ORCID Profile
0000-0003-3440-1913
Current Organisation
Boston Children's Hospital
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Publisher: Public Library of Science (PLoS)
Date: 27-12-2022
DOI: 10.1371/JOURNAL.PGEN.1010557
Abstract: Genetic association studies of many heritable traits resulting from physiological testing often have modest s le sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of erse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For ex le, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 ( PRIM1 ) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV 1 /FVC), and an eQTL of matrix metallopeptidase 15 ( MMP15 ) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 ( HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble erse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.
Publisher: Springer Science and Business Media LLC
Date: 04-03-2021
DOI: 10.1038/S42003-021-01823-W
Abstract: Genetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with many due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits.
Publisher: Springer Science and Business Media LLC
Date: 20-02-2017
DOI: 10.1038/NG.3795
Publisher: Cold Spring Harbor Laboratory
Date: 11-06-2021
DOI: 10.1101/2021.06.08.21258515
Abstract: The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis -linked genes. However, despite the availability of gene expression and epigenomic data sets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic eQTLs, suggesting that better models are needed. The field must confront this deficit, and pursue this “missing regulation.”
Publisher: Cold Spring Harbor Laboratory
Date: 20-09-2020
DOI: 10.1101/2020.09.19.304832
Abstract: Genetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with most due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits.
Publisher: Cold Spring Harbor Laboratory
Date: 18-05-2021
DOI: 10.1101/2021.05.13.21257044
Abstract: Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents deter- mining if the same allele is responsible, indicating a shared underlying mechanism. Using a collection of 129,058 cases and controls across six diseases, we show that ~40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles two-fold by combining cases and controls across diseases, allowing us to identify more eQTLs driven by the shared alleles. The patterns of sharing indicate widespread shared mechanisms, but not a single global autoimmune mecha- nism. Our approach can be applied to any set of traits, and is particularly valuable as s le collections become depleted.
Location: United States of America
No related grants have been discovered for Sung Chun.