Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.

Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signali...

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Autores principales:	Wei Cheng, Sohini Ramachandran, Lorin Crawford
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2020
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/2d111a8b08b84a2b92339b71397291b2
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spelling	oai:doaj.org-article:2d111a8b08b84a2b92339b71397291b22021-12-02T20:02:58ZEstimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.1553-73901553-740410.1371/journal.pgen.1008855https://doaj.org/article/2d111a8b08b84a2b92339b71397291b22020-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1008855https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.Wei ChengSohini RamachandranLorin CrawfordPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 16, Iss 6, p e1008855 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Wei Cheng Sohini Ramachandran Lorin Crawford Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.
description	Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.
format	article
author	Wei Cheng Sohini Ramachandran Lorin Crawford
author_facet	Wei Cheng Sohini Ramachandran Lorin Crawford
author_sort	Wei Cheng
title	Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.
title_short	Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.
title_full	Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.
title_fullStr	Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.
title_full_unstemmed	Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.
title_sort	estimation of non-null snp effect size distributions enables the detection of enriched genes underlying complex traits.
publisher	Public Library of Science (PLoS)
publishDate	2020
url	https://doaj.org/article/2d111a8b08b84a2b92339b71397291b2
work_keys_str_mv	AT weicheng estimationofnonnullsnpeffectsizedistributionsenablesthedetectionofenrichedgenesunderlyingcomplextraits AT sohiniramachandran estimationofnonnullsnpeffectsizedistributionsenablesthedetectionofenrichedgenesunderlyingcomplextraits AT lorincrawford estimationofnonnullsnpeffectsizedistributionsenablesthedetectionofenrichedgenesunderlyingcomplextraits
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Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.

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