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• W1964351947 endingPage "46" @default.
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• W1964351947 abstract "Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits. Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits." @default.
• W1964351947 created "2016-06-24" @default.
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• W1964351947 date "2014-01-01" @default.
• W1964351947 modified "2023-10-03" @default.
• W1964351947 title "Rare-Variant Extensions of the Transmission Disequilibrium Test: Application to Autism Exome Sequence Data" @default.
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• W1964351947 cites W1968776100 @default.
• W1964351947 cites W1969063901 @default.
• W1964351947 cites W1970848271 @default.
• W1964351947 cites W1976861043 @default.
• W1964351947 cites W1977673229 @default.
• W1964351947 cites W1982220354 @default.
• W1964351947 cites W1984068087 @default.
• W1964351947 cites W1987754412 @default.
• W1964351947 cites W1989168127 @default.
• W1964351947 cites W2003958717 @default.
• W1964351947 cites W2009132217 @default.
• W1964351947 cites W2012768252 @default.
• W1964351947 cites W2019483502 @default.
• W1964351947 cites W2029387902 @default.
• W1964351947 cites W2031082289 @default.
• W1964351947 cites W2031827634 @default.
• W1964351947 cites W2038015622 @default.
• W1964351947 cites W2039299884 @default.
• W1964351947 cites W2042982067 @default.
• W1964351947 cites W2044394153 @default.
• W1964351947 cites W2050756466 @default.
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• W1964351947 cites W2073154969 @default.
• W1964351947 cites W2081037263 @default.
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• W1964351947 doi "https://doi.org/10.1016/j.ajhg.2013.11.021" @default.
• W1964351947 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3882934" @default.
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• W1964351947 hasPublicationYear "2014" @default.
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