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Background: Genome-wide association studies have identified hundreds of loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contribute to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest. Methods: We analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell (HLC) lines (n=166), as well as in an independent collection of primary liver tissues (n=96) to perform discovery of splicing quantitative trait loci (sQTLs). Results: We observe that transcript splicing is highly cell type specific, and the genes that are differentially spliced between iPSCs and hepatocyte-like cells are enriched for metabolism pathway annotations. We identify 1384 hepatocyte-like cell sQTLs and 1455 iPSC sQTLs at false discovery rate of Conclusions: These results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.

This PSB 2023 session discusses challenges in clinical implication and application of risk prediction models, which includes but is not limited to: implementation of risk models, responsible use of polygenic risk scores (PGS), and other risk prediction strategies. We focus on the development and use of new, scalable methods for harmonizing and refining risk prediction models by incorporating genetic and non-genetic risk factors, applying new phenotyping strategies, and integrating clinical factors and biomarkers. Lastly, we will discuss innovation in expanding the utility of these prediction models to underrepresented populations. This session focuses on the overarching theme of enabling early diagnosis, and treatment and preventive measures related to complex diseases and comorbidities.

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus’s structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system’s protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).

Additional file 10 Review history

The Genotype-Tissue Expression (GTEx) project dissects how genetic variation affects gene expression and splicing. Some human genetic variants affect the amount of RNA produced and the splicing of gene transcripts, crucial steps in development and maintaining a healthy individual. However, some of these changes only occur in a small number of tissues within the body. The Genotype-Tissue Expression (GTEx) project has been expanded over time, and, looking at the final data in version 8, Aguet et al. present a deep characterization of genetic associations and gene expression and splicing in 838 individuals over 49 tissues (see the Perspective by Wilson). This large study was able to characterize the details underlying many aspects of gene expression and provides a resource with which to better understand the fundamental molecular mechanisms of how genetic variants affect gene regulation and complex traits in humans. Science, this issue p. 1318; see also p. 1298 The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues. We thank the donors and their families for their generous gifts of organ donation for transplantation and tissue donations for the GTEx research project; the Genomics Platform at the Broad Institute for data generation; J. Struewing for support and leadership of the GTEx project; M. Khan and C. Stolte for the illustrations in Fig. 1; and R. Do, D. Jordan, and M. Verbanck for providing GWAS pleiotropy scores. Funding: This work was supported by the Common Fund of the Office of the Director, U.S. National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, NIA, NIAID, and NINDS through NIH contracts HHSN261200800001E (Leidos Prime contract with NCI: A.M.S., D.E.T., N.V.R., J.A.M., L.S., M.E.B., L.Q., T.K., D.B., K.R., and A.U.), 10XS170 (NDRI: W.F.L., J.A.T., G.K., A.M., S.S., R.H., G.Wa., M.J., M.Wa., L.E.B., C.J., J.W., B.R., M.Hu., K.M., L.A.S., H.M.G., M.Mo., and L.K.B.), 10XS171 (Roswell Park Cancer Institute: B.A.F., M.T.M., E.K., B.M.G., K.D.R., and J.B.), 10X172 (Science Care Inc.), 12ST1039 (IDOX), 10ST1035 (Van Andel Institute: S.D.J., D.C.R., and D.R.V.), HHSN268201000029C (Broad Institute: F.A., G.G., K.G.A., A.V.S., X.Li., E.T., S.G., A.G., S.A., K.H.H., D.T.N., K.H., S.R.M., and J.L.N.), 5U41HG009494 (F.A., G.G., and K.G.A.), and through NIH grants R01 DA006227-17 (University of Miami Brain Bank: D.C.M. and D.A.D.), Supplement to University of Miami grant DA006227 (D.C.M. and D.A.D.), R01 MH090941 (University of Geneva), R01 MH090951 and R01 MH090937 (University of Chicago), R01 MH090936 (University of North Carolina–Chapel Hill), R01MH101814 (M.M.-A., V.W., S.B.M., R.G., E.T.D., D.G.-M., and A.V.), U01HG007593 (S.B.M.), R01MH101822 (C.D.B.), U01HG007598 (M.O. and B.E.S.), U01MH104393 (A.P.F.), extension H002371 to 5U41HG002371 (W.J.K.), as well as other funding sources: R01MH106842 (T.L., P.M., E.F., and P.J.H.), R01HL142028 (T.L., Si.Ka., and P.J.H.), R01GM122924 (T.L. and S.E.C.), R01MH107666 (H.K.I.), P30DK020595 (H.K.I.), UM1HG008901 (T.L.), R01GM124486 (T.L.), R01HG010067 (Y.Pa.), R01HG002585 (G.Wa. and M.St.), Gordon and Betty Moore Foundation GBMF 4559 (G.Wa. and M.St.), 1K99HG009916-01 (S.E.C.), R01HG006855 (Se.Ka. and R.E.H.), BIO2015-70777-P, Ministerio de Economia y Competitividad and FEDER funds (M.M.-A., V.W., R.G., and D.G.-M.), la Caixa Foundation ID 100010434 under agreement LCF/BQ/SO15/52260001 (D.G.-M.), NIH CTSA grant UL1TR002550-01 (P.M.), Marie-Skłodowska Curie fellowship H2020 Grant 706636 (S.K.-H.), R35HG010718 (E.R.G.), FPU15/03635, Ministerio de Educación, Cultura y Deporte (M.M.-A.),R01MH109905, 1R01HG010480 (A.Ba.), Searle Scholar Program (A.Ba.), R01HG008150 (S.B.M.), 5T32HG000044-22, NHGRI Institutional Training Grant in Genome Science (N.R.G.), EU IMI program (UE7-DIRECT-115317-1) (E.T.D. and A.V.), FNS funded project RNA1 (31003A_149984) (E.T.D. and A.V.), DK110919 (F.H.), F32HG009987 (F.H.), Massachusetts Lions Eye Research Fund Grant (A.R.H.), Wellcome grant WT108749/Z/15/Z (P.F.), and European Molecular Biology Laboratory (P.F. and D.Z.). Peer Reviewed "Article signat per 1 autors/es del BSC membres del THE GTEX CONSORTIUM: Marta Mele Messeguer"

