Abstract：
:Although next-generation DNA sequencing technologies have made rare variant association studies feasible and affordable, the development of powerful statistical methods for rare variant association studies is still under way. Most of the existing methods for rare variant association studies compare the number of rare mutations in a group of rare variants (in a gene or a pathway) between cases and controls. However, these methods assume that all causal variants are risk to diseases. Recently, several methods that are robust to the direction and magnitude of effects of causal variants have been proposed. However, they are applicable to unrelated individuals only, whereas family data have been shown to improve power to detect rare variants. In this article, we propose two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. Using extensive simulation studies, we evaluate and compare our proposed methods with two methods based on the weights proposed by Madsen and Browning. Our results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning, especially when both risk and protective variants are present.
journal_name
Genet Epidemioljournal_title
Genetic epidemiologyauthors
Fang S,Sha Q,Zhang Sdoi
10.1002/gepi.21646subject
Has Abstractpub_date
2012-07-01 00:00:00pages
499-507issue
5eissn
0741-0395issn
1098-2272journal_volume
36pub_type
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journal_title：Genetic epidemiology
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journal_title：Genetic epidemiology
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journal_title：Genetic epidemiology
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journal_title：Genetic epidemiology
pub_type： 杂志文章
doi：10.1002/gepi.20016
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doi：10.1002/gepi.1370170708
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journal_title：Genetic epidemiology
pub_type： 杂志文章,评审
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journal_title：Genetic epidemiology
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journal_title：Genetic epidemiology
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journal_title：Genetic epidemiology
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journal_title：Genetic epidemiology
pub_type： 杂志文章,meta分析
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