M, Perry S, Ferguson M, LeBlanc M, Paquette J, Patry L, Rideout AL, Thomas A, Orr A, McMaster CR, Michaud JL, Deal C, Langlois S, Superneau DW, Parkash S, Ludman M, Skidmore DL, Samuels ME. Mutations in origin recognition complex gene Orc4 cause Meier-Gorlin syndrome. Nature Genetics. 2011;43:360-364.

    Not in our library10.1038/ng.777

  114. 114.

    Gusev A, Lee SH, Trynka G, Finucane H, Vilhjálmsson BJ, Xu H, Zang C, Ripke S, Bulik-Sullivan B, Stahl E, Schizophrenia Working Group of the Psychiatric Genomics Consortium, SWE-SCZ Consortium, Kähler AK, Hultman CM, Purcell SM, McCarroll SA, Daly M, Pasaniuc B, Sullivan PF, Neale BM, Wray NR, Raychaudhuri S, Price AL, Schizophrenia Working Group of the Psychiatric Genomics Consortium, SWE-SCZ Consortium. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. American Journal of Human Genetics. 2014;95:535-552.

    Not in our library10.1016/j.ajhg.2014.10.004

  115. 115.

    Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, Jansen R, de Geus EJC, Boomsma DI, Wright FA, Sullivan PF, Nikkola E, Alvarez M, Civelek M, Lusis AJ, Lehtimäki T, Raitoharju E, Kähönen M, Seppälä I, Raitakari OT, Kuusisto J, Laakso M, Price AL, Pajukanta P, Pasaniuc B. Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics. 2016;48:245-252.

    Not in our library10.1038/ng.3506

  116. 116.

    Hampe J, Franke A, Rosenstiel P, Till A, Teuber M, Huse K, Albrecht M, Mayr G, De La Vega FM, Briggs J, Günther S, Prescott NJ, Onnie CM, Häsler R, Sipos B, Fölsch UR, Lengauer T, Platzer M, Mathew CG, Krawczak M, Schreiber S. A genome-wide association scan of nonsynonymous snps identifies A susceptibility variant for crohn disease in ATG16L1. Nature Genetics. 2007;39:207-211.

    Not in our library10.1038/ng1954

  117. 117.

    Hani EH, Boutin P, Durand E, Inoue H, Permutt MA, Velho G, Froguel P. Missense mutations in the pancreatic islet beta cell inwardly rectifying K+ channel gene (KIR6.2/BIR): a meta-analysis suggests a role in the polygenic basis of type II diabetes mellitus in Caucasians. Diabetologia. 1998;41:1511-1515.

    Not in our library10.1007/s001250051098

  118. 118.

    Hani EH, Stoffers DA, Chèvre JC, Durand E, Stanojevic V, Dina C, Habener JF, Froguel P. Defective mutations in the insulin promoter factor-1 (IPF-1) gene in late-onset type 2 diabetes mellitus. The Journal of Clinical Investigation. 1999;104:R41-R48.

    Not in our library10.1172/jci7469

  119. 119.

    Harding HP, Zeng H, Zhang Y, Jungries R, Chung P, Plesken H, Sabatini DD, Ron D. Diabetes mellitus and exocrine pancreatic dysfunction in perk-/- mice reveals a role for translational control in secretory cell survival. Molecular Cell. 2001;7:1153-1163.

    Not in our library10.1016/s1097-2765(01)00264-7

  120. 120.

    Hardy C, Khanim F, Torres R, Scott-Brown M, Seller A, Poulton J, Collier D, Kirk J, Polymeropoulos M, Latif F, Barrett T. Clinical and molecular genetic analysis of 19 Wolfram syndrome kindreds demonstrating a wide spectrum of mutations in WFS1. American Journal of Human Genetics. 1999;65:1279-1290.

    Not in our library10.1086/302609

  121. 121.

    Hästbacka J, Salonen R, Laurila P, de la Chapelle A, Kaitila I. Prenatal diagnosis of diastrophic dysplasia with polymorphic DNA markers. Journal of Medical Genetics. 1993;30:265-268.

    Not in our library10.1136/jmg.30.4.265

  122. 122.

    HaVinh R, Alanay Y, Bank RA, Campos-Xavier AB, Zankl A, Superti-Furga A, Bonafé L. Phenotypic and molecular characterization of bruck syndrome (osteogenesis imperfecta with contractures of the large joints) caused by a recessive mutation in PLOD2. American Journal of Medical Genetics Part A. 2004;131A:115-120.

    Not in our library10.1002/ajmg.a.30231

  123. 123.

    Hegele RA, Connelly PW, Maguire GF, Huff MW, Leiter L, Wolfe BM, Evans AJ, Little JA. An apolipoprotein CII mutation, ciilys19 -- -- ’hr' identified in patients with hyperlipidemia. Disease Markers. 1991;9:73-80.

    Not in our library

  124. 124.

    Heizmann C, Kirchgessner T, Kwiterovich PO, Ladias JA, Derby C, Antonarakis SE, Lusis AJ. Dna polymorphism haplotypes of the human lipoprotein lipase gene: possible association with high density lipoprotein levels. Human Genetics. 1991;86:578-584.

    Not in our library10.1007/bf00201544

  125. 125.

    Hood RL, Lines MA, Nikkel SM, Schwartzentruber J, Beaulieu C, Nowaczyk MJM, Allanson J, Kim CA, Wieczorek D, Moilanen JS, Lacombe D, Gillessen-Kaesbach G, Whiteford ML, Quaio CRDC, Gomy I, Bertola DR, Albrecht B, Platzer K, McGillivray G, Zou R, McLeod DR, Chudley AE, Chodirker BN, Marcadier J, FORGE Canada Consortium, Majewski J, Bulman DE, White SM, Boycott KM. Mutations in SRCAP, encoding SNF2-related CREBBP activator protein, cause floating-harbor syndrome. American Journal of Human Genetics. 2012;90:308-313.

    Not in our library10.1016/j.ajhg.2011.12.001

  126. 126.

    Horikawa Y, Iwasaki N, Hara M, Furuta H, Hinokio Y, Cockburn BN, Lindner T, Yamagata K, Ogata M, Tomonaga O, Kuroki H, Kasahara T, Iwamoto Y, Bell GI. Mutation in hepatocyte nuclear factor-1 beta gene (Tcf2) associated with MODY. Nature Genetics. 1997;17:384-385.

    Not in our library10.1038/ng1297-384

  127. 127.

    Hormozdiari F, van de Bunt M, Segrè AV, Li X, Joo JWJ, Bilow M, Sul JH, Sankararaman S, Pasaniuc B, Eskin E. Colocalization of GWAS and eQTL signals detects target genes. American Journal of Human Genetics. 2016;99:1245-1260.

    Not in our library10.1016/j.ajhg.2016.10.003

  128. 128.

    Horn D, Robinson PN. Progeroid facial features and lipodystrophy associated with a novel splice site mutation in the final intron of the FBN1 gene. American Journal of Medical Genetics Part A. 2011;155:721-724.

    Not in our library10.1002/ajmg.a.33905

  129. 129.

    Hugot JP, Chamaillard M, Zouali H, Lesage S, Cézard JP, Belaiche J, Almer S, Tysk C, O’Morain CA, Gassull M, Binder V, Finkel Y, Cortot A, Modigliani R, Laurent-Puig P, Gower-Rousseau C, Macry J, Colombel JF, Sahbatou M, Thomas G. Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn’s disease. Nature. 2001;411:599-603.

    Not in our library10.1038/35079107

  130. 130.

    Huh D, Paulsson J. Non-Genetic heterogeneity from stochastic partitioning at cell division. Nature Genetics. 2011;43:95-100.

    Not in our library10.1038/ng.729

  131. 131.

    Hukku A, Pividori M, Luca F, Pique-Regi R, Im HK, Wen X. Probabilistic colocalization of genetic variants from complex and molecular traits: promise and limitations. American Journal of Human Genetics. 2021;108:25-35.

    Not in our library10.1016/j.ajhg.2020.11.012

  132. 132.

    Hyland VJ, Robertson SP, Flanagan S, Savarirayan R, Roscioli T, Masel J, Hayes M, Glass IA. Somatic and germline mosaicism for a R248C missense mutation in FGFR3, resulting in a skeletal dysplasia distinct from thanatophoric dysplasia. American Journal of Medical Genetics. Part A. 2003;120A:157-168.