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

Genome-wide association studies have identified multiple novel genomic loci associated with vascular diseases. Many of these loci are common non-coding variants that affect the expression of disease-relevant genes within coronary vascular cells. To identify such genes on a genome-wide level, we performed deep transcriptomic analysis of genotyped primary human coronary artery smooth muscle cells (HCASMCs) and coronary endothelial cells (HCAECs) from the same subjects, including splicing Quantitative Trait Loci (sQTL), allele-specific expression (ASE), and colocalization analyses. We identified sQTLs for TARS2, YAP1, CFDP1, and STAT6 in HCASMCs and HCAECs, and 233 ASE genes, a subset of which are also GTEx eGenes in arterial tissues. Colocalization of GWAS association signals for coronary artery disease (CAD), migraine, stroke and abdominal aortic aneurysm with GTEx eGenes in aorta, coronary artery and tibial artery discovered novel candidate risk genes for these diseases. At the CAD and stroke locus tagged by rs2107595 we demonstrate colocalization with expression of the proximal gene TWIST1. We show that disrupting the rs2107595 locus alters TWIST1 expression and that the risk allele has increased binding of the NOTCH signaling protein RBPJ. Finally, we provide data that TWIST1 expression influences vascular SMC phenotypes, including proliferation and calcification, as a potential mechanism supporting a role for TWIST1 in CAD., Author summary Genome-wide association studies (GWAS) have identified hundreds of genetic variants that are associated with human vascular disease including coronary artery disease. These are predominantly common single nucleotide polymorphisms (SNPs) in non-coding regions, which makes the identification of the causal genes and their underlying connection to pathophysiology challenging. Mapping of expression quantitative trait loci (eQTLs) has been performed to associate GWAS SNPs with risk genes in vascular cells and tissues. However, atherosclerotic vascular tissues contain multiple cell types. We perform deep transcriptomic profiling of genotyped human-derived vascular cells–endothelial cells and smooth muscle cells–and use splicing quantitative trait locus, allele-specific expression, and colocalization analyses to annotate genetic variants associated with vascular diseases and gain insight into their potential function in a cell-type specific manner. Based on these analyses, we identified computationally and then validate experimentally an association between the CAD risk locus rs2107595 and the gene TWIST1. We propose that the minor allele for this locus can affect transcription factor binding and provide data supporting a role for TWIST1 in modulating smooth muscle cell phenotype.

