Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method.
The genetic etiology of many complex diseases is highly heterogeneous. A complex disease can be caused by multiple mutations within the same gene or mutations in multiple genes at various genomic loci. Although these disease-susceptibility mutations can be collectively common in the population, they...
| Published in: | Genetics Vol. 210; no. 2; pp. 463 - 477 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Oxford University Press / USA
Oct2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132297959&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132297959 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00166731 GNT jtl: Genetics issn: 00166731 maglogo: N pubinfo: dt: Oct2018 vid: 210 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 132297959 10.1534/genetics.118.301266 132297959 ppf: 463 ppct: 14 formats: tig: atl: Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method. aug: au: Ming Li Zihuai He Xiaoran Tong Witte, John S. Qing Lu affil: Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Indiana 47405 sug: subj: Mutation Evaluation Genetic Techniques Methods Human Disease Susceptibility Sequence Analysis Methods Polymorphism, Genetic Conceptual Framework Phenotype Evaluation Alcoholism Familial and Genetic Gene Expression ab: The genetic etiology of many complex diseases is highly heterogeneous. A complex disease can be caused by multiple mutations within the same gene or mutations in multiple genes at various genomic loci. Although these disease-susceptibility mutations can be collectively common in the population, they are often individually rare or even private to certain families. Familybased studies are powerful for detecting rare variants enriched in families, which is an important feature for sequencing studies due to the heterogeneous nature of rare variants. In addition, family designs can provide robust protection against population stratification. Nevertheless, statistical methods for analyzing family-based sequencing data are underdeveloped, especially those accounting for heterogeneous etiology of complex diseases. In this article, we introduce a random field framework for detecting gene-phenotype associations in family-based sequencing studies, referred to as family-based genetic random field (FGRF). Similar to existing familybased association tests, FGRF could utilize within-family and between-family information separately or jointly to test an association. We demonstrate that FGRF has comparable statistical power with existing methods when there is no genetic heterogeneity, but can improve statistical power when there is genetic heterogeneity across families. The proposed method also shares the same advantages with the conventional family-based association tests (e.g., being robust to population stratification). Finally, we applied the proposed method to a sequencing data from the Minnesota Twin Family Study, and revealed several genes, including SAMD14, potentially associated with alcohol dependence. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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