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In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying alternative pathways to the traits under investigation. Here, we develop MR-TRYX, a framework that exploits horizontal pleiotropy to discover putative risk factors for disease. We begin by detecting outliers in a single exposure–outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across hundreds of complete GWAS summary datasets to systematically identify other (candidate) traits that associate with the outliers. We develop a multi-trait pleiotropy model of the heterogeneity in the exposure–outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways are uncovered with already established causal effects, validating the approach, but also alternative putative causal pathways. Adjustment for pleiotropic pathways reduces the heterogeneity across the analyses., In Mendelian randomization (MR) studies, one typically selects SNPs as instrumental variables that do not directly affect the outcome to avoid violation of MR assumptions. Here, Cho et al. present a framework, MR-TRYX, that leverages knowledge of such outliers of horizontal pleiotropy to identify putative causal relationships between exposure and outcome.

Aging is a key risk factor for chronic diseases of the elderly. MicroRNAs regulate post-transcriptional gene silencing through base-pair binding on their target mRNAs. We identified nonlinear changes in age-related microRNAs by analyzing whole blood from 1334 healthy individuals. We observed a larger influence of the age as compared to the sex and provide evidence for a shift to the 5’ mature form of miRNAs in healthy aging. The addition of 3059 diseased patients uncovered pan-disease and disease-specific alterations in aging profiles. Disease biomarker sets for all diseases were different between young and old patients. Computational deconvolution of whole-blood miRNAs into blood cell types suggests that cell intrinsic gene expression changes may impart greater significance than cell abundance changes to the whole blood miRNA profile. Altogether, these data provide a foundation for understanding the relationship between healthy aging and disease, and for the development of age-specific disease biomarkers., Aging is a key risk factor for chronic diseases of the elderly. Here the authors perform large-scale miRNA profiling of blood from individuals of a range of ages and show that common diseases alter the physiological age-related blood microRNA profile.

Our paper explores the value-laden and epistemic resources that scientists working in epigenetics and developmental programming of health and disease (DOHaD) mobilise to produce scientific representations of pregnancy and parenthood, which in turn imagine norms, values, and responsibilities for the protection of future generations. In order to do so, we first describe the place of questions regarding the relative weight of paternal and maternal influences on the health of the offspring in the discursive formalisation of this research in scientific publications. This enables us to identify the mutual constitution of 'prototypes' (i.e. experimental designs, settings, techniques) and 'stereotypes' (i.e. social meanings, beliefs, norms and values) of parental roles in DOHaD and epigenetic biomedical sciences, by means of a specific gendered figuration of paternal influences: the 'father-as-sperm'. Second, and drawing from a set of interviews (N = 15), we describe a tension between this dominant, objectifying molecular discourse and the perspective of individual scientists. The situated perspective of individual researchers provides in fact evidence for a conflictual (moral and epistemic) economy of gendered engagements with parental figurations in DOHaD and epigenetic research, and consequently suggests a more fine-grained, as well as conflictual web of socio-political positioning of this 'knowledge' in its societal circulation.

Neutrophil granulocytes are critical mediators of innate immunity and tissue regeneration. Rare diseases of neutrophil granulocytes may affect their differentiation and/or functions. However, there are very few validated diagnostic tests assessing the functions of neutrophil granulocytes in these diseases. Here, we set out to probe omics analysis as a novel diagnostic platform for patients with defective differentiation and function of neutrophil granulocytes. We analyzed highly purified neutrophil granulocytes from 68 healthy individuals and 16 patients with rare monogenic diseases. Cells were isolated from fresh venous blood (purity >99%) and used to create a spectral library covering almost 8000 proteins using strong cation exchange fractionation. Patient neutrophil samples were then analyzed by data-independent acquisition proteomics, quantifying 4154 proteins in each sample. Neutrophils with mutations in the neutrophil elastase gene ELANE, showed large proteome changes that suggest these mutations may affect maturation of neutrophil granulocytes and initiate misfolded protein response and cellular stress mechanisms. In contrast, only few proteins changed in patients with leukocyte adhesion deficiency (LAD) and chronic granulomatous disease (CGD). Strikingly, neutrophil transcriptome analysis showed no correlation with its proteome. In case of two patients with undetermined genetic causes, proteome analysis guided the targeted genetic diagnostics and uncovered the underlying genomic mutations. Data-independent acquisition proteomics may help to define novel pathomechanisms in neutrophil diseases and provide a clinically useful diagnostic dimension.