Additional file 10. Review History.

Chronic kidney disease (CKD), a condition in which the kidneys are unable to clear waste products, affects 700 million people globally. Genome-wide association studies (GWASs) have identified sequence variants for CKD; however, the biological basis of these GWAS results remains poorly understood. To address this issue, we created an expression quantitative trait loci (eQTL) atlas for the glomerular and tubular compartments of the human kidney. Through integrating the CKD GWAS with eQTL, single-cell RNA sequencing and regulatory region maps, we identified novel genes for CKD. Putative causal genes were enriched for proximal tubule expression and endolysosomal function, where DAB2, an adaptor protein in the TGF-β pathway, formed a central node. Functional experiments confirmed that reducing Dab2 expression in renal tubules protected mice from CKD. In conclusion, compartment-specific eQTL analysis is an important avenue for the identification of novel genes and cellular pathways involved in CKD development and thus potential new opportunities for its treatment.

The needle structures of type III secretion (T3S) systems are formed by the secretion and polymerization of a needle subunit protein, YscF in Yersinia pestis. A subset of T3S systems employ unique heterodimeric chaperones, YscE and YscG in Y. pestis, to prevent the polymerization of needle subunits within the bacterial cell. We demonstrate that the YscE/YscG chaperone is also required for stable YscF expression and for secretion of YscF. Overexpression of a functional maltose-binding protein (MBP)–YscG hybrid protein stabilized cytoplasmic YscF but YscF was not secreted in the absence of YscE. Furthermore, a YscE mutant protein was identified that functioned with YscG to stabilize cytosolic YscF; however, YscF was not secreted. These findings confirm a role for the YscE/YscG chaperone in YscF secretion and suggest that YscE may have a specific role in this process. Recent studies have shown that YscF deleted of its N-terminal 15 residues is still secreted and functional, suggesting that YscF may not require an N-terminal secretion signal. However, we demonstrate that YscF contains an N-terminal secretion signal and that a functional N-terminal signal is required for YscF secretion.

Gene regulation shapes the evolution of phenotypic diversity. We investigated the evolution of liver promoters and enhancers in six primate species using ChIP-seq (H3K27ac and H3K4me1) to profile cis-regulatory elements (CREs) and using RNA-seq to characterize gene expression in the same individuals. To quantify regulatory divergence, we compared CRE activity across species by testing differential ChIP-seq read depths directly measured for orthologous sequences. We show that the primate regulatory landscape is largely conserved across the lineage, with 63% of the tested human liver CREs showing similar activity across species. Conserved CRE function is associated with sequence conservation, proximity to coding genes, cell-type specificity, and transcription factor binding. Newly evolved CREs are enriched in immune response and neurodevelopmental functions. We further demonstrate that conserved CREs bind master regulators, suggesting that while CREs contribute to species adaptation to the environment, core functions remain intact. Newly evolved CREs are enriched in young transposable elements (TEs), including Long-Terminal-Repeats (LTRs) and SINE-VNTR-Alus (SVAs), that significantly affect gene expression. Conversely, only 16% of conserved CREs overlap TEs. We tested the cis-regulatory activity of 69 TE subfamilies by luciferase reporter assays, spanning all major TE classes, and showed that 95.6% of tested TEs can function as either transcriptional activators or repressors. In conclusion, we demonstrated the critical role of TEs in primate gene regulation and illustrated potential mechanisms underlying evolutionary divergence among the primate species through the noncoding genome.

Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotype-phenotype link.

© 2020 American Association for the Advancement of Science. All rights reserved. INTRODUCTION: The human genome contains tens of thousands of rare (minor allele frequency 800 genomes matched with transcriptomes across 49 tissues, we were able to study RVs that underlie extreme changes in the transcriptome. To capture the diversity of these extreme changes, we developed and integrated approaches to identify expression, allele-specific expression, and alternative splicing outliers, and characterized the RV landscape underlying each outlier signal. We demonstrate that personal genome interpretation and RV discovery is enhanced by using these signals. This approach provides a new means to integrate a richer set of functional RVs into models of genetic burden, improve disease gene identification, and enable the delivery of precision genomics.

