Cross-Tissue and Tissue-Specific eQTLs: Partitioning the Heritability of a Complex Trait

Top signals from genome-wide association studies (GWASs) of type 2 diabetes (T2D) are enriched with expression quantitative trait loci (eQTLs) identified in skeletal muscle and adipose tissue. We therefore hypothesized that such eQTLs might account for a disproportionate share of the heritability estimated from all SNPs interrogated through GWASs. To test this hypothesis, we applied linear mixed models to the Wellcome Trust Case Control Consortium (WTCCC) T2D data set and to data sets representing Mexican Americans from Starr County, TX, and Mexicans from Mexico City. We estimated the proportion of phenotypic variance attributable to the additive effect of all variants interrogated in these GWASs, as well as a much smaller set of variants identified as eQTLs in human adipose tissue, skeletal muscle, and lymphoblastoid cell lines. The narrow-sense heritability explained by all interrogated SNPs in each of these data sets was substantially greater than the heritability accounted for by genome-wide-significant SNPs (∼10%); GWAS SNPs explained over 50% of phenotypic variance in the WTCCC, Starr County, and Mexico City data sets. The estimate of heritability attributable to cross-tissue eQTLs was greater in the WTCCC data set and among lean Hispanics, whereas adipose eQTLs significantly explained heritability among Hispanics with a body mass index ≥ 30. These results support an important role for regulatory variants in the genetic component of T2D susceptibility, particularly for eQTLs that elicit effects across insulin-responsive peripheral tissues.     

Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability

Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ～10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.     

Quantifying the underestimation of relative risks from genome-wide association studies

Genome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will identify alleles with larger effects on genetic risk than those currently estimated from GWAS replication studies. We show that under plausible assumptions, whilst the majority of the per-allele relative risks (RR) estimated from GWAS data will be close to the true risk at the causal variant, some could be considerable underestimates. For example, for an estimated RR in the range 1.2-1.3, there is approximately a 38% chance that it exceeds 1.4 and a 10% chance that it is over 2. We show how these probabilities can vary depending on the true effects associated with low-frequency variants and on the minor allele frequency (MAF) of the most associated SNP. We investigate the consequences of the underestimation of effect sizes for predictions of an individual's disease risk and interpret our results for the design of fine mapping experiments. Although these effects mean that the amount of heritability explained by known GWAS loci is expected to be larger than current projections, this increase is likely to explain a relatively small amount of the so-called "missing" heritability.     

Simultaneous Discovery,Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.     

A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants

In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.     

GCTA: a tool for genome-wide complex trait analysis

For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.     

A Multi-SNP Locus-Association Method Reveals a Substantial Fraction of the Missing Heritability

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.     

Insight from genome-wide association studies in rheumatoid arthritis and multiple sclerosis

Autoimmune diseases are caused by multiple genes and environmental effects. In addition, genetic contributions and the number of associated genes differ among different diseases and ethnic populations. Genome-wide association studies (GWAS) on rheumatoid arthritis (RA) and multiple sclerosis (MS) show that these diseases share many genetic factors. Recently, in addition to the major histocompatibility complex (MHC) gene, other genetic loci have been found to be associated with the risk for autoimmune diseases. This review focuses on the search for genetic variants that influence the susceptibility to RA and MS as typical autoimmune diseases and discusses the future of GWAS.     

Presence of multiple independent effects in risk loci of common complex human diseases

Many genetic loci and SNPs associated with many common complex human diseases and traits are now identified. The total genetic variance explained by these loci for a trait or disease, however, has often been very small. Much of the "missing heritability" has been revealed to be hidden in the genome among the large number of variants with small effects. Several recent studies have reported the presence of multiple independent SNPs and genetic heterogeneity in trait-associated loci. It is therefore reasonable to speculate that such a phenomenon could be common among loci known to be associated with a complex trait or disease. For testing this hypothesis, a total of 117 loci known to be associated with rheumatoid arthritis (RA), Crohn disease (CD), type 1 diabetes (T1D), or type 2 diabetes (T2D) were selected. The presence of multiple independent effects was assessed in the case-control samples genotyped by the Wellcome Trust Case Control Consortium study and imputed with SNP genotype information from the HapMap Project and the 1000 Genomes Project. Eleven loci with evidence of multiple independent effects were identified in the study, and the number was expected to increase at larger sample sizes and improved statistical power. The variance explained by the multiple effects in a locus was much higher than the variance explained by the single reported SNP effect. The results thus significantly improve our understanding of the allelic structure of these individual disease-associated loci, as well as our knowledge of the general genetic mechanisms of common complex traits and diseases.     