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Loci discovered by genome-wide association studies predominantly map outside protein-coding genes. The interpretation of the functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking by which to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages genome-wide association studies’ findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding not offered by current methods. We further assess enrichment of genome-wide association studies for 19 traits within Encyclopedia of DNA Elements- and Roadmap-derived regulatory regions. We characterize unique enrichment patterns for traits and annotations driving novel biological insights. The method is implemented in standalone software and an R package, to facilitate its application by the research community.

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer – but not other cancers – explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter)., eLife digest Ageing happens to us all, and as the cabaret singer Maurice Chevalier pointed out, "old age is not that bad when you consider the alternative". Yet, the growing ageing population of most developed countries presents challenges to healthcare systems and government finances. For many older people, long periods of ill health are part of the end of life, and so a better understanding of ageing could offer the opportunity to prolong healthy living into old age. Ageing is complex and takes a long time to study – a lifetime in fact. This makes it difficult to discern its causes, among the countless possibilities based on an individual’s genes, behaviour or environment. While thousands of regions in an individual’s genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle. Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average. The DNA of over 500,000 people was read to reveal the specific ‘genetic fingerprints’ of each participant. Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan. Five of these regions were new and had not been linked to lifespan before. Across the twelve as a whole several were known to be involved in Alzheimer’s disease, smoking-related cancer or heart disease. Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average. Many factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone. Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease.

The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites., We are grateful to all families and participants who took part in these studies. We also acknowledge and appreciate the unique efforts of the research teams and practitioners contributing to the collection of this wealth of data. C. M. M. is supported by funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 721567. J. F. F. has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 633595 (DynaHEALTH) and 733206 (LifeCycle). R. M. F. and R. N. B. are supported by Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150). D. L. C. is funded by the American Diabetes Association Grant 1-17-PDF-077. D. O. M-K. was supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023). N. M. W. is supported by an Australian National Health and Medical Research Council Early Career Fellowship (APP1104818). T. S. A. was partially funded by the Gene-Diet Interactions in Obesity (GENDINOB) project on behalf of GOYA male cohort data management and analyses and acknowledges the same. S. D. was supported by National Institute of Health Research. T. M. F. is supported by the European Research Council grant: 323195 SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk).H.H.is funded by The Children's Hospital of Philadelphia Endowed Chair in Genomic Research. A. T. H. is supported by the Wellcome Trust Senior Investigator Awards (WT098395), National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0611-10219). J. Hebebrand received grants from German Research Society, German Ministry of Education and Research. M-F. H. is currently supported by an American Diabetes Association (ADA) Pathway Program Accelerator Early Investigator Award (1-15-ACE-26). S. J. is supported by Helse Vest no. 23929, Bergen Forskningsstiftelse and KG Jebsen Foundation and University of Bergen. J. K. has been supported by the Academy of Finland Research Professor program (grants 265240 & 263278). J. P. K. is funded by a University of Queensland Development Fellowship (UQFEL1718945). H. L. has served as a speaker for Eli-Lilly and Shire and has received research grants from Shire; all outside the submitted work. C. M. L is supported by the Li Ka Shing Foundation, WT-SSI/John Fell funds and by the NIHR Biomedical Research Centre, Oxford, by Widenlife and NIH (5P50HD028138-27). S. E. M. was funded by an NHMRC Senior Reseach Fellowship (APP1103623). K. Panoutsopoulou is funded by a career development fellowship (grant 20308) and by the Wellcome Trust (WT098051). C. P. at UCL Institute of Child Health, with support from the National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London. I. P. was funded in part by the Wellcome Trust (WT205915), and the European Union's Horizon 2020 research, the European Union FP7-IDEAS-ERC Advanced Grant (GEPIDIAB, ERC-AG -ERC-294785), and innovation programme (DYNAhealth, H2020-PHC-2014-633595). R. C. R. is supported by CRUK (grant number C18281/A19169). J. G. S. is supported by an NHMRC Practitioner Fellowship Grant (APP1105807). J. T.; r is funded by the European Regional Development Fund (ERDF), the European Social Fund (ESF), Convergence Programme for Cornwall and the Isles of Scilly and the Diabetes Research and Wellness Foundation Non-Clinical Fellowship. N. V-T. is funded by a pre-doctoral grant from the Agencia de Gestio d'Ajuts Universitaris i de Recerca (AGAUR) (2015 FI_B 00636), Generalitat de Catalunya. T. G. M. V was supported by ZonMW (TOP 40-00812-98-11010. J. F. W. is supported by the MRC Human Genetics Unit quinquennial programme QTL in Health and Disease. H. Y. is funded by Diabetes UK RD Lawrence fellowship (grant: 17/0005594). M. H. Z was supported by BBMRI-NL (CP2013-50). E. Z. is supported by the Wellcome Trust (098051). B. F. is supported by Novo Nordisk Foundation (12955) and an Oak Foundation Fellowship. S. S. and M-R. J. have received funding from the European Union's Horizon 2020 research and innovation programme [under grant agreement No 633595] for the DynaHEALTH action. P. R. N. was supported by the European Research Council (ERC), University of Bergen, KG Jebsen and Helse Vest. G. D. S. works within the MRC Integrative Epidemiology Unit at the University of Bristol (MC_UU_12013/1). D. A. L was supported by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement (Grant number 669545; DevelopObese), US National Institute of Health (grant: R01 DK10324), the UK Medical Research Council (grant: MC_UU_00011/6), Wellcome Trust GWAS grant (WT088806), an NIHR Senior Investigator Award (NF-SI-0611-10196) and the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. L. Paternoster was supported by the UK Medical Research Council Unit grants MC_UU_12013_5. N. J. T. is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 102215/2/13/2), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC) and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). V.W.V.J. received an additional grant from the Netherlands Organization for Health Research and Development (NWO, ZonMw-VIDI 016.136.361), a European Research Council Consolidator Grant (ERC-2014-CoG-648916) and funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 633595 (DynaHEALTH) and 733206 (LifeCycle). D. M. E. is funded by the UK Medical Research Council Unit grant MC_UU_12013_4, Australian Research Council Future Fellowship (FT130101709) and a NHMRC Senior Research Fellowship (GNT1137714). S. F. A. G. is funded by the Daniel B. Burke Endowed Chair for Diabetes Research and R01 HD056465. D. I. B. is supported by Spinozapremie (NWO-56-464-14192) and the Royal Netherlands Academy of Science Professor Award (PAH/6635) to DIB. M. I. M. is a Wellcome Senior Investgator and NIHR Senior Investigator supported by the Wellcome (090532, 098381, 203141), NIHR (NF-SI-0617-10090) and the US National Institute of Health (grant: R01 DK10324), the UK Medical Research Council (grant: MCiabetes UK RD Lawrence fellowship (grant: 17/0005594). M. H. Z was supported by BBMRI-NLNIHR Biomedical Research Centre, Oxford. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.