BackgroundGenome-wide association studies have identified 150+ loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contributes to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest.Methods and ResultsWe analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell (HLC) lines (n=166), as well as in an independent collection of primary liver tissues (n=96). We observe that transcript splicing is highly cell-type specific, and the genes that are differentially spliced between iPSCs and HLCs are enriched for metabolism pathway annotations. We identify 1,381 HLC splicing quantitative trait loci (sQTLs) and 1,462 iPSC sQTLs and find that sQTLs are often shared across cell types. To evaluate the contribution of sQTLs to variation in lipid levels, we conduct colocalization analysis using lipid genome-wide association data. We identify 19 lipid-associated loci that colocalize either with an HLC expression quantitative trait locus (eQTL) or sQTL. Only one locus colocalizes with both an sQTL and eQTL, indicating that sQTLs contribute information about GWAS loci that cannot be obtained by analysis of steady-state gene expression alone.ConclusionsThese results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.SummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

Deciphering the impact of genetic variation on gene regulation is fundamental to understanding common, complex human diseases. Although histone modifications are important markers of gene regulatory regions of the genome, any specific histone modification has not been assayed in more than a few individuals in the human liver. As a result, the impacts of genetic variation that direct histone modification states in the liver are poorly understood. Here, we generate the most comprehensive genome-wide dataset of two epigenetic marks, H3K4me3 and H3K27ac, and annotate thousands of putative regulatory elements in the human liver. We integrate these findings with genome-wide gene expression data collected from the same human liver tissues and high-resolution promoter-focused chromatin interaction maps collected from human liver-derived HepG2 cells. We demonstrate widespread functional consequences of natural genetic variation on putative regulatory element activity and gene expression levels. Leveraging these extensive datasets, we fine-map a total of 77 GWAS loci that have been associated with at least one complex phenotype. Our results contribute to the repertoire of genes and regulatory mechanisms governing complex disease development and further the basic understanding of genetic and epigenetic regulation of gene expression in the human liver tissue.

Data generation is expensive in terms of both time and money. Sharing data enables the rapid replication, validation, and application of discoveries, increasing the pace and accuracy of research. As research parasites, or users of other people's data, we recognize that a strong science ecosystem requires that those who share best be recognized. We find that widely accessible benchmark datasets have provided outsized benefits, and we hope that the benefits of sharing will also begin to accrue to individual investigators who share well. Funders can enhance progress by adjusting incentives to better support data sharers, which will make their programmatic investments more effective. We note some funders who are making such efforts.

BackgroundA recent human exome-chip study on plasma lipids identified a missense mutation in theA1CF(APOBEC1 complementation factor) gene that is associated with elevated triglyceride (TG) levels, but how A1CF, an RNA binding protein, influences plasma TG is unknown.MethodsWe generatedA1cfknockout (A1cf−/−) mice and knock-in mice homozygous for the TG-associated Gly398Ser mutation (A1cfGS/GS), determined lipid phenotypes, and assessed TG physiology through measurements of clearance and secretion. We further identified A1CF’s RNA binding targets using enhanced cross-linking and immunoprecipitation sequencing of cultured HepG2 cells and investigated pathways enriched for these targets. Transcriptomic effects of A1CF deficiency were evaluated through RNA sequencing and analyses for differential expression, alternative splicing, and RNA editing.ResultsBothA1cf−/−andA1cfGS/GSmice exhibited increased fasting plasma TG, establishing that the TG phenotype is due to A1CF loss of function.In vivoTG secretion and clearance studies revealed increased TG secretion without changes in clearance inA1cf−/−mice. Increased VLDL-apoB secretion was also seen inA1cf−/−rat hepatoma cells, but no increase in apoB synthesis was observed. This phenotype was seen without significant shifts in apoB-100/apoB-48 in A1CF deficiency. To discover novel pathways for A1CF’s role in TG metabolism, we identified A1CF’s RNA binding targets, which were enriched for pathways related to proteasomal catabolism and endoplasmic reticulum (ER) stress. Indeed, proteasomal inhibition led to increased cellular stress inA1cf−/−cells, and higher expression of ER-stress protein GRP78 was observed in restingA1cf−/−cells. RNA-seq of whole livers from wild-type andA1cf−/−mice revealed that pro-inflammatory, not lipogenesis, genes were upregulated as a secondary effect of A1CF deficiency. Differential alternative splicing (AS) analysis and RNA editing analysis revealed that genes involved in cellular stress and metabolism underwent differential changes in A1CF deficiency, and top A1CF binding target proteins with relevance to intracellular stress were differentially expressed on the protein but not mRNA level, implicating multiple mechanisms by which A1CF influences TG secretion.ConclusionsThese data suggest an important role for A1CF in mediating VLDL-TG secretion through regulating intracellular stress.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