    Not in our library10.1002/ajmg.a.20012

  133. 133.

    Iijima H, Emi M, Wada M, Daimon M, Toriyama S, Koyano S, Sato H, Hopkins PN, Hunt SC, Kubota I, Kawata S, Kato T. Association of an intronic haplotype of the LIPC gene with hyperalphalipoproteinemia in two independent populations. Journal of Human Genetics. 2008;53:193-200.

    Not in our library10.1007/s10038-007-0236-0

  134. 134.

    Isaacs A, Sayed-Tabatabaei FA, Njajou OT, Witteman JCM, van Duijn CM. The -514 C- > T hepatic lipase promoter region polymorphism and plasma lipids: a meta-analysis. The Journal of Clinical Endocrinology and Metabolism. 2004;89:3858-3863.

    Not in our library10.1210/jc.2004-0188

  135. 135.

    Jerber J, Seaton DD, Cuomo ASE, Kumasaka N, Haldane J, Steer J, Patel M, Pearce D, Andersson M, Bonder MJ, Mountjoy E, Ghoussaini M, Lancaster MA, HipSci Consortium, Marioni JC, Merkle FT, Gaffney DJ, Stegle O. Population-Scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nature Genetics. 2021;53:304-312.

    Not in our library10.1038/s41588-021-00801-6

  136. 136.

    Jiang XC, Bruce C, Mar J, Lin M, Ji Y, Francone OL, Tall AR. Targeted mutation of plasma phospholipid transfer protein gene markedly reduces high-density lipoprotein levels. The Journal of Clinical Investigation. 1999;103:907-914.

    Not in our library10.1172/jci5578

  137. 137.

    Kathiresan S, Srivastava D. Genetics of human cardiovascular disease. Cell. 2012;148:1242-1257.

    Not in our library10.1016/j.cell.2012.03.001

  138. 138.

    Kawashiri M, Zhang Y, Usher D, Reilly M, Puré E, Rader DJ. Effects of coexpression of the LDL receptor and APOE on cholesterol metabolism and atherosclerosis in LDL receptor-deficient mice. Journal of Lipid Research. 2001;42:943-950.

    Not in our library10.1016/s0022-2275(20)31618-7

  139. 139.
    Mutations in FKBP10 cause recessive osteogenesis imperfecta and bruck syndrome · 2010
    No notice
  140. 140.

    Khan Z, Ford MJ, Cusanovich DA, Mitrano A, Pritchard JK, Gilad Y. Primate transcript and protein expression levels evolve under compensatory selection pressures. Science. 2013;342:1100-1104.

    Not in our library10.1126/science.1242379

  141. 141.

    Khanim F, Kirk J, Latif F, Barrett TG. Wfs1/Wolframin mutations, Wolfram syndrome, and associated diseases. Human Mutation. 2001;17:357-367.

    Not in our library10.1002/humu.1110

  142. 142.
    Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations · 2017
    No notice
  143. 143.

    Knowles DA, Davis JR, Edgington H, Raj A, Favé M-J, Zhu X, Potash JB, Weissman MM, Shi J, Levinson DF, Awadalla P, Mostafavi S, Montgomery SB, Battle A. Allele-Specific expression reveals interactions between genetic variation and environment. Nature Methods. 2017;14:699-702.

    Not in our library10.1038/nmeth.4298

  144. 144.

    Kosaki K, Suzuki T, Muroya K, Hasegawa T, Sato S, Matsuo N, Kosaki R, Nagai T, Hasegawa Y, Ogata T. Ptpn11 (protein-tyrosine phosphatase, nonreceptor-type 11) mutations in seven Japanese patients with Noonan syndrome. The Journal of Clinical Endocrinology and Metabolism. 2002;87:3529-3533.

    Not in our library10.1210/jcem.87.8.8694

  145. 145.

    Krantz ID, McCallum J, DeScipio C, Kaur M, Gillis LA, Yaeger D, Jukofsky L, Wasserman N, Bottani A, Morris CA, Nowaczyk MJM, Toriello H, Bamshad MJ, Carey JC, Rappaport E, Kawauchi S, Lander AD, Calof AL, Li H-H, Devoto M, Jackson LG. Cornelia de Lange syndrome is caused by mutations in NIPBL, the human homolog of Drosophila melanogaster Nipped-B. Nature Genetics. 2004;36:631-635.

    Not in our library10.1038/ng1364

  146. 146.

    Kruszka P, Berger SI, Casa V, Dekker MR, Gaesser J, Weiss K, Martinez AF, Murdock DR, Louie RJ, Prijoles EJ, Lichty AW, Brouwer OF, Zonneveld-Huijssoon E, Stephan MJ, Hogue J, Hu P, Tanima-Nagai M, Everson JL, Prasad C, Cereda A, Iascone M, Schreiber A, Zurcher V, Corsten-Janssen N, Escobar L, Clegg NJ, Delgado MR, Hajirnis O, Balasubramanian M, Kayserili H, Deardorff M, Poot RA, Wendt KS, Lipinski RJ, Muenke M. Cohesin complex-associated holoprosencephaly. Brain. 2019;142:2631-2643.

    Not in our library10.1093/brain/awz210

  147. 147.

    Kumar N, Singh A, Kulkarni RV. Transcriptional bursting in gene expression: analytical results for general stochastic models. PLOS Computational Biology. 2015;11:e1004292.

    Not in our library10.1371/journal.pcbi.1004292

  148. 148.

    Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, Amin V, Whitaker JW, Schultz MD, Ward LD, Sarkar A, Quon G, Sandstrom RS, Eaton ML, Wu YC, Pfenning AR, Wang X, Claussnitzer M, Liu Y, Coarfa C, Harris RA, Shoresh N, Epstein CB, Gjoneska E, Leung D, Xie W, Hawkins RD, Lister R, Hong C, Gascard P, Mungall AJ, Moore R, Chuah E, Tam A, Canfield TK, Hansen RS, Kaul R, Sabo PJ, Bansal MS, Carles A, Dixon JR, Farh KH, Feizi S, Karlic R, Kim AR, Kulkarni A, Li D, Lowdon R, Elliott G, Mercer TR, Neph SJ, Onuchic V, Polak P, Rajagopal N, Ray P, Sallari RC, Siebenthall KT, Sinnott-Armstrong NA, Stevens M, Thurman RE, Wu J, Zhang B, Zhou X, Beaudet AE, Boyer LA, De Jager PL, Farnham PJ, Fisher SJ, Haussler D, Jones SJM, Li W, Marra MA, McManus MT, Sunyaev S, Thomson JA, Tlsty TD, Tsai LH, Wang W, Waterland RA, Zhang MQ, Chadwick LH, Bernstein BE, Costello JF, Ecker JR, Hirst M, Meissner A, Milosavljevic A, Ren B, Stamatoyannopoulos JA, Wang T, Kellis M. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317-330.

    Not in our library10.1038/nature14248

  149. 149.

    Kusnadi EP, Timpone C, Topisirovic I, Larsson O, Furic L. Regulation of gene expression via translational buffering. Biochimica et Biophysica Acta. Molecular Cell Research. 2022;1869:119140.

    Not in our library10.1016/j.bbamcr.2021.119140

  150. 150.

    Labay V, Raz T, Baron D, Mandel H, Williams H, Barrett T, Szargel R, McDonald L, Shalata A, Nosaka K, Gregory S, Cohen N. Mutations in SLC19A2 cause thiamine-responsive megaloblastic anaemia associated with diabetes mellitus and deafness. Nature Genetics. 1999;22:300-304.

    Not in our library10.1038/10372

  151. 151.

    Larson DR, Singer RH, Zenklusen D. A single molecule view of gene expression. Trends in Cell Biology. 2009;19:630-637.

    Not in our library10.1016/j.tcb.2009.08.008

  152. 152.

    Laukkanen O, Lindström J, Eriksson J, Valle TT, Hämäläinen H, Ilanne-Parikka P, Keinänen-Kiukaanniemi S, Tuomilehto J, Uusitupa M, Laakso M, Finnish Diabetes Prevention Study. Polymorphisms in the SLC2A2 (GLUT2) gene are associated with the conversion from impaired glucose tolerance to type 2 diabetes: the Finnish diabetes prevention study. Diabetes. 2005;54:2256-2260.

    Not in our library10.2337/diabetes.54.7.2256

  153. 153.