Methodological Considerations in Estimation of Phenotype Heritability Using Genome-Wide SNP Data,Illustrated by an Analysis of the Heritability of Height in a Large Sample of African Ancestry Adults

Height has an extremely polygenic pattern of inheritance. Genome-wide association studies (GWAS) have revealed hundreds of common variants that are associated with human height at genome-wide levels of significance. However, only a small fraction of phenotypic variation can be explained by the aggregate of these common variants. In a large study of African-American men and women (n = 14,419), we genotyped and analyzed 966,578 autosomal SNPs across the entire genome using a linear mixed model variance components approach implemented in the program GCTA (Yang et al Nat Genet 2010), and estimated an additive heritability of 44.7% (se: 3.7%) for this phenotype in a sample of evidently unrelated individuals. While this estimated value is similar to that given by Yang et al in their analyses, we remain concerned about two related issues: (1) whether in the complete absence of hidden relatedness, variance components methods have adequate power to estimate heritability when a very large number of SNPs are used in the analysis; and (2) whether estimation of heritability may be biased, in real studies, by low levels of residual hidden relatedness. We addressed the first question in a semi-analytic fashion by directly simulating the distribution of the score statistic for a test of zero heritability with and without low levels of relatedness. The second question was addressed by a very careful comparison of the behavior of estimated heritability for both observed (self-reported) height and simulated phenotypes compared to imputation R² as a function of the number of SNPs used in the analysis. These simulations help to address the important question about whether today''s GWAS SNPs will remain useful for imputing causal variants that are discovered using very large sample sizes in future studies of height, or whether the causal variants themselves will need to be genotyped de novo in order to build a prediction model that ultimately captures a large fraction of the variability of height, and by implication other complex phenotypes. Our overall conclusions are that when study sizes are quite large (5,000 or so) the additive heritability estimate for height is not apparently biased upwards using the linear mixed model; however there is evidence in our simulation that a very large number of causal variants (many thousands) each with very small effect on phenotypic variance will need to be discovered to fill the gap between the heritability explained by known versus unknown causal variants. We conclude that today''s GWAS data will remain useful in the future for causal variant prediction, but that finding the causal variants that need to be predicted may be extremely laborious.     

Finding Missing Heritability in Less Significant Loci and Allelic Heterogeneity: Genetic Variation in Human Height

Genome-wide association studies (GWAS) have identified many common variants associated with complex traits in human populations. Thus far, most reported variants have relatively small effects and explain only a small proportion of phenotypic variance, leading to the issues of ‘missing’ heritability and its explanation. Using height as an example, we examined two possible sources of missing heritability: first, variants with smaller effects whose associations with height failed to reach genome-wide significance and second, allelic heterogeneity due to the effects of multiple variants at a single locus. Using a novel analytical approach we examined allelic heterogeneity of height-associated loci selected from SNPs of different significance levels based on the summary data of the GIANT (stage 1) studies. In a sample of 1,304 individuals collected from an island population of the Adriatic coast of Croatia, we assessed the extent of height variance explained by incorporating the effects of less significant height loci and multiple effective SNPs at the same loci. Our results indicate that approximately half of the 118 loci that achieved stringent genome-wide significance (p-value<5×10⁻⁸) showed evidence of allelic heterogeneity. Additionally, including less significant loci (i.e., p-value<5×10⁻⁴) and accounting for effects of allelic heterogeneity substantially improved the variance explained in height.     

使用多因素易患性阈值模型评估双相情感障碍的遗传度缺失

为了评估双向情感障碍的遗传度缺失,文章通过查询美国国家人类基因组研究所(National Human Genome Research Institute,NHGRI)的gwascatalog目录,检索出所有已发现的双相情感障碍易感变异,使用多因素易患性阈值模型计算每个易感变异对双相情感障碍遗传度的解释度。将所有易感变异遗传度解释度求和得到双相情感障碍已知易感变异对遗传度的总解释度,使用此总解释度评估双相情感障碍的遗传度缺失。结果显示,已知双相情感障碍易感变异对双相情感障碍遗传度的合计解释度为38.34%,尚有61.66%的遗传度无法被已有易感变异解释,属于遗传度缺失。双相情感障碍38.34%的遗传度解释度较早前国外同类研究大幅度提高,表明随着新的双相情感障碍易感变异被不断发现,双相情感障碍遗传度缺失得到大幅度减小。但双相情感障碍遗传度缺失依然存在且数目较大的事实也表明双相情感障碍尚存在许多未知的分子遗传学机制有待进一步阐明。     

Autoimmune Disease Classification by Inverse Association with SNP Alleles

With multiple genome-wide association studies (GWAS) performed across autoimmune diseases, there is a great opportunity to study the homogeneity of genetic architectures across autoimmune disease. Previous approaches have been limited in the scope of their analysis and have failed to properly incorporate the direction of allele-specific disease associations for SNPs. In this work, we refine the notion of a genetic variation profile for a given disease to capture strength of association with multiple SNPs in an allele-specific fashion. We apply this method to compare genetic variation profiles of six autoimmune diseases: multiple sclerosis (MS), ankylosing spondylitis (AS), autoimmune thyroid disease (ATD), rheumatoid arthritis (RA), Crohn''s disease (CD), and type 1 diabetes (T1D), as well as five non-autoimmune diseases. We quantify pair-wise relationships between these diseases and find two broad clusters of autoimmune disease where SNPs that make an individual susceptible to one class of autoimmune disease also protect from diseases in the other autoimmune class. We find that RA and AS form one such class, and MS and ATD another. We identify specific SNPs and genes with opposite risk profiles for these two classes. We furthermore explore individual SNPs that play an important role in defining similarities and differences between disease pairs. We present a novel, systematic, cross-platform approach to identify allele-specific relationships between disease pairs based on genetic variation as well as the individual SNPs which drive the relationships. While recognizing similarities between diseases might lead to identifying novel treatment options, detecting differences between diseases previously thought to be similar may point to key novel disease-specific genes and pathways.     

Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms

Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.     

Genome-wide association studies in multiple sclerosis: lessons and future prospects

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease with complex aetiology. A haplotype within the major histocompatibility region is the major risk factor for MS, but despite clear evidence for a genetic component additional risk variants were not identified until the recent advent of genome-wide association studies (GWAS). At present, 10 GWAS have been conducted in MS, and together with follow-up studies these have confirmed 16 loci with genome-wide significance. Many of these common risk variants are located at or near genes with central immunological functions and the majority are associated with other autoimmune diseases. However, evidence from pathway analyses on more modestly associated variants also supports the involvement of neurological genes. Although the mechanisms by which the associated variants exert their effects are still poorly understood, some have been shown to correlate with expression of nearby genes. Further studies are required to define the functionally relevant variants in the identified regions and to investigate their effects at the molecular and cellular level. Finally, many genetic risk variants for MS remain to be identified. In order to expose some of the loci with more modest effects, a GWAS in nearly 10,000 MS patients has recently been completed.     

Mining the LIPG allelic spectrum reveals the contribution of rare and common regulatory variants to HDL cholesterol

Genome-wide association studies (GWAS) have successfully identified loci associated with quantitative traits, such as blood lipids. Deep resequencing studies are being utilized to catalogue the allelic spectrum at GWAS loci. The goal of these studies is to identify causative variants and missing heritability, including heritability due to low frequency and rare alleles with large phenotypic impact. Whereas rare variant efforts have primarily focused on nonsynonymous coding variants, we hypothesized that noncoding variants in these loci are also functionally important. Using the HDL-C gene LIPG as an example, we explored the effect of regulatory variants identified through resequencing of subjects at HDL-C extremes on gene expression, protein levels, and phenotype. Resequencing a portion of the LIPG promoter and 5' UTR in human subjects with extreme HDL-C, we identified several rare variants in individuals from both extremes. Luciferase reporter assays were used to measure the effect of these rare variants on LIPG expression. Variants conferring opposing effects on gene expression were enriched in opposite extremes of the phenotypic distribution. Minor alleles of a common regulatory haplotype and noncoding GWAS SNPs were associated with reduced plasma levels of the LIPG gene product endothelial lipase (EL), consistent with its role in HDL-C catabolism. Additionally, we found that a common nonfunctional coding variant associated with HDL-C (rs2000813) is in linkage disequilibrium with a 5' UTR variant (rs34474737) that decreases LIPG promoter activity. We attribute the gene regulatory role of rs34474737 to the observed association of the coding variant with plasma EL levels and HDL-C. Taken together, the findings show that both rare and common noncoding regulatory variants are important contributors to the allelic spectrum in complex trait loci.     

Multiple common susceptibility variants near BMP pathway loci GREM1, BMP4, and BMP2 explain part of the missing heritability of colorectal cancer

Genome-wide association studies (GWAS) have identified 14 tagging single nucleotide polymorphisms (tagSNPs) that are associated with the risk of colorectal cancer (CRC), and several of these tagSNPs are near bone morphogenetic protein (BMP) pathway loci. The penalty of multiple testing implicit in GWAS increases the attraction of complementary approaches for disease gene discovery, including candidate gene- or pathway-based analyses. The strongest candidate loci for additional predisposition SNPs are arguably those already known both to have functional relevance and to be involved in disease risk. To investigate this proposition, we searched for novel CRC susceptibility variants close to the BMP pathway genes GREM1 (15q13.3), BMP4 (14q22.2), and BMP2 (20p12.3) using sample sets totalling 24,910 CRC cases and 26,275 controls. We identified new, independent CRC predisposition SNPs close to BMP4 (rs1957636, P = 3.93×10(-10)) and BMP2 (rs4813802, P = 4.65×10(-11)). Near GREM1, we found using fine-mapping that the previously-identified association between tagSNP rs4779584 and CRC actually resulted from two independent signals represented by rs16969681 (P = 5.33×10(-8)) and rs11632715 (P = 2.30×10(-10)). As low-penetrance predisposition variants become harder to identify-owing to small effect sizes and/or low risk allele frequencies-approaches based on informed candidate gene selection may become increasingly attractive. Our data emphasise that genetic fine-mapping studies can deconvolute associations that have arisen owing to independent correlation of a tagSNP with more than one functional SNP, thus explaining some of the apparently missing heritability of common diseases.     

Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture

The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.