The most recent Genome-wide Association Study (GWAS) meta-analysis has reported a total of 58 genomic loci to be statistically significantly associated with genetic susceptibility to Coronary Artery Disease (CAD) (Consortium, 2015). Many of these loci also associate with other phenotypes, with the majority being lipid traits (Tada et al., 2014). But also hypertension, stroke (Dichgans et al., 2014) and migraine (Pickrell et al., 2016) appear to share genetic determinants with CAD. To functionally annotate the genomic loci harboring these association SNPs we sequenced the transcriptomes of 20 same donor human coronary artery endothelial (EC) and smooth muscle cell (SMC) lines. Deep RNA-Sequencing was used to assess Differential Gene Expression, Differential Splicing and Allele-Specific Expression. Focusing on GWAS loci for vascular phenotypes (CAD, stroke, migraine) we identified genes which display allele-specific differences in mRNA expression or splicing. We propose these genes as suitable targets for follow up studies. Consortium, C.A.D. (2015). A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nature genetics 47, 1121-1130. Tada, H., Won, H.H., Melander, O., Yang, J., Peloso, G.M., and Kathiresan, S. (2014). Multiple associated variants increase the heritability explained for plasma lipids and coronary artery disease. Circulation Cardiovascular genetics 7, 583-587. Dichgans, M., Malik, R., Konig, I.R., Rosand, J., Clarke, R., Gretarsdottir, S., Thorleifsson, G., Mitchell, B.D., Assimes, T.L., Levi, C., et al. (2014). Shared genetic susceptibility to ischemic stroke and coronary artery disease: a genome-wide analysis of common variants. Stroke; a journal of cerebral circulation 45, 24-36. Pickrell, J.K., Berisa, T., Liu, J.Z., Segurel, L., Tung, J.Y., and Hinds, D.A. (2016). Detection and interpretation of shared genetic influences on 42 human traits. Nature genetics 48, 709-717.

Summary Genome-wide association studies have struggled to identify functional genes and variants underlying complex phenotypes. We recruited a multi-ethnic cohort of healthy volunteers (n = 91) and used their tissue to generate induced pluripotent stem cells (iPSCs) and hepatocyte-like cells (HLCs) for genome-wide mapping of expression quantitative trait loci (eQTLs) and allele-specific expression (ASE). We identified many eQTL genes (eGenes) not observed in the comparably sized Genotype-Tissue Expression project's human liver cohort (n = 96). Focusing on blood lipid-associated loci, we performed massively parallel reporter assays to screen candidate functional variants and used genome-edited stem cells, CRISPR interference, and mouse modeling to establish rs2277862- CPNE1 , rs10889356- DOCK7 , rs10889356- ANGPTL3 , and rs10872142- FRK as functional SNP-gene sets. We demonstrated HLC eGenes CPNE1 , VKORC1 , UBE2L3 , and ANGPTL3 and HLC ASE gene ACAA2 to be lipid-functional genes in mouse models. These findings endorse an iPSC-based experimental framework to discover functional variants and genes contributing to complex human traits.

