Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.     

Modifier effects between regulatory and protein-coding variation

Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.     

Treatment- and population-dependent activity patterns of behavioral and expression QTLs

Genetic control of gene expression and higher-order phenotypes is almost invariably dependent on environment and experimental conditions. We use two families of recombinant inbred strains of mice (LXS and BXD) to study treatment- and genotype-dependent control of hippocampal gene expression and behavioral phenotypes. We analyzed responses to all combinations of two experimental perturbations, ethanol and restraint stress, in both families, allowing for comparisons across 8 combinations of treatment and population. We introduce the concept of QTL activity patterns to characterize how associations between genomic loci and traits vary across treatments. We identified several significant behavioral QTLs and many expression QTLs (eQTLs). The behavioral QTLs are highly dependent on treatment and population. We classified eQTLs into three groups: cis-eQTLs (expression variation that maps to within 5 Mb of the cognate gene), syntenic trans-eQTLs (the gene and the QTL are on the same chromosome but not within 5 Mb), and non-syntenic trans-eQTLs (the gene and the QTL are on different chromosomes). We found that most non-syntenic trans-eQTLs were treatment-specific whereas both classes of syntenic eQTLs were more conserved across treatments. We also found there was a correlation between regions along the genome enriched for eQTLs and SNPs that were conserved across the LXS and BXD families. Genes with eQTLs that co-localized with the behavioral QTLs and displayed similar QTL activity patterns were identified as potential candidate genes associated with the phenotypes, yielding identification of novel genes as well as genes that have been previously associated with responses to ethanol.     

Aberrant Gene Expression in Humans

Gene expression as an intermediate molecular phenotype has been a focus of research interest. In particular, studies of expression quantitative trait loci (eQTL) have offered promise for understanding gene regulation through the discovery of genetic variants that explain variation in gene expression levels. Existing eQTL methods are designed for assessing the effects of common variants, but not rare variants. Here, we address the problem by establishing a novel analytical framework for evaluating the effects of rare or private variants on gene expression. Our method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers. Using population-scale mRNA sequencing data, we identify outlier individuals using a multivariate approach. We find that outlier individuals are more readily detected with respect to gene sets that include genes involved in cellular regulation and signal transduction, and less likely to be detected with respect to the gene sets with genes involved in metabolic pathways and other fundamental molecular functions. Analysis of polymorphic data suggests that private SNPs of outlier individuals are enriched in the enhancer and promoter regions of corresponding aberrantly-expressed genes, suggesting a specific regulatory role of private SNPs, while the commonly-occurring regulatory genetic variants (i.e., eQTL SNPs) show little evidence of involvement. Additional data suggest that non-genetic factors may also underlie aberrant gene expression. Taken together, our findings advance a novel viewpoint relevant to situations wherein common eQTLs fail to predict gene expression when heritable, rare inter-individual variation exists. The analytical framework we describe, taking into consideration the reality of differential phenotypic robustness, may be valuable for investigating complex traits and conditions.     

Genetic variation and genetic structure of five Chinese indigenous pig populations in Jiangsu Province revealed by sequencing data

In this study, we investigated the genetic variants, including SNPs and indels (short insertions or deletions, less than 50 bp in length), in the genomes and genetic structures of five pig populations (in the northern Taihu Lake region, Jiangsu Province) using the genotyping by genome reducing and sequencing (GGRS) approach. A total of 581 million good reads with an average depth of 11× and an average coverage of 2.16% were used to call variants. In general, 202 106 SNPs and 34 415 indels were obtained, of which 2690 SNPs and 224 indels were capable of inducing protein‐coding changes. The genes containing these variants were extracted for functional annotation. The results of gene enrichment analysis revealed that the SNPs under investigation may be associated with reproduction, disease resistance, meat quality and adipose tissue traits, whereas the indels were associated mainly with adipose tissue and disease. Analysis of the genetic structure showed that each population displayed comparable, large differentiations from the others, indicating their uniqueness. In conclusion, the results of our study provide the first genomic overview of the genetic variants and population structures of five Chinese indigenous pig populations.     

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.     