    Le Goff C, Mahaut C, Wang LW, Allali S, Abhyankar A, Jensen S, Zylberberg L, Collod-Beroud G, Bonnet D, Alanay Y, Brady AF, Cordier MP, Devriendt K, Genevieve D, Kiper PÖS, Kitoh H, Krakow D, Lynch SA, Le Merrer M, Mégarbane A, Mortier G, Odent S, Polak M, Rohrbach M, Sillence D, Stolte-Dijkstra I, Superti-Furga A, Rimoin DL, Topouchian V, Unger S, Zabel B, Bole-Feysot C, Nitschke P, Handford P, Casanova JL, Boileau C, Apte SS, Munnich A, Cormier-Daire V. Mutations in the TGFβ binding-protein-like domain 5 of FBN1 are responsible for acromicric and geleophysic dysplasias. American Journal of Human Genetics. 2011;89:7-14.

    Not in our library10.1016/j.ajhg.2011.05.012

  154. 154.

    Le Goff C, Mahaut C, Abhyankar A, Le Goff W, Serre V, Afenjar A, Destrée A, di Rocco M, Héron D, Jacquemont S, Marlin S, Simon M, Tolmie J, Verloes A, Casanova JL, Munnich A, Cormier-Daire V. Mutations at a single codon in mad homology 2 domain of smad4 cause myhre syndrome. Nature Genetics. 2012;44:85-88.

    Not in our library10.1038/ng.1016

  155. 155.

    Le Goff C, Mahaut C, Bottani A, Doray B, Goldenberg A, Moncla A, Odent S, Nitschke P, Munnich A, Faivre L, Cormier-Daire V. Not all floating-harbor syndrome cases are due to mutations in exon 34 of SRCAP. Human Mutation. 2013;34:88-92.

    Not in our library10.1002/humu.22216

  156. 156.
    Novel Crohn Disease Locus Identified by Genome-Wide Association Maps to a Gene Desert on 5p13.1 and Modulates Expression of PTGER4 · 2007
    No notice
  157. 157.

    Lindner TH, Njolstad PR, Horikawa Y, Bostad L, Bell GI, Sovik O. A novel syndrome of diabetes mellitus, renal dysfunction and genital malformation associated with a partial deletion of the pseudo-POU domain of hepatocyte nuclear factor-1beta. Human Molecular Genetics. 1999;8:2001-2008.

    Not in our library10.1093/hmg/8.11.2001

  158. 158.

    Lindor NM, Furuichi Y, Kitao S, Shimamoto A, Arndt C, Jalal S. Rothmund-Thomson syndrome due to RecQ4 helicase mutations: report and clinical and molecular comparisons with Bloom syndrome and Werner syndrome. American Journal of Medical Genetics. 2000;90:223-228.

    Not in our library10.1002/(sici)1096-8628(20000131)90:3<223::aid-ajmg7>3.0.co;2-z

  159. 159.

    Lindor NM, Gunawardena SR, Thibodeau SN. Mutations of Smad4 account for both LAPS and myhre syndromes. American Journal of Medical Genetics. Part A. 2012;158A:1520-1521.

    Not in our library10.1002/ajmg.a.35374

  160. 160.

    Liu JZ, van Sommeren S, Huang H, Ng SC, Alberts R, Takahashi A, Ripke S, Lee JC, Jostins L, Shah T, Abedian S, Cheon JH, Cho J, Dayani NE, Franke L, Fuyuno Y, Hart A, Juyal RC, Juyal G, Kim WH, Morris AP, Poustchi H, Newman WG, Midha V, Orchard TR, Vahedi H, Sood A, Sung JY, Malekzadeh R, Westra H-J, Yamazaki K, Yang S-K, International Multiple Sclerosis Genetics Consortium, International IBD Genetics Consortium, Barrett JC, Alizadeh BZ, Parkes M, Bk T, Daly MJ, Kubo M, Anderson CA, Weersma RK. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nature Genetics. 2015;47:979-986.

    Not in our library10.1038/ng.3359

  161. 161.

    Liu Y, Beyer A, Aebersold R. On the dependency of cellular protein levels on mRNA abundance. Cell. 2016;165:535-550.

    Not in our library10.1016/j.cell.2016.03.014

  162. 162.

    Mabuchi A, Manabe N, Haga N, Kitoh H, Ikeda T, Kawaji H, Tamai K, Hamada J, Nakamura S, Brunetti-Pierri N, Kimizuka M, Takatori Y, Nakamura K, Nishimura G, Ohashi H, Ikegawa S. Novel types of COMP mutations and genotype-phenotype association in pseudoachondroplasia and multiple epiphyseal dysplasia. Human Genetics. 2003;112:84-90.

    Not in our library10.1007/s00439-002-0845-9

  163. 163.

    Macfarlane WM, Frayling TM, Ellard S, Evans JC, Allen LI, Bulman MP, Ayres S, Shepherd M, Clark P, Millward A, Demaine A, Wilkin T, Docherty K, Hattersley AT. Missense mutations in the insulin promoter factor-1 gene predispose to type 2 diabetes. The Journal of Clinical Investigation. 1999;104:R33-R39.

    Not in our library10.1172/jci7449

  164. 164.

    Mackay DJG, Callaway JLA, Marks SM, White HE, Acerini CL, Boonen SE, Dayanikli P, Firth HV, Goodship JA, Haemers AP, Hahnemann JMD, Kordonouri O, Masoud AF, Oestergaard E, Storr J, Ellard S, Hattersley AT, Robinson DO, Temple IK. Hypomethylation of multiple imprinted loci in individuals with transient neonatal diabetes is associated with mutations in Zfp57. Nature Genetics. 2008;40:949-951.

    Not in our library10.1038/ng.187

  165. 165.

    Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, Payne AJ, Steinthorsdottir V, Scott RA, Grarup N, Cook JP, Schmidt EM, Wuttke M, Sarnowski C, Mägi R, Nano J, Gieger C, Trompet S, Lecoeur C, Preuss MH, Prins BP, Guo X, Bielak LF, Below JE, Bowden DW, Chambers JC, Kim YJ, Ng MCY, Petty LE, Sim X, Zhang W, Bennett AJ, Bork-Jensen J, Brummett CM, Canouil M, Ec Kardt K-U, Fischer K, Kardia SLR, Kronenberg F, Läll K, Liu C-T, Locke AE, Luan J, Ntalla I, Nylander V, Schönherr S, Schurmann C, Yengo L, Bottinger EP, Brandslund I, Christensen C, Dedoussis G, Florez JC, Ford I, Franco OH, Frayling TM, Giedraitis V, Hackinger S, Hattersley AT, Herder C, Ikram MA, Ingelsson M, Jørgensen ME, Jørgensen T, Kriebel J, Kuusisto J, Ligthart S, Lindgren CM, Linneberg A, Lyssenko V, Mamakou V, Meitinger T, Mohlke KL, Morris AD, Nadkarni G, Pankow JS, Peters A, Sattar N, Stančáková A, Strauch K, Taylor KD, Thorand B, Thorleifsson G, Thorsteinsdottir U, Tuomilehto J, Witte DR, Dupuis J, Peyser PA, Zeggini E, Loos RJF, Froguel P, Ingelsson E, Lind L, Groop L, Laakso M, Collins FS, Jukema JW, Palmer CNA, Grallert H, Metspalu A, Dehghan A, Köttgen A, Abecasis GR, Meigs JB, Rotter JI, Marchini J, Pedersen O, Hansen T, Langenberg C, Wareham NJ, Stefansson K, Gloyn AL, Morris AP, Boehnke M, McCarthy MI. Fine-Mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nature Genetics. 2018;50:1505-1513.

    Not in our library10.1038/s41588-018-0241-6

  166. 166.

    Maheshwari M, Belmont J, Fernbach S, Ho T, Molinari L, Yakub I, Yu F, Combes A, Towbin J, Craigen WJ, Gibbs R. Ptpn11 mutations in Noonan syndrome type I: detection of recurrent mutations in exons 3 and 13. Human Mutation. 2002;20:298-304.

    Not in our library10.1002/humu.10129

  167. 167.

    Makrythanasis P, Temtamy S, Aglan MS, Otaify GA, Hamamy H, Antonarakis SE. A novel homozygous mutation in FGFR3 causes tall stature, severe lateral tibial deviation, scoliosis, hearing impairment, camptodactyly, and arachnodactyly. Human Mutation. 2014;35:959-963.

