Arthur Ko

Integrative approaches for large-scale transcriptome-wide association studies

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Amerindian-specific regions under positive selection harbour new lipid variants in Latinos.

Dyslipidemia and obesity are especially prevalent in populations with Amerindian backgrounds, such as Mexican–Americans, which predispose these populations to cardiovascular disease. Here we design an approach, known as the cross-population allele screen (CPAS), which we conduct prior to a genome-wide association study (GWAS) in 19,273 Europeans and Mexicans, in order to identify Amerindian risk genes in Mexicans. Utilizing CPAS to restrict the GWAS input variants to only those differing in frequency between the two populations, we identify novel Amerindian lipid genes, receptor-related orphan receptor alpha (RORA) and salt-inducible kinase 3 (SIK3), and three loci previously unassociated with dyslipidemia or obesity. We also detect lipoprotein lipase (LPL) and apolipoprotein A5 (APOA5) harbouring specific Amerindian signatures of risk variants and haplotypes. Notably, we observe that SIK3 and one novel lipid locus underwent positive selection in Mexicans. Furthermore, after a high-fat meal, the SIK3 risk variant carriers display high triglyceride levels. These findings suggest that Amerindian-specific genetic architecture leads to a higher incidence of dyslipidemia and obesity in modern Mexicans.

A high-quality human reference panel reveals the complexity and distribution of genomic structural variants

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100 bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals.

Remote Ischemic Conditioning Alters Methylation and Expression of Cell Cycle Genes in Aneurysmal Subarachnoid Hemorrhage

Background and Purpose— Remote ischemic conditioning (RIC) is a phenomenon in which short periods of nonfatal ischemia in 1 tissue confers protection to distant tissues. Here we performed a longitudinal human pilot study in patients with aneurysmal subarachnoid hemorrhage undergoing RIC by limb ischemia to compare changes in DNA methylation and transcriptome profiles before and after RIC. Methods— Thirteen patients underwent 4 RIC sessions over 2 to 12 days after rupture of an intracranial aneurysm. We analyzed whole blood transcriptomes using RNA sequencing and genome-wide DNA methylomes using reduced representation bisulfite sequencing, both before and after RIC. We tested differential expression and differential methylation using an intraindividual paired study design and then overlapped the differential expression and differential methylation results for analyses of functional categories and protein–protein interactions. Results— We observed 164 differential expression genes and 3493 differential methylation CpG sites after RIC, of which 204 CpG sites overlapped with 103 genes, enriched for pathways of cell cycle (P<3.8×10−4) and inflammatory responses (P<1.4×10−4). The cell cycle pathway genes form a significant protein–protein interaction network of tightly coexpressed genes (P<0.00001). Conclusions— Gene expression and DNA methylation changes in aneurysmal subarachnoid hemorrhage patients undergoing RIC are involved in coordinated cell cycle and inflammatory responses.

Molecular Characterization of the Lipid Genome-Wide Association Study Signal on Chromosome 18q11.2 Implicates HNF4A-Mediated Regulation of the TMEM241 Gene

Objective— We recently identified a locus on chromosome 18q11.2 for high serum triglycerides in Mexicans. We hypothesize that the lead genome-wide association study single-nucleotide polymorphism rs9949617, or its linkage disequilibrium proxies, regulates 1 of the 5 genes in the triglyceride-associated region. Approach and Results— We performed a linkage disequilibrium analysis and found 9 additional variants in linkage disequilibrium (r 2>0.7) with the lead single-nucleotide polymorphism. To select the variants for functional analyses, we annotated the 10 variants using DNase I hypersensitive sites, transcription factor and chromatin states and identified rs17259126 as the lead candidate variant for functional in vitro validation. Using luciferase transcriptional reporter assay in liver HepG2 cells, we found that the G allele exhibits a significantly lower effect on transcription (P<0.05). The electrophoretic mobility shift and ChIPqPCR (chromatin immunoprecipitation coupled with quantitative polymerase chain reaction) assays confirmed that the minor G allele of rs17259126 disrupts an hepatocyte nuclear factor 4 &agr;–binding site. To find the regional candidate gene, we performed a local expression quantitative trait locus analysis and found that rs17259126 and its linkage disequilibrium proxies alter expression of the regional transmembrane protein 241 (TMEM241) gene in 795 adipose RNAs from the Metabolic Syndrome In Men (METSIM) cohort (P=6.11×10−07–5.80×10−04). These results were replicated in expression profiles of TMEM241 from the Multiple Tissue Human Expression Resource (MuTHER; n=856). Conclusions— The Mexican genome-wide association study signal for high serum triglycerides on chromosome 18q11.2 harbors a regulatory single-nucleotide polymorphism, rs17259126, which disrupts normal hepatocyte nuclear factor 4 &agr; binding and decreases the expression of the regional TMEM241 gene. Our data suggest that decreased transcript levels of TMEM241 contribute to increased triglyceride levels in Mexicans.

Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS

Increased adiposity is a hallmark of obesity and overweight, which affect 2.2 billion people world-wide. Understanding the genetic and molecular mechanisms that underlie obesity-related phenotypes can help to improve treatment options and drug development. Here we perform promoter Capture Hi–C in human adipocytes to investigate interactions between gene promoters and distal elements as a transcription-regulating mechanism contributing to these phenotypes. We find that promoter-interacting elements in human adipocytes are enriched for adipose-related transcription factor motifs, such as PPARG and CEBPB, and contribute to heritability of cis-regulated gene expression. We further intersect these data with published genome-wide association studies for BMI and BMI-related metabolic traits to identify the genes that are under genetic cis regulation in human adipocytes via chromosomal interactions. This integrative genomics approach identifies four cis-eQTL-eGene relationships associated with BMI or obesity-related traits, including rs4776984 and MAP2K5, which we further confirm by EMSA, and highlights 38 additional candidate genes.GWAS have identified numerous genetic loci for BMI and related traits. Here, Pan et al. generate Promoter Capture Hi-C data for human white adipocytes and integrate these with data of transcription factor motifs, RNA-seq and GWAS to identify eQTL-eGene relationships mediated by chromosomal interactions.

Reverse GWAS: Using Genetics to Identify and Model Phenotypic Subtypes

Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automatic statistical approaches to subtype definition particularly valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a bespoke decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show these features can be crucial for power and calibration. We validate RGWAS in practice by recovering known stress subtypes in major depressive disorder. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests genetic heterogeneity may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting potential have potential translational value. Author summary Complex diseases depend on interactions between many known and unknown genetic and environmental factors. However, most studies aggregate these strata and test for associations on average across samples, though biological factors and medical interventions can have dramatically different effects on different people. Further, more-sophisticated models are often infeasible because relevant sources of heterogeneity are not generally known a priori. We introduce Reverse GWAS to simultaneously split samples into homogeneoues subtypes and to learn differences in genetic or treatment effects between subtypes. Unlike existing approaches to computational subtype identification using high-dimensional trait data, RGWAS accounts for covariates, binary disease traits and, especially, population structure; these features are each invaluable in extensive simulations. We validate RGWAS by recovering known genetic subtypes of major depression. We demonstrate RGWAS is practically useful in a metabolic study, finding three novel subtypes with both SNP- and polygenic-level heterogeneity. Importantly, RGWAS can uncover differential treatment response: for example, we show that statin, a common drug and potential type 2 diabetes risk factor, may have opposing subtype-specific effects on blood glucose.

Genetic and environmental perturbations lead to regulatory decoherence

Correlation among traits is a fundamental feature of biological systems. From morphological characters, to transcriptional or metabolic networks, the correlations we routinely observe between traits reflect a shared regulation that remains poorly understood and difficult to study. To address this problem, we developed a new and flexible approach that allows us to identify factors associated with variation in correlation between individuals. Here, we use data from three large human cohorts to study the effects of genetic variation and environmental perturbation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (namely, infection and disease) lead to a systematic loss of correlation, which we define as ‘decoherence’. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we show that correlation itself is a trait under genetic control: specifically, we mapped and replicated hundreds of ‘correlation QTLs’, which often involve transcription factors or their known target genes. Together, this work furthers our understanding of how and why coordinated biological processes break down, and highlights the role of decoherence in disease emergence.

Author Correction: Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS

In the original version of this Article, Supplementary Table 10 contained incorrect primer sequences for the mobility shift assay for SNP rs4776984. These errors have now been fixed and the corrected version of the Supplementary Information PDF is available to download from the HTML version of the Article.

ASElux: an ultra-fast and accurate allelic reads counter

Motivation Mapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele‐specific expression (ASE) analysis. The existing methods, such as simulation and SNP‐aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA‐sequence (RNA‐seq) data. ASElux significantly reduces runtime by disregarding reads outside single nucleotide polymorphisms (SNPs) during the alignment. Results When compared to other tools on simulated and experimental data, ASElux achieves a higher accuracy on ASE estimation than non‐SNP‐aware aligners and requires a much shorter time than the benchmark SNP‐aware aligner, GSNAP with just a slight loss in performance. ASElux can process 40 million read‐pairs from an RNA‐sequence (RNA‐seq) sample and count allelic reads within 10 min, which is comparable to directly counting the allelic reads from alignments based on other tools. Furthermore, processing an RNA‐seq sample using ASElux in conjunction with a general aligner, such as STAR, is more accurate and still ˜4× faster than STAR + WASP, and ˜33× faster than the lead SNP‐aware aligner, GSNAP, making ASElux ideal for ASE analysis of large‐scale transcriptomic studies. We applied ASElux to 273 lung RNA‐seq samples from GTEx and identified a splice‐QTL rs11078928 in lung which explains the mechanism underlying an asthma GWAS SNP rs11078927. Thus, our analysis demonstrated ASE as a highly powerful complementary tool to cis‐expression quantitative trait locus (eQTL) analysis. Availability and implementation The software can be downloaded from https://github.com/abl0719/ASElux. Supplementary information Supplementary data are available at Bioinformatics online.