A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data.

Human Heredity
Dajiang J Liu, Suzanne M Leal

Abstract

There is great interest to sequence unrelated or pedigree samples for detecting rare variant quantitative trait associations. In order to reduce the cost of sequencing and improve power, many studies sequence selected samples with extreme traits. Existing methods for detecting rare variant associations were developed for unrelated samples. Methods are needed to analyze (selected or randomly ascertained) pedigree samples. We propose a unified framework of modeling extreme trait genetic associations (MEGA) with rare variants. Using MEGA and appropriate permutation algorithms, many rare variant tests can be extended to family data. As an application, we compared study designs using both sib-pairs and unrelated individuals. Extensive simulations were carried out using realistic population genetic and complex trait models. It is demonstrated that when extreme sampling is implemented within equal-sized cohorts of unrelated individuals or sib-pairs, analyzing unrelated individuals is consistently more powerful than studying sib-pairs. A higher portion of rare variants can be identified through sequencing unrelated samples compared to sibs. Alternatively, if samples are ascertained using fixed thresholds from an infinite-sized populati...Continue Reading

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Citations

Mar 29, 2014·European Journal of Human Genetics : EJHG·Qiuying Sha, Shuanglin Zhang
Oct 1, 2015·Statistics in Medicine·Lajmi Lakhal-ChaiebCelia M T Greenwood
Feb 28, 2014·Annals of Human Genetics·Tsung-Jen HsiehJohn Jen Tai
Dec 24, 2013·American Journal of Human Genetics·Zongxiao HeSuzanne M Leal
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Aug 27, 2013·Current Opinion in Allergy and Clinical Immunology·Kelly Burkett, Celia Greenwood
Jul 16, 2013·Current Protocols in Human Genetics·Bingshan LiSuzanne M Leal

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