    Not in our library10.1002/humu.22597

  168. 168.

    Malecki MT, Jhala US, Antonellis A, Fields L, Doria A, Orban T, Saad M, Warram JH, Montminy M, Krolewski AS. Mutations in NeuroD1 are associated with the development of type 2 diabetes mellitus. Nature Genetics. 1999;23:323-328.

    Not in our library10.1038/15500

  169. 169.

    Mancuso N, Shi H, Goddard P, Kichaev G, Gusev A, Pasaniuc B. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. American Journal of Human Genetics. 2017;100:473-487.

    Not in our library10.1016/j.ajhg.2017.01.031

  170. 170.

    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TFC, McCarroll SA, Visscher PM. Finding the missing heritability of complex diseases. Nature. 2009;461:747-753.

    Not in our library10.1038/nature08494

  171. 171.

    Mao H, Yang W, Lee PPW, Ho MH-K, Yang J, Zeng S, Chong C-Y, Lee T-L, Tu W, Lau Y-L. Exome sequencing identifies novel compound heterozygous mutations of IL-10 receptor 1 in neonatal-onset Crohn’s disease. Genes & Immunity. 2012;13:437-442.

    Not in our library10.1038/gene.2012.8

  172. 172.

    Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, Reynolds AP, Sandstrom R, Qu H, Brody J, Shafer A, Neri F, Lee K, Kutyavin T, Stehling-Sun S, Johnson AK, Canfield TK, Giste E, Diegel M, Bates D, Hansen RS, Neph S, Sabo PJ, Heimfeld S, Raubitschek A, Ziegler S, Cotsapas C, Sotoodehnia N, Glass I, Sunyaev SR, Kaul R, Stamatoyannopoulos JA. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190-1195.

    Not in our library10.1126/science.1222794

  173. 173.

    Mauvais-Jarvis F, Smith SB, Le May C, Leal SM, Gautier J-F, Molokhia M, Riveline J-P, Rajan AS, Kevorkian J-P, Zhang S, Vexiau P, German MS, Vaisse C. Pax4 gene variations predispose to ketosis-prone diabetes. Human Molecular Genetics. 2004;13:3151-3159.

    Not in our library10.1093/hmg/ddh341

  174. 174.

    McCarroll SA, Huett A, Kuballa P, Chilewski SD, Landry A, Goyette P, Zody MC, Hall JL, Brant SR, Cho JH, Duerr RH, Silverberg MS, Taylor KD, Rioux JD, Altshuler D, Daly MJ, Xavier RJ. Deletion polymorphism upstream of IRGM associated with altered IRGM expression and Crohn’s disease. Nature Genetics. 2008;40:1107-1112.

    Not in our library10.1038/ng.215

  175. 175.

    McCarthy JJ, Lehner T, Reeves C, Moliterno DJ, Newby LK, Rogers WJ, Topol EJ, Genequest investigators. Association of genetic variants in the HDL receptor, SR-B1, with abnormal lipids in women with coronary artery disease. Journal of Medical Genetics. 2003;40:453-458.

    Not in our library10.1136/jmg.40.6.453

  176. 176.

    McKusick-nathans institute of genetic medicine. Online Mendelian Inheritance in Man, OMIM. 2021.

    Not in our library

  177. 177.

    McManus CJ, May GE, Spealman P, Shteyman A. Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome Research. 2014;24:422-430.

    Not in our library10.1101/gr.164996.113

  178. 178.

    McManus J, Cheng Z, Vogel C. Next-Generation analysis of gene expression regulation -- comparing the roles of synthesis and degradation. Molecular BioSystems. 2015;11:2680-2689.

    Not in our library10.1039/c5mb00310e

  179. 179.

    Menke LA, van Belzen MJ, Alders M, Cristofoli F, DDD Study, Ehmke N, Fergelot P, Foster A, Gerkes EH, Hoffer MJV, Horn D, Kant SG, Lacombe D, Leon E, Maas SM, Melis D, Muto V, Park S-M, Peeters H, Peters DJM, Pfundt R, van Ravenswaaij-Arts CMA, Tartaglia M, Hennekam RCM. CREBBP mutations in individuals without Rubinstein-Taybi syndrome phenotype. American Journal of Medical Genetics. Part A. 2016;170:2681-2693.

    Not in our library10.1002/ajmg.a.37800

  180. 180.

    Menke LA, DDD study, Gardeitchik T, Hammond P, Heimdal KR, Houge G, Hufnagel SB, Ji J, Johansson S, Kant SG, Kinning E, Leon EL, Newbury-Ecob R, Paolacci S, Pfundt R, Ragge NK, Rinne T, Ruivenkamp C, Saitta SC, Sun Y, Tartaglia M, Terhal PA, van Essen AJ, Vigeland MD, Xiao B, Hennekam RC. Further delineation of an entity caused by CREBBP and ep300 mutations but not resembling Rubinstein-Taybi syndrome. American Journal of Medical Genetics. Part A. 2018;176:862-876.

    Not in our library10.1002/ajmg.a.38626

  181. 181.
    Index and biological spectrum of human DNase I hypersensitive sites · 2020
    No notice
  182. 182.

    Mostafavi H, Spence JP, Naqvi S, Pritchard JK. Limited Overlap of EQTLs and GWAS Hits Due to Systematic Differences in Discovery. bioRxiv. 2022.

    Not in our library10.1101/2022.05.07.491045

  183. 183.

    Mountjoy E, Schmidt EM, Carmona M, Schwartzentruber J, Peat G, Miranda A, Fumis L, Hayhurst J, Buniello A, Karim MA, Wright D, Hercules A, Papa E, Fauman EB, Barrett JC, Todd JA, Ochoa D, Dunham I, Ghoussaini M. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nature Genetics. 2021;53:1527-1533.

    Not in our library10.1038/s41588-021-00945-5

  184. 184.

    Mu Z, Wei W, Fair B, Miao J, Zhu P, Li YI. The impact of cell type and context-dependent regulatory variants on human immune traits. Genome Biology. 2021;22:122.

    Not in our library10.1186/s13059-021-02334-x

  185. 185.

    Nasser J, Bergman DT, Fulco CP, Guckelberger P, Doughty BR, Patwardhan TA, Jones TR, Nguyen TH, Ulirsch JC, Lekschas F, Mualim K, Natri HM, Weeks EM, Munson G, Kane M, Kang HY, Cui A, Ray JP, Eisenhaure TM, Collins RL, Dey K, Pfister H, Price AL, Epstein CB, Kundaje A, Xavier RJ, Daly MJ, Huang H, Finucane HK, Hacohen N, Lander ES, Engreitz JM. Genome-Wide enhancer maps link risk variants to disease genes. Nature. 2021;593:238-243.

    Not in our library10.1038/s41586-021-03446-x

  186. 186.

    Nathan A, Asgari S, Ishigaki K, Valencia C, Amariuta T, Luo Y, Beynor JI, Baglaenko Y, Suliman S, Price AL, Lecca L, Murray MB, Moody DB, Raychaudhuri S. Single-Cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature. 2022;606:120-128.

    Not in our library10.1038/s41586-022-04713-1

  187. 187.

    Neavin D, Nguyen Q, Daniszewski MS, Liang HH, Chiu HS, Wee YK, Senabouth A, Lukowski SW, Crombie DE, Lidgerwood GE, Hernández D, Vickers JC, Cook AL, Palpant NJ, Pébay A, Hewitt AW, Powell JE. Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. Genome Biology. 2021;22:76.

    Not in our library10.1186/s13059-021-02293-3

  188. 188.

    Neve B, Fernandez-Zapico ME, Ashkenazi-Katalan V, Dina C, Hamid YH, Joly E, Vaillant E, Benmezroua Y, Durand E, Bakaher N, Delannoy V, Vaxillaire M, Cook T, Dallinga-Thie GM, Jansen H, Charles MA, Clément K, Galan P, Hercberg S, Helbecque N, Charpentier G, Prentki M, Hansen T, Pedersen O, Urrutia R, Melloul D, Froguel P. Role of transcription factor KLF11 and its diabetes-associated gene variants in pancreatic beta cell function. PNAS. 2005;102:4807-4812.

    Not in our library10.1073/pnas.0409177102

  189. 189.
    Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS · 2010
    No notice
  190. 190.

    Nicolas D, Phillips NE, Naef F. What shapes eukaryotic transcriptional bursting? Molecular BioSystems. 2017;13:1280-1290.