Epialleles via DNA methylation: consequences for plant evolution

In plants, naturally occurring methylation of genes can affect the level of gene expression. Variation among individuals in the degree of methylation of a gene, termed epialleles, produces novel phenotypes that are heritable across generations. To date, ecologically important genes with methylated epialleles have been found to affect floral shape, vegetative and seed pigmentation, pathogen resistance and development in plants. Currently, the extent to which epiallelic variation is an important common contributor to phenotypic variation in natural plant populations and its fitness consequences are not known. Because epiallele phenotypes can have identical underlying DNA sequences, response to selection on these phenotypes is likely to differ from expectations based on traditional models of microevolution. Research is needed to understand the role of epialleles in natural plant populations. Recent advances in molecular genetic techniques could enable population biologists to screen for epiallelic variants within plant populations and disentangle epigenetic from more standard genetic sources of phenotypic variance, such as additive genetic variance, dominance variance, epistasis and maternal genetic effects.     

Identifying genetic variants regulating MGMT gene expression - A study in monozygotic Danish twins

Identifying genetic factors affecting the regulation of the O-6-Methylguanine-DNA Methyltransferase (MGMT) gene and estimating the genetic contribution of the MGMT gene through within-pair correlation in monozygotic twin pairs is of particular importance in various types of cancer such as glioblastoma. We used gene expression data in whole blood from 448 monozygotic twins from the Middle Age Danish Twins (MADT) study to investigate genetic regulation of the MGMT gene by performing a genome-wide association study (GWAS) of the variation in MGMT expression. Additionally, we estimated within-pair dependence measures of the expression values looking for the genetic influence of significant identified genes. We identified 243 single nucleotide polymorphisms (SNPs) significantly (p < 5e-8) associated with expression of MGMT, all located on chromosome 10 near the MGMT gene. Of the 243 SNPs, 7 are novel cis-eQTLs. By further looking into the suggestively significant SNPs (increasing cutoff to p = 1e-6), we identified 11 suggestive trans-eQTLs located on chromosome 17. These variants were in or proximal to a total of seven genes, which may regulate MGMT expression. The within-pair correlation of the expression of MGMT, TRIM37, and SEPT4 provided the upper bound genetic influence of these genes. Overall, identifying cis- or trans-acting genetic variations regulating the MGMT gene can pave the way for a better understanding of the MGMT gene function and ultimately in understanding the patient's sensitivity to therapeutic alkylating agents.     

Variants in the CNR1 and the FAAH Genes and Adiposity Traits in the Community

Pharmacologic blockade of the endocannabinoid receptor 1 leads to weight loss and an improved metabolic risk profile in overweight and obese individuals. We hypothesize that common genetic variants in the CNR1 (encoding endocannabinoid receptor 1) and FAAH genes (encoding fatty acid amide hydrolase, a key enzyme hydrolyzing endocannabinoids) are associated with adiposity traits. We genotyped 18 single‐nucleotide polymorphisms (SNPs) in the CNR1 gene and 9 SNPs in the FAAH gene in 2,415 Framingham Offspring Study participants (mean age 61 ± 10 years; 52.6% women; mean BMI 28.2 ± 5.4 kg/m²; 30.3% obese) and studied them for association with cross‐sectional and longitudinal measures of adiposity (BMI, waist circumference, change over time in BMI and waist circumference, visceral and subcutaneous adipose tissue) using linear mixed‐effect models. The selected SNPs captured 85% (r² = 0.8) of the common variation (minor allele frequency >5%) at the CNR1 locus and 96% (r² = 0.8) of the common variation at the FAAH locus (defined as the genomic segment containing the gene +20 kb upstream and +10 kb downstream). After correction for multiple testing, none of the SNPs in the CNR1 gene or in the FAAH gene displayed statistical evidence for association with BMI, waist circumference, and visceral adipose tissue or subcutaneous adipose tissue (all P > 0.18). Despite comprehensive SNP mapping across the genes and their regulatory regions in a large unselected sample, we failed to find evidence for an association of common variants in the CNR1 and FAAH genes with measures of adiposity in our community‐based sample.     

