TY - JOUR
T1 - Adjusting family relatedness in data-driven burden test of rare variants
AU - Zhang, Qunyuan
AU - Wang, Lihua
AU - Koboldt, Dan
AU - Boreki, Ingrid B.
AU - Province, Michael A.
N1 - Publisher Copyright:
© 2014 WILEY PERIODICALS, INC.
PY - 2014/12/1
Y1 - 2014/12/1
N2 - Family data represent a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control for the inflation of false positives. We compare WSMM with a nondata-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.
AB - Family data represent a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control for the inflation of false positives. We compare WSMM with a nondata-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.
KW - Burden test
KW - Family data
KW - Mixed model
KW - Permutation
KW - Rare variants
UR - http://www.scopus.com/inward/record.url?scp=84910641691&partnerID=8YFLogxK
U2 - 10.1002/gepi.21848
DO - 10.1002/gepi.21848
M3 - Article
C2 - 25169066
AN - SCOPUS:84910641691
SN - 0741-0395
VL - 38
SP - 722
EP - 727
JO - Genetic Epidemiology
JF - Genetic Epidemiology
IS - 8
ER -