    Not in our library10.1039/c7mb00154a

  191. 191.

    NJC12. Software Heritage. 2023.

    Not in our library

  192. 192.

    O’Driscoll M, Ruiz-Perez VL, Woods CG, Jeggo PA, Goodship JA. A splicing mutation affecting expression of ataxia-telangiectasia and Rad3-related protein (ATR) results in seckel syndrome. Nature Genetics. 2003;33:497-501.

    Not in our library10.1038/ng1129

  193. 193.
    Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure · 2022
    No notice
  194. 194.

    Ogi T, Walker S, Stiff T, Hobson E, Limsirichaikul S, Carpenter G, Prescott K, Suri M, Byrd PJ, Matsuse M, Mitsutake N, Nakazawa Y, Vasudevan P, Barrow M, Stewart GS, Taylor AMR, O’Driscoll M, Jeggo PA. Identification of the first ATRIP-deficient patient and novel mutations in ATR define a clinical spectrum for ATR-ATRIP seckel syndrome. PLOS Genetics. 2012;8:e1002945.

    Not in our library10.1371/journal.pgen.1002945

  195. 195.

    Ogura Y, Bonen DK, Inohara N, Nicolae DL, Chen FF, Ramos R, Britton H, Moran T, Karaliuskas R, Duerr RH, Achkar JP, Brant SR, Bayless TM, Kirschner BS, Hanauer SB, Nuñez G, Cho JH. A frameshift mutation in NOD2 associated with susceptibility to Crohn’s disease. Nature. 2001;411:603-606.

    Not in our library10.1038/35079114

  196. 196.

    Oishi K, Hofmann S, Diaz GA, Brown T, Manwani D, Ng L, Young R, Vlassara H, Ioannou YA, Forrest D, Gelb BD. Targeted disruption of slc19a2, the gene encoding the high-affinity thiamin transporter thtr-1, causes diabetes mellitus, sensorineural deafness and megaloblastosis in mice. Human Molecular Genetics. 2002;11:2951-2960.

    Not in our library10.1093/hmg/11.23.2951

  197. 197.

    Ordovas JM, Schaefer EJ, Salem D, Ward RH, Glueck CJ, Vergani C, Wilson PW, Karathanasis SK. Apolipoprotein A-I gene polymorphism associated with premature coronary artery disease and familial hypoalphalipoproteinemia. The New England Journal of Medicine. 1986;314:671-677.

    Not in our library10.1056/nejm198603133141102

  198. 198.

    Ota M, Nagafuchi Y, Hatano H, Ishigaki K, Terao C, Takeshima Y, Yanaoka H, Kobayashi S, Okubo M, Shirai H, Sugimori Y, Maeda J, Nakano M, Yamada S, Yoshida R, Tsuchiya H, Tsuchida Y, Akizuki S, Yoshifuji H, Ohmura K, Mimori T, Yoshida K, Kurosaka D, Okada M, Setoguchi K, Kaneko H, Ban N, Yabuki N, Matsuki K, Mutoh H, Oyama S, Okazaki M, Tsunoda H, Iwasaki Y, Sumitomo S, Shoda H, Kochi Y, Okada Y, Yamamoto K, Okamura T, Fujio K. Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases. Cell. 2021;184:3006-3021.

    Not in our library10.1016/j.cell.2021.03.056

  199. 199.

    Pai AA, Cain CE, Mizrahi-Man O, De Leon S, Lewellen N, Veyrieras J-B, Degner JF, Gaffney DJ, Pickrell JK, Stephens M, Pritchard JK, Gilad Y. The contribution of RNA decay quantitative trait loci to inter-individual variation in steady-state gene expression levels. PLOS Genetics. 2012;8:e1003000.

    Not in our library10.1371/journal.pgen.1003000

  200. 200.
    The Genetic and Mechanistic Basis for Variation in Gene Regulation · 2015
    No notice
  201. 201.

    Pandit B, Sarkozy A, Pennacchio LA, Carta C, Oishi K, Martinelli S, Pogna EA, Schackwitz W, Ustaszewska A, Landstrom A, Bos JM, Ommen SR, Esposito G, Lepri F, Faul C, Mundel P, López Siguero JP, Tenconi R, Selicorni A, Rossi C, Mazzanti L, Torrente I, Marino B, Digilio MC, Zampino G, Ackerman MJ, Dallapiccola B, Tartaglia M, Gelb BD. Gain-Of-Function Raf1 mutations cause Noonan and LEOPARD syndromes with hypertrophic cardiomyopathy. Nature Genetics. 2007;39:1007-1012.

    Not in our library10.1038/ng2073

  202. 202.

    Park Y, Sarkar A, Bhutani K, Kellis M. Multi-Tissue Polygenic Models for Transcriptome-Wide Association Studies. bioRxiv. 2017.

    Not in our library10.1101/107623

  203. 203.

    Patel D, Zhang X, Farrell JJ, Chung J, Stein TD, Lunetta KL, Farrer LA. Cell-type-specific expression quantitative trait loci associated with alzheimer disease in blood and brain tissue. Translational Psychiatry. 2021;11:250.

    Not in our library10.1038/s41398-021-01373-z

  204. 204.

    Pedraza JM, Paulsson J. Effects of molecular memory and bursting on fluctuations in gene expression. Science. 2008;319:339-343.

    Not in our library10.1126/science.1144331

  205. 205.

    Perez RK, Gordon MG, Subramaniam M, Kim MC, Hartoularos GC, Targ S, Sun Y, Ogorodnikov A, Bueno R, Lu A, Thompson M, Rappoport N, Dahl A, Lanata CM, Matloubian M, Maliskova L, Kwek SS, Li T, Slyper M, Waldman J, Dionne D, Rozenblatt-Rosen O, Fong L, Dall’Era M, Balliu B, Regev A, Yazdany J, Criswell LA, Zaitlen N, Ye CJ. Single-Cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science. 2022;376:eabf1970.

    Not in our library10.1126/science.abf1970

  206. 206.

    Piane M, Della Monica M, Piatelli G, Lulli P, Lonardo F, Chessa L, Scarano G. Majewski osteodysplastic primordial dwarfism type II (MOPD II) syndrome previously diagnosed as seckel syndrome: report of a novel mutation of the PCNT gene. American Journal of Medical Genetics. Part A. 2009;149A:2452-2456.

    Not in our library10.1002/ajmg.a.33035

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    Pinney SE, Oliver-Krasinski J, Ernst L, Hughes N, Patel P, Stoffers DA, Russo P, De León DD. Neonatal diabetes and congenital malabsorptive diarrhea attributable to a novel mutation in the human neurogenin-3 gene coding sequence. The Journal of Clinical Endocrinology and Metabolism. 2011;96:1960-1965.

    Not in our library10.1210/jc.2011-0029

  208. 208.

    Plengvidhya N, Kooptiwut S, Songtawee N, Doi A, Furuta H, Nishi M, Nanjo K, Tantibhedhyangkul W, Boonyasrisawat W, Yenchitsomanus P, Doria A, Banchuin N. Pax4 mutations in thais with maturity onset diabetes of the young. The Journal of Clinical Endocrinology and Metabolism. 2007;92:2821-2826.

    Not in our library10.1210/jc.2006-1927

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    Polymeropoulos MH, Ide SE, Wright M, Goodship J, Weissenbach J, Pyeritz RE, Da Silva EO, Ortiz De Luna RI, Francomano CA. The gene for the Ellis-van Creveld syndrome is located on chromosome 4p16. Genomics. 1996;35:1-5.

    Not in our library10.1006/geno.1996.0315

  210. 210.

    Poulton CJ, Schot R, Kia SK, Jones M, Verheijen FW, Venselaar H, de Wit M-CY, de Graaff E, Bertoli-Avella AM, Mancini GMS. Microcephaly with simplified gyration, epilepsy, and infantile diabetes linked to inappropriate apoptosis of neural progenitors. American Journal of Human Genetics. 2011;89:265-276.

    Not in our library10.1016/j.ajhg.2011.07.006

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    Prescott NJ, Dominy KM, Kubo M, Lewis CM, Fisher SA, Redon R, Huang N, Stranger BE, Blaszczyk K, Hudspith B, Parkes G, Hosono N, Yamazaki K, Onnie CM, Forbes A, Dermitzakis ET, Nakamura Y, Mansfield JC, Sanderson J, Hurles ME, Roberts RG, Mathew CG. Independent and population-specific association of risk variants at the IRGM locus with Crohn’s disease. Human Molecular Genetics. 2010;19:1828-1839.

