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      Quantifying genetic effects on disease mediated by assayed gene expression levels

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          Abstract

          Disease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs), but it remains unclear whether this overlap is driven by gene expression levels mediating genetic effects on disease. Here we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis-genetic component of gene expression levels. We applied MESC to GWAS summary statistics for 42 traits (average N = 323K) and cis-eQTL summary statistics for 48 tissues from the Genotype-Tissue Expression (GTEx) consortium. Averaging across traits, only 11±2% of heritability was mediated by assayed gene expression levels. Expression-mediated heritability was enriched in genes with evidence of selective constraint and genes with disease-appropriate annotations. Our results demonstrate that assayed bulk-tissue eQTLs, though disease relevant, cannot explain the majority of disease heritability.

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          Most cited references86

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            PLINK: a tool set for whole-genome association and population-based linkage analyses.

            Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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              Is Open Access

              A global reference for human genetic variation

              The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                11 April 2020
                18 May 2020
                June 2020
                18 November 2020
                : 52
                : 6
                : 626-633
                Affiliations
                [1 ]Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, Massachusetts, USA
                [2 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
                [3 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
                [4 ]Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
                [5 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
                [6 ]Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts, USA
                Author notes

                Author Contributions

                D.W.Y, L.J.O, A.L.P, and A.G conceived of the project. D.W.Y and A.G. designed experiments. D.W.Y performed the experiments and analyzed the data. D.W.Y. and A.G. wrote the manuscript with input from L.J.O. and A.L.P.

                [* ]Correspondence should be addressed to D.W.Y. ( douglasyao@ 123456g.harvard.edu ) or A.G. ( alexander_gusev@ 123456dfci.harvard.edu )
                Article
                NIHMS1583318
                10.1038/s41588-020-0625-2
                7276299
                32424349
                f65fbe73-130e-4de7-81b6-5d7ca1be9199

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                Genetics
                Genetics

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