Health risk factors such as body mass index (BMI) and serum cholesterol are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. We develop and apply a method (called GSMR) that performs a multi-SNP Mendelian randomization analysis using summary-level data from genome-wide association studies to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height, and years of schooling (EduYears) with common diseases (sample sizes of up to 405,072). We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimer’s disease, and bidirectional associations with opposite effects (e.g., higher BMI increases the risk of T2D but the effect of T2D on BMI is negative)., Genetic methods are useful to test whether risk factors are causal for or consequence of disease. Here, Zhu et al. develop a generalized summary-based Mendelian Randomization (GSMR) method which uses summary-level data from GWAS to test for causal associations of health risk factors with common diseases.

Chromosome conformation capture methods have identified subchromosomal structures of higher-order chromatin interactions called topologically associated domains (TADs) that are separated from each other by boundary regions1, 2. By subdividing the genome into discrete regulatory units, TADs restrict the contacts that enhancers establish with their target genes3, 4, 5. However, the mechanisms that underlie partitioning of the genome into TADs remain poorly understood. Here we show by chromosome conformation capture (capture Hi-C and 4C-seq methods) that genomic duplications in patient cells and genetically modified mice can result in the formation of new chromatin domains (neo-TADs) and that this process determines their molecular pathology. Duplications of non-coding DNA within the mouse Sox9 TAD (intra-TAD) that cause female to male sex reversal in humans6, showed increased contact of the duplicated regions within the TAD, but no change in the overall TAD structure. In contrast, overlapping duplications that extended over the next boundary into the neighbouring TAD (inter-TAD), resulted in the formation of a new chromatin domain (neo-TAD) that was isolated from the rest of the genome. As a consequence of this insulation, inter-TAD duplications had no phenotypic effect. However, incorporation of the next flanking gene, Kcnj2, in the neo-TAD resulted in ectopic contacts of Kcnj2 with the duplicated part of the Sox9 regulatory region, consecutive misexpression of Kcnj2, and a limb malformation phenotype. Our findings provide evidence that TADs are genomic regulatory units with a high degree of internal stability that can be sculptured by structural genomic variations. This process is important for the interpretation of copy number variations, as these variations are routinely detected in diagnostic tests for genetic disease and cancer. This finding also has relevance in an evolutionary setting because copy-number differences are thought to have a crucial role in the evolution of genome complexity.

