An evaluation of approaches for rare variant association analyses of binary traits in related samples

Abstract Recognizing that family data provide unique advantage of identifying rare risk variants in genetic association studies, many cohorts with related samples have gone through whole genome sequencing in large initiatives such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Ana...

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Autores principales:	Ming-Huei Chen, Achilleas Pitsillides, Qiong Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/72b245b1894a491e9d3931f638fb49ac
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spelling	oai:doaj.org-article:72b245b1894a491e9d3931f638fb49ac2021-12-02T14:06:24ZAn evaluation of approaches for rare variant association analyses of binary traits in related samples10.1038/s41598-021-82547-z2045-2322https://doaj.org/article/72b245b1894a491e9d3931f638fb49ac2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82547-zhttps://doaj.org/toc/2045-2322Abstract Recognizing that family data provide unique advantage of identifying rare risk variants in genetic association studies, many cohorts with related samples have gone through whole genome sequencing in large initiatives such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Analyzing rare variants poses challenges for binary traits in that some genotype categories may have few or no observed events, causing bias and inflation in commonly used methods. Several methods have recently been proposed to better handle rare variants while accounting for family relationship, but their performances have not been thoroughly evaluated together. Here we compare several existing approaches including SAIGE but not limited to related samples using simulations based on the Framingham Heart Study samples and genotype data from Illumina HumanExome BeadChip where rare variants are the majority. We found that logistic regression with likelihood ratio test applied to related samples was the only approach that did not have inflated type I error rates in both single variant test (SVT) and gene-based tests, followed by Firth logistic regression that had inflation in its direction insensitive gene-based test at prevalence 0.01 only, applied to either related or unrelated samples, though theoretically logistic regression and Firth logistic regression do not account for relatedness in samples. SAIGE had inflation in SVT at prevalence 0.1 or lower and the inflation was eliminated with a minor allele count filter of 5. As for power, there was no approach that outperformed others consistently among all single variant tests and gene-based tests.Ming-Huei ChenAchilleas PitsillidesQiong YangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ming-Huei Chen Achilleas Pitsillides Qiong Yang An evaluation of approaches for rare variant association analyses of binary traits in related samples
description	Abstract Recognizing that family data provide unique advantage of identifying rare risk variants in genetic association studies, many cohorts with related samples have gone through whole genome sequencing in large initiatives such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Analyzing rare variants poses challenges for binary traits in that some genotype categories may have few or no observed events, causing bias and inflation in commonly used methods. Several methods have recently been proposed to better handle rare variants while accounting for family relationship, but their performances have not been thoroughly evaluated together. Here we compare several existing approaches including SAIGE but not limited to related samples using simulations based on the Framingham Heart Study samples and genotype data from Illumina HumanExome BeadChip where rare variants are the majority. We found that logistic regression with likelihood ratio test applied to related samples was the only approach that did not have inflated type I error rates in both single variant test (SVT) and gene-based tests, followed by Firth logistic regression that had inflation in its direction insensitive gene-based test at prevalence 0.01 only, applied to either related or unrelated samples, though theoretically logistic regression and Firth logistic regression do not account for relatedness in samples. SAIGE had inflation in SVT at prevalence 0.1 or lower and the inflation was eliminated with a minor allele count filter of 5. As for power, there was no approach that outperformed others consistently among all single variant tests and gene-based tests.
format	article
author	Ming-Huei Chen Achilleas Pitsillides Qiong Yang
author_facet	Ming-Huei Chen Achilleas Pitsillides Qiong Yang
author_sort	Ming-Huei Chen
title	An evaluation of approaches for rare variant association analyses of binary traits in related samples
title_short	An evaluation of approaches for rare variant association analyses of binary traits in related samples
title_full	An evaluation of approaches for rare variant association analyses of binary traits in related samples
title_fullStr	An evaluation of approaches for rare variant association analyses of binary traits in related samples
title_full_unstemmed	An evaluation of approaches for rare variant association analyses of binary traits in related samples
title_sort	evaluation of approaches for rare variant association analyses of binary traits in related samples
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/72b245b1894a491e9d3931f638fb49ac
work_keys_str_mv	AT minghueichen anevaluationofapproachesforrarevariantassociationanalysesofbinarytraitsinrelatedsamples AT achilleaspitsillides anevaluationofapproachesforrarevariantassociationanalysesofbinarytraitsinrelatedsamples AT qiongyang anevaluationofapproachesforrarevariantassociationanalysesofbinarytraitsinrelatedsamples AT minghueichen evaluationofapproachesforrarevariantassociationanalysesofbinarytraitsinrelatedsamples AT achilleaspitsillides evaluationofapproachesforrarevariantassociationanalysesofbinarytraitsinrelatedsamples AT qiongyang evaluationofapproachesforrarevariantassociationanalysesofbinarytraitsinrelatedsamples
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An evaluation of approaches for rare variant association analyses of binary traits in related samples

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