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Assessing the Effect of Sampling Strategies on the Power of Linkage Analysis to Identify Pathway‐Specific Loci Underlying a Complex Disease
Author(s) -
Guo Xiuqing,
Lin Ying-Chao,
Wang Yaping,
Cheng Li Shu-Chuan,
Yang Huiying
Publication year - 2001
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.2001.21.s1.s754
Subject(s) - linkage (software) , genetic linkage , quantitative trait locus , trait , selection (genetic algorithm) , biology , genetics , population , sampling (signal processing) , quantile , nuclear family , statistics , gene , mathematics , computer science , medicine , artificial intelligence , environmental health , filter (signal processing) , computer vision , programming language , sociology , anthropology
Using the simulated general population data sets, we first examined the effect of sampling strategies on the power of identifying linkage by selecting samples with (A) two affected sibs in a nuclear family and (B) one affected sib and one sib with an intermediate trait value in the upper quantiles. Second, we evaluated the improvement in power when analyzing correlated traits simultaneously. Under each selection criteria, 100 replicates of 300 nuclear families were sampled and analyzed with two‐point linkage analysis for ten markers (1 cM apart) from each of the candidate regions. Different genes were identified under different sampling strategies. When a gene has a pleitropic effect, it is more powerful to analyze correlated traits simultaneously, either by using a linear combination or the larger value of standardized traits, than to analyze each trait separately. © 2001 Wiley‐Liss, Inc.