Gene regulation plays a critical role in the evolution of phenotypic diversity. We investigated the evolution of liver promoters and enhancers in six primate species. We performed ChlP-seq for two histone modifications and RNA-seq to profile cis-regulatory element (CRE) activity and gene expression. The primate regulatory landscape is largely conserved across the lineage. Conserved CRE function is associated with sequence conservation, proximity to coding genes, cell type specificity of CRE function, and transcription factor binding. Newly evolved CREs are enriched in immune response and neurodevelopmental functions, while conserved CREs bind master regulators. Transposable elements (TEs) are the primary source of novelty in primate gene regulation. Newly evolved CREs are enriched in young TEs that affect gene expression. However, only 17% of conserved CREs overlap a TE, suggesting that target gene expression is under strong selection. Finally, we identified specific genomic features driving the functional recruitment of newly inserted TEs.

Human genetics studies have implicated GALNT2, encoding GalNAc-T2, as a novel regulator of high-density lipoprotein cholesterol (HDL-C) metabolism, but the mechanisms relating GALNT2 to HDL-C remain unclear. We investigated the impact of homozygous GALNT2 deficiency on HDL-C in humans and mammalian models. We identified two humans homozygous for loss-of-function mutations in GALNT2 who demonstrated low HDL-C. We also found that GALNT2 loss-of-function in mice, rats, and nonhuman primates decreased HDL-C. O-glycoproteomics studies of a human GALNT2 deficient subject validated ANGPTL3 and ApoC-III as GalNAc-T2 targets. Additional glycoproteomics in rodents identified targets influencing HDL-C, including phospholipid transfer protein (PLTP). GALNT2 deficiency reduced plasma PLTP activity in humans and rodents, and in mice this was rescued by reconstitution of hepatic Galnt2. We also found that GALNT2 GWAS SNPs associated with reduced HDL-C also correlate with lower hepatic GALNT2 expression. These results posit GALNT2 as a direct modulator of HDL metabolism across mammals.

Background Pathway analysis based on Genome-Wide Association Studies (GWAS) data has become popular as a secondary analysis strategy. Although many pathway analysis tools have been developed for case-control studies, there is no tool that can use all information from raw genotypes in general nuclear families. We developed Pathway-PDT, which uses the framework of Pedigree Disequilibrium Test (PDT) for general family data, to perform pathway analysis based on raw genotypes in family-based GWAS. Results Simulation results showed that Pathway-PDT is more powerful than the p-value based method, ALIGATOR. Pathway-PDT also can be more powerful than the PLINK set-based test when analyzing general nuclear families with multiple siblings or missing parents. Additionally, Pathway-PDT has a flexible and convenient user interface, which allows users to modify their analysis parameters as well as to apply various types of gene and pathway definitions. Conclusions The Pathway-PDT method is implemented in C++ with POSIX threads and is computationally feasible for pathway analysis with large scale family GWAS datasets. The Windows binary along with Makefile and source codes for the Linux are available at https://sourceforge.net/projects/pathway-pdt/.

Beyond calculating parameter estimates to characterize the distribution of genetic features of populations (frequencies of mutations in various regions of the genome, allele frequencies, measures of Hardy-Weinberg disequilibrium), genetic epidemiology aims to identify correlations between genetic variants and phenotypic traits, with considerable emphasis placed on finding genetic variants that increase susceptibility to disease and disease-related traits. However, determining correlation alone does not suffice: genetic variants common in an isolated ethnic group with a high burden of a given disease may show relatively high correlation with disease but, as markers of ethnicity, these may not necessarily have any functional role in disease. To establish a causal relationship between genetic variants and disease (or disease-related traits), proper statistical analyses of human data must incorporate epidemiologic approaches to examining sets of families or unrelated individuals with information available on individuals' disease status or related traits.Through different analytical approaches, statistical analysis of human data can answer several important questions about the relationship between genes and disease: 1. Does the disease tend to cluster in families more than expected by chance alone? 2. Does the disease appear to follow a particular genetic model of transmission in families? 3. Do variants at a particular genetic marker tend to cosegregate with disease in families? 4. Do specific genetic markers tend to be carried more frequently by those with disease than by those without, in a given population (or across families)? The first question can be examined using studies of familial aggregation or correlation. An ancillary question: "how much of the susceptibility to disease (or variation in disease-related traits) might be accounted for by genetic factors?" is typically answered by estimating heritability, the proportion of disease susceptibility or trait variation attributable to genetics. The second question can be formally tested using pedigrees for which disease affection status or trait values are available through a modeling approach known as segregation analysis. The third question can be answered with data on pedigrees with affected members and genotype information at markers of interest, using linkage analysis. The fourth question is answerable using genotype information at markers on unrelated affected and unaffected individuals and/or families with affected and unaffected members. All of these questions can also be explored for quantitative (or continuously distributed) traits by examining variation in trait values between family members or between unrelated individuals. While each of these questions and the analytical approaches for answering them is explored extensively in subsequent chapters (heritability in Chapters 9 and 10, segregation in Chapter 12, linkage in Chapters 13-17, and association in Chapters 18-21 and 23), this chapter focuses on statistical methods to answer questions of familial aggregation.