    Not in our library10.1093/hmg/ddq041

  212. 212.

    Puig-Hervás MT, Temtamy S, Aglan M, Valencia M, Martínez-Glez V, Ballesta-Martínez MJ, López-González V, Ashour AM, Amr K, Pulido V, Guillén-Navarro E, Lapunzina P, Caparrós-Martín JA, Ruiz-Perez VL. Mutations in PLOD2 cause autosomal-recessive connective tissue disorders within the Bruck syndrome -- osteogenesis imperfecta phenotypic spectrum. Human Mutation. 2012;33:1444-1449.

    Not in our library10.1002/humu.22133

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    Pullinger CR, Hennessy LK, Chatterton JE, Liu W, Love JA, Mendel CM, Frost PH, Malloy MJ, Schumaker VN, Kane JP. Familial ligand-defective apolipoprotein B. identification of a new mutation that decreases LDL receptor binding affinity. The Journal of Clinical Investigation. 1995;95:1225-1234.

    Not in our library10.1172/jci117772

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    Qiu H, Zhang B, Zhou T. Analytical results for a generalized model of bursty gene expression with molecular memory. Physical Review. E. 2019;100:012128.

    Not in our library10.1103/physreve.100.012128

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    Raeder H, Johansson S, Holm PI, Haldorsen IS, Mas E, Sbarra V, Nermoen I, Eide SÅ, Grevle L, Bjørkhaug L, Sagen JV, Aksnes L, Søvik O, Lombardo D, Molven A, Njølstad PR. Mutations in the cel VNTR cause a syndrome of diabetes and pancreatic exocrine dysfunction. Nature Genetics. 2006;38:54-62.

    Not in our library10.1038/ng1708

  216. 216.

    Rai V, Quang DX, Erdos MR, Cusanovich DA, Daza RM, Narisu N, Zou LS, Didion JP, Guan Y, Shendure J, Parker SCJ, Collins FS. Single-Cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Molecular Metabolism. 2020;32:109-121.

    Not in our library10.1016/j.molmet.2019.12.006

  217. 217.

    Raj A, van Oudenaarden A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008;135:216-226.

    Not in our library10.1016/j.cell.2008.09.050

  218. 218.

    Raj A, van Oudenaarden A. Single-Molecule approaches to stochastic gene expression. Annual Review of Biophysics. 2009;38:255-270.

    Not in our library10.1146/annurev.biophys.37.032807.125928

  219. 219.

    Rauch A, Thiel CT, Schindler D, Wick U, Crow YJ, Ekici AB, van Essen AJ, Goecke TO, Al-Gazali L, Chrzanowska KH, Zweier C, Brunner HG, Becker K, Curry CJ, Dallapiccola B, Devriendt K, Dörfler A, Kinning E, Megarbane A, Meinecke P, Semple RK, Spranger S, Toutain A, Trembath RC, Voss E, Wilson L, Hennekam R, de Zegher F, Dörr H-G, Reis A. Mutations in the pericentrin (PCNT) gene cause primordial dwarfism. Science. 2008;319:816-819.

    Not in our library10.1126/science.1151174

  220. 220.

    Razzaque MA, Nishizawa T, Komoike Y, Yagi H, Furutani M, Amo R, Kamisago M, Momma K, Katayama H, Nakagawa M, Fujiwara Y, Matsushima M, Mizuno K, Tokuyama M, Hirota H, Muneuchi J, Higashinakagawa T, Matsuoka R. Germline gain-of-function mutations in RAF1 cause Noonan syndrome. Nature Genetics. 2007;39:1013-1017.

    Not in our library10.1038/ng2078

  221. 221.

    Reis AF, Ye W-Z, Dubois-Laforgue D, Bellanné-Chantelot C, Timsit J, Velho G. Association of a variant in exon 31 of the sulfonylurea receptor 1 (SUR1) gene with type 2 diabetes mellitus in French Caucasians. Human Genetics. 2000;107:138-144.

    Not in our library10.1007/s004390000345

  222. 222.

    Rivas MA, Beaudoin M, Gardet A, Stevens C, Sharma Y, Zhang CK, Boucher G, Ripke S, Ellinghaus D, Burtt N, Fennell T, Kirby A, Latiano A, Goyette P, Green T, Halfvarson J, Haritunians T, Korn JM, Kuruvilla F, Lagacé C, Neale B, Lo KS, Schumm P, Törkvist L, Dubinsky MC, Brant SR, Silverberg MS, Duerr RH, Altshuler D, Gabriel S, Lettre G, Franke A, D’Amato M, McGovern DPB, Cho JH, Rioux JD, Xavier RJ, Daly MJ, National Institute of Diabetes and Digestive Kidney Diseases Inflammatory Bowel Disease Genetics Consortium, United Kingdom Inflammatory Bowel Disease Genetics Consortium, International Inflammatory Bowel Disease Genetics Consortium. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nature Genetics. 2011;43:1066-1073.

    Not in our library10.1038/ng.952

  223. 223.

    Rivas MA, Graham D, Sulem P, Stevens C, Desch AN, Goyette P, Gudbjartsson D, Jonsdottir I, Thorsteinsdottir U, Degenhardt F, Mucha S, Kurki MI, Li D, D’Amato M, Annese V, Vermeire S, Weersma RK, Halfvarson J, Paavola-Sakki P, Lappalainen M, Lek M, Cummings B, Tukiainen T, Haritunians T, Halme L, Koskinen LLE, Ananthakrishnan AN, Luo Y, Heap GA, Visschedijk MC, MacArthur DG, Neale BM, Ahmad T, Anderson CA, Brant SR, Duerr RH, Silverberg MS, Cho JH, Palotie A, Saavalainen P, Kontula K, Färkkilä M, McGovern DPB, Franke A, Stefansson K, Rioux JD, Xavier RJ, Daly MJ, UK IBD Genetics Consortium, NIDDK IBD Genetics Consortium. A protein-truncating R179X variant in RNF186 confers protection against ulcerative colitis. Nature Communications. 2016;7:12342.

    Not in our library10.1038/ncomms12342

  224. 224.

    Rossi A, Superti-Furga A. Mutations in the diastrophic dysplasia sulfate transporter (DTDST) gene (SLC26A2): 22 novel mutations, mutation review, associated skeletal phenotypes, and diagnostic relevance. Human Mutation. 2001;17:159-171.

    Not in our library10.1002/humu.1

  225. 225.

    Rubio-Cabezas O, Jensen JN, Hodgson MI, Codner E, Ellard S, Serup P, Hattersley AT. Permanent neonatal diabetes and enteric anendocrinosis associated with biallelic mutations in Neurog3. Diabetes. 2011;60:1349-1353.

    Not in our library10.2337/db10-1008

  226. 226.

    Ruiz-Perez VL, Tompson SWJ, Blair HJ, Espinoza-Valdez C, Lapunzina P, Silva EO, Hamel B, Gibbs JL, Young ID, Wright MJ, Goodship JA. Mutations in two nonhomologous genes in a head-to-head configuration cause Ellis-van Creveld syndrome. American Journal of Human Genetics. 2003;72:728-732.

    Not in our library10.1086/368063

  227. 227.

    Rust S, Rosier M, Funke H, Real J, Amoura Z, Piette JC, Deleuze JF, Brewer HB, Duverger N, Denèfle P, Assmann G. Tangier disease is caused by mutations in the gene encoding ATP-binding cassette transporter 1. Nature Genetics. 1999;22:352-355.

    Not in our library10.1038/11921

  228. 228.

    Sansbury FH, Flanagan SE, Houghton JAL, Shuixian Shen FL, Al-Senani AMS, Habeb AM, Abdullah M, Kariminejad A, Ellard S, Hattersley AT. Slc2A2 mutations can cause neonatal diabetes, suggesting GLUT2 may have a role in human insulin secretion. Diabetologia. 2012;55:2381-2385.

    Not in our library10.1007/s00125-012-2595-0

  229. 229.

    Sansbury FH, Kirel B, Caswell R, Lango Allen H, Flanagan SE, Hattersley AT, Ellard S, Shaw-Smith CJ. Biallelic Rfx6 mutations can cause childhood as well as neonatal onset diabetes mellitus. European Journal of Human Genetics. 2015;23:1744-1748.

