Journal
GENETICS
Volume 210, Issue 2, Pages 463-476Publisher
GENETICS SOCIETY AMERICA
DOI: 10.1534/genetics.118.301266
Keywords
family-based association study; rare variants; genetic heterogeneity; population stratification; alcohol dependence
Categories
Funding
- National Institute on Drug Abuse [R01DA043501]
- National Library of Medicine [R01LM012848]
- National Heart, Lung and Blood Institute [K01HL140333]
- Eunice Kennedy Shriver National Institute of Child Health and Human Development [R03HD092854]
- National Center for Advancing Translational Sciences through Indiana Clinical and Translational Sciences Institute [UL1TR001108]
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [R03HD092854] Funding Source: NIH RePORTER
- NATIONAL CANCER INSTITUTE [R01CA201358] Funding Source: NIH RePORTER
- NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES [UL1TR001108] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [K01HL140333] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON DRUG ABUSE [R01DA043501] Funding Source: NIH RePORTER
- NATIONAL LIBRARY OF MEDICINE [R01LM012848] Funding Source: NIH RePORTER
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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. Family-based 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 family-based 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.
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