The monocyte chemotactic protein-1 (MCP-1) is a chemokine that plays an important role in the recruitment of monocytes to M. tuberculosis infection sites, and previous studies have reported that genetic variants in MCP1 are associated with differential susceptibility to pulmonary tuberculosis (PTB). We examined eight MCP1 single nucleotide polymorphisms (SNPs) in a multi-ethnic, case-control design that included: 321 cases and 346 controls from Guinea-Bissau, 258 cases and 271 controls from The Gambia, 295 cases and 179 controls from the U.S. (African-Americans), and an additional set of 237 cases and 144 controls of European ancestry from the U.S. and Argentina. Two locus interactions were also examined for polymorphisms in MCP1 and interleukin 12B (IL12B), another gene implicated in PTB risk. Examination of previously associated MCP1 SNPs rs1024611 (-2581A/G), rs2857656 (-362G/C) and rs4586 (+900C/T) did not show evidence for association. One interaction between rs2857656 and IL12B SNP rs2288831 was observed among Africans but the effect was in the opposite direction in Guineans (OR = 1.90, p = 0.001) and Gambians (OR = 0.64, p = 0.024). Our data indicate that the effect of genetic variation within MCP1 is not clear cut and additional studies will be needed to elucidate its role in TB susceptibility.

Importance Because APOE locus variants contribute to risk of late-onset Alzheimer disease (LOAD) and to differences in age at onset (AAO), it is important to know whether other established LOAD risk loci also affect AAO in affected participants. Objectives To investigate the effects of known Alzheimer disease risk loci in modifying AAO and to estimate their cumulative effect on AAO variation using data from genome-wide association studies in the Alzheimer Disease Genetics Consortium. Design, Setting, and Participants The Alzheimer Disease Genetics Consortium comprises 14 case-control, prospective, and family-based data sets with data on 9162 participants of white race/ethnicity with Alzheimer disease occurring after age 60 years who also had complete AAO information, gathered between 1989 and 2011 at multiple sites by participating studies. Data on genotyped or imputed single-nucleotide polymorphisms most significantly associated with risk at 10 confirmed LOAD loci were examined in linear modeling of AAO, and individual data set results were combined using a random-effects, inverse variance–weighted meta-analysis approach to determine whether they contribute to variation in AAO. Aggregate effects of all risk loci on AAO were examined in a burden analysis using genotype scores weighted by risk effect sizes. Main Outcomes and Measures Age at disease onset abstracted from medical records among participants with LOAD diagnosed per standard criteria. Results Analysis confirmed the association of APOE with earlier AAO ( P = 3.3 × 10 −96 ), with associations in CR1 (rs6701713, P = 7.2 × 10 −4 ), BIN1 (rs7561528, P = 4.8 × 10 −4 ), and PICALM (rs561655, P = 2.2 × 10 −3 ) reaching statistical significance ( P APOE contributes to 3.7% of the variation in AAO ( R2 = 0.256) over baseline ( R2 = 0.221), whereas the other 9 loci together contribute to 2.2% of the variation ( R2 = 0.242). Conclusions and Relevance We confirmed an association of APOE (OMIM107741) variants with AAO among affected participants with LOAD and observed novel associations of CR1 (OMIM 120620), BIN1 (OMIM 601248), and PICALM (OMIM 603025) with AAO . In contrast to earlier hypothetical modeling, we show that the combined effects of Alzheimer disease risk variants on AAO are on the scale of, but do not exceed, the APOE effect. While the aggregate effects of risk loci on AAO may be significant, additional genetic contributions to AAO are individually likely to be small.

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes., Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.