    Not in our library10.1038/ejhg.2015.161

  230. 230.

    Savage DB, Agostini M, Barroso I, Gurnell M, Luan J, Meirhaeghe A, Harding A-H, Ihrke G, Rajanayagam O, Soos MA, George S, Berger D, Thomas EL, Bell JD, Meeran K, Ross RJ, Vidal-Puig A, Wareham NJ, O’Rahilly S, Chatterjee VKK, Schafer AJ. Digenic inheritance of severe insulin resistance in a human pedigree. Nature Genetics. 2002;31:379-384.

    Not in our library10.1038/ng926

  231. 231.

    Schaefke B, Sun W, Li YS, Fang L, Chen W. The evolution of posttranscriptional regulation. Wiley Interdisciplinary Reviews. RNA. 2018;9:e1485.

    Not in our library10.1002/wrna.1485

  232. 232.

    Schafer S, Adami E, Heinig M, Rodrigues KEC, Kreuchwig F, Silhavy J, van Heesch S, Simaite D, Rajewsky N, Cuppen E, Pravenec M, Vingron M, Cook SA, Hubner N. Translational regulation shapes the molecular landscape of complex disease phenotypes. Nature Communications. 2015;6:7200.

    Not in our library10.1038/ncomms8200

  233. 233.

    Schmiedel BJ, Singh D, Madrigal A, Valdovino-Gonzalez AG, White BM, Zapardiel-Gonzalo J, Ha B, Altay G, Greenbaum JA, McVicker G, Seumois G, Rao A, Kronenberg M, Peters B, Vijayanand P. Impact of genetic polymorphisms on human immune cell gene expression. Cell. 2018;175:1701-1715.

    Not in our library10.1016/j.cell.2018.10.022

  234. 234.

    Schmiedel BJ, Gonzalez-Colin C, Fajardo V, Rocha J, Madrigal A, Ramírez-Suástegui C, Bhattacharyya S, Simon H, Greenbaum JA, Peters B, Seumois G, Ay F, Chandra V, Vijayanand P. Single-Cell eQTL analysis of activated T cell subsets reveals activation and cell type-dependent effects of disease-risk variants. Science Immunology. 2022;7:eabm2508.

    Not in our library10.1126/sciimmunol.abm2508

  235. 235.

    Schneider WJ, Kovanen PT, Brown MS, Goldstein JL, Utermann G, Weber W, Havel RJ, Kotite L, Kane JP, Innerarity TL, Mahley RW. Familial dysbetalipoproteinemia:abnormal binding of mutant apoprotein E to low density lipoprotein receptors of human fibroblasts and membranes from liver and adrenal of rats, rabbits, and cows. The Journal of Clinical Investigation. 1981;68:1075-1085.

    Not in our library10.1172/jci110330

  236. 236.

    Schubbert S, Zenker M, Rowe SL, Böll S, Klein C, Bollag G, van der Burgt I, Musante L, Kalscheuer V, Wehner L-E, Nguyen H, West B, Zhang KYJ, Sistermans E, Rauch A, Niemeyer CM, Shannon K, Kratz CP. Germline KRAS mutations cause Noonan syndrome. Nature Genetics. 2006;38:331-336.

    Not in our library10.1038/ng1748

  237. 237.

    Sellick GS, Barker KT, Stolte-Dijkstra I, Fleischmann C, Coleman RJ, Garrett C, Gloyn AL, Edghill EL, Hattersley AT, Wellauer PK, Goodwin G, Houlston RS. Mutations in Ptf1a cause pancreatic and cerebellar agenesis. Nature Genetics. 2004;36:1301-1305.

    Not in our library10.1038/ng1475

  238. 238.

    Senée V, Chelala C, Duchatelet S, Feng D, Blanc H, Cossec J-C, Charon C, Nicolino M, Boileau P, Cavener DR, Bougnères P, Taha D, Julier C. Mutations in Glis3 are responsible for a rare syndrome with neonatal diabetes mellitus and congenital hypothyroidism. Nature Genetics. 2006;38:682-687.

    Not in our library10.1038/ng1802

  239. 239.

    Shaheen R, Faqeih E, Ansari S, Abdel-Salam G, Al-Hassnan ZN, Al-Shidi T, Alomar R, Sogaty S, Alkuraya FS. Genomic analysis of primordial dwarfism reveals novel disease genes. Genome Research. 2014;24:291-299.

    Not in our library10.1101/gr.160572.113

  240. 240.

    Shahrezaei V, Swain PS. Analytical distributions for stochastic gene expression. PNAS. 2008;105:17256-17261.

    Not in our library10.1073/pnas.0803850105

  241. 241.

    Shalev SA, Tenenbaum-Rakover Y, Horovitz Y, Paz VP, Ye H, Carmody D, Highland HM, Boerwinkle E, Hanis CL, Muzny DM, Gibbs RA, Bell GI, Philipson LH, Greeley SAW. Microcephaly, epilepsy, and neonatal diabetes due to compound heterozygous mutations in IER3IP1: insights into the natural history of a rare disorder. Pediatric Diabetes. 2014;15:252-256.

    Not in our library10.1111/pedi.12086

  242. 242.

    Shaw-Smith C, Flanagan SE, Patch AM, Grulich-Henn J, Habeb AM, Hussain K, Pomahacova R, Matyka K, Abdullah M, Hattersley AT, Ellard S. Recessive slc19a2 mutations are a cause of neonatal diabetes mellitus in thiamine-responsive megaloblastic anaemia. Pediatric Diabetes. 2012;13:314-321.

    Not in our library10.1111/j.1399-5448.2012.00855.x

  243. 243.

    Shaw-Smith C. GATA4 mutations are a cause of neonatal and childhood-onset diabetes. Diabetes. 2014;63:2888-2894.

    Not in our library10.2337/db14-0061

  244. 244.

    Shimajiri Y, Sanke T, Furuta H, Hanabusa T, Nakagawa T, Fujitani Y, Kajimoto Y, Takasu N, Nanjo K. A missense mutation of Pax4 gene (R121W) is associated with type 2 diabetes in Japanese. Diabetes. 2001;50:2864-2869.

    Not in our library10.2337/diabetes.50.12.2864

  245. 245.

    Shimano H, Yamada N, Katsuki M, Shimada M, Gotoda T, Harada K, Murase T, Fukazawa C, Takaku F, Yazaki Y. Overexpression of apolipoprotein E in transgenic mice: marked reduction in plasma lipoproteins except high density lipoprotein and resistance against diet-induced hypercholesterolemia. PNAS. 1992a;89:1750-1754.

    Not in our library10.1073/pnas.89.5.1750

  246. 246.

    Shimano H, Yamada N, Katsuki M, Yamamoto K, Gotoda T, Harada K, Shimada M, Yazaki Y. Plasma lipoprotein metabolism in transgenic mice overexpressing apolipoprotein E. accelerated clearance of lipoproteins containing apolipoprotein B. The Journal of Clinical Investigation. 1992b;90:2084-2091.

    Not in our library10.1172/jci116091

  247. 247.

    Smith SB. Rfx6 directs islet formation and insulin production in mice and humans. Nature. 2010;463:775-780.

    Not in our library10.1038/nature08748

  248. 248.

    Soria LF, Ludwig EH, Clarke HR, Vega GL, Grundy SM, McCarthy BJ. Association between a specific apolipoprotein B mutation and familial defective apolipoprotein B-100. PNAS. 1989;86:587-591.

    Not in our library10.1073/pnas.86.2.587

  249. 249.

    Spotila LD, Sereda L, Prockop DJ. Partial isodisomy for maternal chromosome 7 and short stature in an individual with a mutation at the COL1A2 locus. American Journal of Human Genetics. 1992;51:1396-1405.

    Not in our library

  250. 250.

    Stoffel M, Duncan SA. The maturity-onset diabetes of the young (MODY1) transcription factor hnf4alpha regulates expression of genes required for glucose transport and metabolism. PNAS. 1997;94:13209-13214.

    Not in our library10.1073/pnas.94.24.13209

  251. 251.

    Stoffers DA, Ferrer J, Clarke WL, Habener JF. Early-Onset type-II diabetes mellitus (MODY4) linked to IPF1. Nature Genetics. 1997;17:138-139.

    Not in our library10.1038/ng1097-138

  252. 252.

