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Addressing the Missing Heritability Problem With the Help of Regulatory Features.

ABSTRACT: Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and published GWAS results with machine learning. When applied to type 2 diabetes and hypertension, the predicted susceptibility loci by FDSP were proved to be capable of explaining additional heritability. In addition, potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways. Our results suggested that taking regulatory features into consideration might be a useful way to address the missing heritability problem. We hope FDSP could offer help for the identification of novel susceptibility loci for complex diseases.

SUBMITTER: Dong SS

PROVIDER: S-EPMC6610400 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Addressing the Missing Heritability Problem With the Help of Regulatory Features.

Dong Shan-Shan SS Guo Yan Y Yang Tie-Lin TL

Evolutionary bioinformatics online 20190703

Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and publi ...[more]

PMID: 31320792

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Project description:CYP3A4 is the most important drug metabolizing enzyme in adult humans because of its prominent expression in liver and gut and because of its broad substrate specificity, which includes drugs from most therapeutic categories and many endogenous substances. Expression and function of CYP3A4 vary extensively both intra- and interindividually thus contributing to unpredictable drug response and toxicity. A multitude of environmental, genetic, and physiological factors are known to influence CYP3A4 expression and activity. Among the best predictable sources of variation are drug-drug interactions, which are either caused by pregnane X-receptor (PXR), constitutive androstane receptor (CAR) mediated gene induction, or by inhibition through coadministered drugs or other chemicals, including also plant and food ingredients. Among physiological and pathophysiological factors are hormonal status, age, and gender, the latter of which was shown to result in higher levels in females compared to males, as well as inflammatory processes that downregulate CYP3A4 transcription. Despite the influence of these non-genetic factors, the genetic influence on CYP3A4 activity was estimated in previous twin studies and using information on repeated drug administration to account for 66% up to 88% of the interindividual variation. Although many single nucleotide polymorphisms (SNPs) within the CYP3A locus have been identified, genetic association studies have so far failed to explain a major part of the phenotypic variability. The term "missing heritability" has been used to denominate the gap between expected and known genetic contribution, e.g., for complex diseases, and is also used here in analogy. In this review we summarize CYP3A4 pharmacogenetics/genomics from the early inheritance estimations up to the most recent genetic and clinical studies, including new findings about SNPs in CYP3A4 (*22) and other genes (P450 oxidoreductase (POR), peroxisome proliferator-activated receptor alpha (PPARA)) with possible contribution to CYP3A4 variable expression.

Project description:Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 x more heritability than GWAS-associated SNPs on average (P=3.3 x 10??). For some diseases, this increase was individually significant: 2.07 x for Multiple Sclerosis (MS) (P=6.5 x 10??) and 1.48 x for Crohn's Disease (CD) (P = 1.3 x 10?³); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 x more MS heritability than known MS SNPs (P < 1.0 x 10?¹? and 2.20 x more CD heritability than known CD SNPs (P = 6.1 x 10??), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 x more heritability from all SNPs at GWAS loci (P = 2.3 x 10??) and 5.33 x more heritability from all autoimmune disease loci (P < 1 x 10?¹? compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Dataset Information

Addressing the Missing Heritability Problem With the Help of Regulatory Features.

Publications

Addressing the Missing Heritability Problem With the Help of Regulatory Features.

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