ArticleHuman heredity2012
A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data.
Article in Human heredity, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A novel statistical method for rare-variant association studies in general pedigrees.BMC proceedings · 2016Article
- Test of rare variant association based on affected sib-pairs.European journal of human genetics : EJHG · 2015Article
- Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data.American journal of human genetics · 2014Article
- Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data.Annual review of statistics and its application · 2014Article
- Detecting association of rare variants by testing an optimally weighted combination of variants for quantitative traits in general families.Annals of human genetics · 2013Article
- Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants.Genetic epidemiology · 2013Article
- Identifying rare variants associated with complex traits via sequencing.Current protocols in human genetics · 2013Review
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2 authors.
Funding
Abstract
objectivesThere 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.
methodsWe 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.
resultsIt 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 population, sequencing one sib with the most extreme trait from each extreme concordant sib-pair is consistently the most powerful design.
conclusionsMEGA will play an important role in the analysis of sequence-based genetic association studies.
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