Defective Gene Detective Identifying genes that give rise to diseases is one of the major goals of sequencing human genomes. However, putative loss-of-function genes, which are often some of the first identified targets of genome and exome sequencing, have often turned out to be sequencing errors rather than true genetic variants. In order to identify the true scope of loss-of-function genes within the human genome, MacArthur et al. (p. 823 ; see the Perspective by Quintana-Murci ) extensively validated the genomes from the 1000 Genomes Project, as well as an additional European individual, and found that the average person has about 100 true loss-of-function alleles of which approximately 20 have two copies within an individual. Because many known disease-causing genes were identified in “normal” individuals, the process of clinical sequencing needs to reassess how to identify likely causative alleles.

Understanding the influence of genetics on the molecular mechanisms underpinning human phenotypic diversity is fundamental to being able to predict health outcomes and treat disease. To interrogate the role of genetics on cellular state and function, gene expression has been extensively used. Past and present studies have highlighted important patterns of heritability, population differentiation and tissue-specificity in gene expression. Current and future studies are taking advantage of systems biology-based approaches and advances in sequencing technology: new methodology aims to translate regulatory networks to enrich pathways responsible for disease etiology and 2nd generation sequencing now offers single-molecular resolution of the transcriptome providing unprecedented information on the structural and genetic characteristics of gene expression. Such advances are leading to a future where rich cellular phenotypes will facilitate understanding of the transmission of genetic effect from the gene to organism.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. The genetic contribution to the variation in human lifespan is ∼ 25%. Despite the large number of identified disease-susceptibility loci, it is not known which loci influence population mortality. We performed a genome-wide association meta-analysis of 7729 long-lived individuals of European descent (≥ 85 years) and 16 121 younger controls (

Approximately 1.1 billion people currently live in countries where consanguineous marriages are customary, and among them one in every three marriages is between cousins. Opinions diverge between those warning of the possible health risks to offspring and others who highlight the social benefits of consanguineous marriages. A consanguinity study group of international experts and counselors met at the Geneva International Consanguinity Workshop from May 3 2010, to May 7, 2010, to discuss the known and presumptive risks and benefits of close kin marriages and to identify important future areas for research on consanguinity. The group highlighted the importance of evidence-based counseling recommendations for consanguineous marriages and of undertaking both genomic and social research in defining the various influences and outcomes of consanguinity. Technological advances in rapid high-throughput genome sequencing and for the identification of copy number variants by comparative genomic hybridization offer a significant opportunity to identify genotype-phenotype correlations focusing on autozygosity, the hallmark of consanguinity. The ongoing strong preferential culture of close kin marriages in many societies, and among migrant communities in Western countries, merits an equivalently detailed assessment of the social and genetic benefits of consanguinity in future studies. Genet Med 2011:13(9):841-847.

Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation likely to play a role in phenotypic diversity and evolution. Much effort has been put into the identification and mapping of regions that vary in copy number among seemingly normal individuals in humans and a number of model organisms, using bioinformatics or hybridization-based methods. These have allowed uncovering associations between copy number changes and complex diseases in whole-genome association studies, as well as identify new genomic disorders. At the genome-wide scale, however, the functional impact of CNV remains poorly studied. Here we review the current catalogs of CNVs, their association with diseases and how they link genotype and phenotype. We describe initial evidence which revealed that genes in CNV regions are expressed at lower and more variable levels than genes mapping elsewhere, and also that CNV not only affects the expression of genes varying in copy number, but also have a global influence on the transcriptome. Further studies are warranted for complete cataloguing and fine mapping of CNVs, as well as to elucidate the different mechanisms by which they influence gene expression.

The enormous variation in human lifespan is in part due to a myriad of sequence variants, only a few of which have been revealed to date. Since many life-shortening events are related to diseases, we developed a Mendelian randomization-based method combining 58 disease-related GWA studies to derive longevity priors for all HapMap SNPs. A Bayesian association scan, informed by these priors, for parental age of death in the UK Biobank study (n=116,279) revealed 16 independent SNPs with significant Bayes factor at a 5% false discovery rate (FDR). Eleven of them replicate (5% FDR) in five independent longevity studies combined; all but three are depleted of the life-shortening alleles in older Biobank participants. Further analysis revealed that brain expression levels of nearby genes (RBM6, SULT1A1 and CHRNA5) might be causally implicated in longevity. Gene expression and caloric restriction experiments in model organisms confirm the conserved role for RBM6 and SULT1A1 in modulating lifespan., Along with various environmental factors, a complex genetic architecture influences human lifespan. Here, McDaid and colleagues reveal novel loci associated with human lifespan and linked biomarkers by Bayesian association scan.

Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.