Genetics of sputum gene expression in chronic obstructive pulmonary disease

Previous expression quantitative trait loci (eQTL) studies have performed genetic association studies for gene expression, but most of these studies examined lymphoblastoid cell lines from non-diseased individuals. We examined the genetics of gene expression in a relevant disease tissue from chronic obstructive pulmonary disease (COPD) patients to identify functional effects of known susceptibility genes and to find novel disease genes. By combining gene expression profiling on induced sputum samples from 131 COPD cases from the ECLIPSE Study with genomewide single nucleotide polymorphism (SNP) data, we found 4315 significant cis-eQTL SNP-probe set associations (3309 unique SNPs). The 3309 SNPs were tested for association with COPD in a genomewide association study (GWAS) dataset, which included 2940 COPD cases and 1380 controls. Adjusting for 3309 tests (p<1.5e-5), the two SNPs which were significantly associated with COPD were located in two separate genes in a known COPD locus on chromosome 15: CHRNA5 and IREB2. Detailed analysis of chromosome 15 demonstrated additional eQTLs for IREB2 mapping to that gene. eQTL SNPs for CHRNA5 mapped to multiple linkage disequilibrium (LD) bins. The eQTLs for IREB2 and CHRNA5 were not in LD. Seventy-four additional eQTL SNPs were associated with COPD at p<0.01. These were genotyped in two COPD populations, finding replicated associations with a SNP in PSORS1C1, in the HLA-C region on chromosome 6. Integrative analysis of GWAS and gene expression data from relevant tissue from diseased subjects has located potential functional variants in two known COPD genes and has identified a novel COPD susceptibility locus.     

Combinations of Susceptibility Genes Are Associated with Higher Risk for Multiple Sclerosis and Imply Disease Course Specificity

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that predominantly affects young adults. The genetic contributions to this multifactorial disease were underscored by a genome wide association study (GWAS) conducted by the International Multiple Sclerosis Genetic Consortium in a multinational cohort prompting the discovery of 57 non-MHC MS-associated common genetic variants. Hitherto, few of these newly reported variants have been replicated in larger independent patient cohorts. We genotyped a cohort of 1033 MS patients and 644 healthy controls with a consistent genetic background for the 57 non-MHC variants reported to be associated with MS by the first large GWAS as well as the HLA DRB1*1501 tagging SNP rs3135388. We robustly replicated three of the 57 non-MHC reported MS-associated single nucleotide polymorphisms (SNPs). In addition, our study revealed several genotype-genotype combinations with an evidently higher degree of disease association than the genotypes of the single SNPs. We further correlated well-defined clinical phenotypes, i.e. ataxia, visual impairment due to optic neuritis and paresis with single SNPs and genotype combinations, and identified several associations. The results may open new avenues for clinical implications of the MS associated genetic variants reported from large GWAS.     

A study of CNVs as trait-associated polymorphisms and as expression quantitative trait loci

We conducted a comprehensive study of copy number variants (CNVs) well-tagged by SNPs (r(2)≥ 0.8) by analyzing their effect on gene expression and their association with disease susceptibility and other complex human traits. We tested whether these CNVs were more likely to be functional than frequency-matched SNPs as trait-associated loci or as expression quantitative trait loci (eQTLs) influencing phenotype by altering gene regulation. Our study found that CNV-tagging SNPs are significantly enriched for cis eQTLs; furthermore, we observed that trait associations from the NHGRI catalog show an overrepresentation of SNPs tagging CNVs relative to frequency-matched SNPs. We found that these SNPs tagging CNVs are more likely to affect multiple expression traits than frequency-matched variants. Given these findings on the functional relevance of CNVs, we created an online resource of expression-associated CNVs (eCNVs) using the most comprehensive population-based map of CNVs to inform future studies of complex traits. Although previous studies of common CNVs that can be typed on existing platforms and/or interrogated by SNPs in genome-wide association studies concluded that such CNVs appear unlikely to have a major role in the genetic basis of several complex diseases examined, our findings indicate that it would be premature to dismiss the possibility that even common CNVs may contribute to complex phenotypes and at least some common diseases.