    Støy J, Edghill EL, Flanagan SE, Ye H, Paz VP, Pluzhnikov A, Below JE, Hayes MG, Cox NJ, Lipkind GM, Lipton RB, Greeley SAW, Patch AM, Ellard S, Steiner DF, Hattersley AT, Philipson LH, Bell GI, Neonatal Diabetes International Collaborative Group. Insulin gene mutations as a cause of permanent neonatal diabetes. PNAS. 2007;104:15040-15044.

    Not in our library10.1073/pnas.0707291104

  253. 253.

    Stránecký V, Hoischen A, Hartmannová H, Zaki MS, Chaudhary A, Zudaire E, Nosková L, Barešová V, Přistoupilová A, Hodaňová K, Sovová J, Hůlková H, Piherová L, Hehir-Kwa JY, de Silva D, Senanayake MP, Farrag S, Zeman J, Martásek P, Baxová A, Afifi HH, St Croix B, Brunner HG, Temtamy S, Kmoch S. Mutations in ANTXR1 cause GAPO syndrome. American Journal of Human Genetics. 2013;92:792-799.

    Not in our library10.1016/j.ajhg.2013.03.023

  254. 254.

    Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, Ingle CE, Dunning M, Flicek P, Koller D, Montgomery S, Tavaré S, Deloukas P, Dermitzakis ET. Population genomics of human gene expression. Nature Genetics. 2007;39:1217-1224.

    Not in our library10.1038/ng2142

  255. 255.

    Strober BJ, Elorbany R, Rhodes K, Krishnan N, Tayeb K, Battle A, Gilad Y. Dynamic genetic regulation of gene expression during cellular differentiation. Science. 2019;364:1287-1290.

    Not in our library10.1126/science.aaw0040

  256. 256.

    Strom TM, Hörtnagel K, Hofmann S, Gekeler F, Scharfe C, Rabl W, Gerbitz KD, Meitinger T. Diabetes insipidus, diabetes mellitus, optic atrophy and deafness (DIDMOAD) caused by mutations in a novel gene (wolframin) coding for a predicted transmembrane protein. Human Molecular Genetics. 1998;7:2021-2028.

    Not in our library10.1093/hmg/7.13.2021

  257. 257.

    Suter DM, Molina N, Gatfield D, Schneider K, Schibler U, Naef F. Mammalian genes are transcribed with widely different bursting kinetics. Science. 2011;332:472-474.

    Not in our library10.1126/science.1198817

  258. 258.

    Tai ES, Adiconis X, Ordovas JM, Carmena-Ramon R, Real J, Corella D, Ascaso J, Carmena R. Polymorphisms at the SRBI locus are associated with lipoprotein levels in subjects with heterozygous familial hypercholesterolemia. Clinical Genetics. 2003;63:53-58.

    Not in our library10.1034/j.1399-0004.2003.630108.x

  259. 259.

    Takenouchi T, Hida M, Sakamoto Y, Torii C, Kosaki R, Takahashi T, Kosaki K. Severe congenital lipodystrophy and a progeroid appearance: mutation in the penultimate exon of FBN1 causing a recognizable phenotype. American Journal of Medical Genetics Part A. 2013;161:3057-3062.

    Not in our library10.1002/ajmg.a.36157

  260. 260.
    Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program · 2021
    No notice
  261. 261.

    Tanzanella C, Antoccia A, Spadoni E, di Masi A, Pecile V, Demori E, Varon R, Marseglia GL, Tiepolo L, Maraschio P. Chromosome instability and nibrin protein variants in Nbs heterozygotes. European Journal of Human Genetics. 2003;11:297-303.

    Not in our library10.1038/sj.ejhg.5200962

  262. 262.

    Tartaglia M, Mehler EL, Goldberg R, Zampino G, Brunner HG, Kremer H, van der Burgt I, Crosby AH, Ion A, Jeffery S, Kalidas K, Patton MA, Kucherlapati RS, Gelb BD. Mutations in PTPN11, encoding the protein tyrosine phosphatase SHP-2, cause Noonan syndrome. Nature Genetics. 2001;29:465-468.

    Not in our library10.1038/ng772

  263. 263.

    Tonkin ET, Wang TJ, Lisgo S, Bamshad MJ, Strachan T. Nipbl, encoding a homolog of fungal scc2-type sister chromatid cohesion proteins and fly Nipped-B, is mutated in Cornelia de Lange syndrome. Nature Genetics. 2004;36:636-641.

    Not in our library10.1038/ng1363

  264. 264.

    Toydemir RM, Brassington AE, Bayrak-Toydemir P, Krakowiak PA, Jorde LB, Whitby FG, Longo N, Viskochil DH, Carey JC, Bamshad MJ. A novel mutation in FGFR3 causes camptodactyly, tall stature, and hearing loss (CATSHL) syndrome. American Journal of Human Genetics. 2006;79:935-941.

    Not in our library10.1086/508433

  265. 265.

    Trynka G, Sandor C, Han B, Xu H, Stranger BE, Liu XS, Raychaudhuri S. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nature Genetics. 2013;45:124-130.

    Not in our library10.1038/ng.2504

  266. 266.

    Tsai AC-H, Dossett CJ, Walton CS, Cramer AE, Eng PA, Nowakowska BA, Pursley AN, Stankiewicz P, Wiszniewska J, Cheung SW. Exon deletions of the ep300 and CREBBP genes in two children with Rubinstein-Taybi syndrome detected by aCGH. European Journal of Human Genetics. 2011;19:43-49.

    Not in our library10.1038/ejhg.2010.121

  267. 267.

    Tufan F, Cefle K, Türkmen S, Türkmen A, Zorba U, Dursun M, Oztürk S, Palandüz S, Ecder T, Mundlos S, Horn D. Clinical and molecular characterization of two adults with autosomal recessive robinow syndrome. American Journal of Medical Genetics Part A. 2005;136A:185-189.

    Not in our library10.1002/ajmg.a.30785

  268. 268.

    Umans BD, Battle A, Gilad Y. Where are the disease-associated eQTLs? Trends in Genetics. 2021;37:109-124.

    Not in our library10.1016/j.tig.2020.08.009

  269. 269.

    Urbut SM, Wang G, Carbonetto P, Stephens M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nature Genetics. 2019;51:187-195.

    Not in our library10.1038/s41588-018-0268-8

  270. 270.

    van Bokhoven H. Mutation of the gene encoding the ROR2 tyrosine kinase causes autosomal recessive robinow syndrome. Nat. Genet. 2000;25:423-426.

    Not in our library10.1038/78113

  271. 271.

    van der Slot AJ, Zuurmond A-M, Bardoel AFJ, Wijmenga C, Pruijs HEH, Sillence DO, Brinckmann J, Abraham DJ, Black CM, Verzijl N, DeGroot J, Hanemaaijer R, TeKoppele JM, Huizinga TWJ, Bank RA. Identification of PLOD2 as telopeptide lysyl hydroxylase, an important enzyme in fibrosis. The Journal of Biological Chemistry. 2003;278:40967-40972.

    Not in our library10.1074/jbc.m307380200

  272. 272.

    Varon R, Vissinga C, Platzer M, Cerosaletti KM, Chrzanowska KH, Saar K, Beckmann G, Seemanová E, Cooper PR, Nowak NJ, Stumm M, Weemaes CM, Gatti RA, Wilson RK, Digweed M, Rosenthal A, Sperling K, Concannon P, Reis A. Nibrin, a novel DNA double-strand break repair protein, is mutated in Nijmegen breakage syndrome. Cell. 1998;93:467-476.

    Not in our library10.1016/s0092-8674(00)81174-5

  273. 273.

    Vaxillaire M, Rouard M, Yamagata K, Oda N, Kaisaki PJ, Boriraj VV, Chevre JC, Boccio V, Cox RD, Lathrop GM, Dussoix P, Philippe J, Timsit J, Charpentier G, Velho G, Bell GI, Froguel P. Identification of nine novel mutations in the hepatocyte nuclear factor 1 alpha gene associated with maturity-onset diabetes of the young (MODY3). Human Molecular Genetics. 1997;6:583-586.

    Not in our library10.1093/hmg/6.4.583

  274. 274.

    Viñuelas J, Kaneko G, Coulon A, Vallin E, Morin V, Mejia-Pous C, Kupiec J-J, Beslon G, Gandrillon O. Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts. BMC Biology. 2013;11:15.

    Not in our library10.1186/1741-7007-11-15

  275. 275.

    Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nature Reviews. Genetics. 2012;13:227-232.

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