TY - JOUR
T1 - A powerful nonparametric statistical framework for family-based association analyses
AU - Li, Ming
AU - He, Zihuai
AU - Schaid, Daniel J.
AU - Cleves, Mario A.
AU - Nick, Todd G.
AU - Lu, Qing
N1 - Publisher Copyright:
© 2015 by the Genetics Society of America.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametricbased methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only withinfamily information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., betweenfamily information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.
AB - Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametricbased methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only withinfamily information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., betweenfamily information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.
KW - Between-family information
KW - Nicotine dependence
KW - Pedigree structure
KW - Population stratification
KW - Within-family information
UR - http://www.scopus.com/inward/record.url?scp=84928987210&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928987210&partnerID=8YFLogxK
U2 - 10.1534/genetics.115.175174
DO - 10.1534/genetics.115.175174
M3 - Article
C2 - 25745024
AN - SCOPUS:84928987210
SN - 0016-6731
VL - 200
SP - 69
EP - 78
JO - Genetics
JF - Genetics
IS - 1
ER -