Family-based association studies

• Published in: Statistical methods in medical research Vol. 9; no. 6; pp. 563 - 587
• Main Author: Zhao, Hongyu
• Format: Journal Article
• Language: English
• Published: Thousand Oaks, CA SAGE Publications 01.12.2000
• Subjects: Alleles; Environment; Epidemiologic Methods; Family Health; Genetic Linkage - genetics; Genetic Markers; Genetic Predisposition to Disease - epidemiology; Humans; Statistics as Topic - methods; X Chromosome
• ISSN: 0962-2802; 1477-0334
• DOI: 10.1177/096228020000900604

Over the past decade, attention has turned from positional cloning of Mendelian disease genes to the dissection of complex diseases. Both theoretical and empirical studies have shown that traditional linkage studies may be inferior in power compared to studies that directly utilize allele status. Case-control association studies, as an alternative, are subject to bias due to population stratification. As a compromise between linkage studies and case-control studies, family-based association designs have received great attention recently due to their potentially higher power to identify complex disease genes and their robustness in the presence of population substructure. In this review, we first describe the basic family-based association design involving one affected offspring with its two parents, all genotyped for a biallelic genetic marker. Extensions of the original transmission disequilibrium tests to multiallelic markers, families with multiple siblings, families with incomplete parental genotypes, and general pedigree structures are discussed. Further developments of statistical methods to study quantitative traits, to analyse genes on the X chromosome, to incorporate multiple tightly linked markers, to identify imprinting genes, and to detect gene-environment interactions are also reviewed. Finally, we discuss the implications of the completion of the Human Genome Project and the identification of hundreds of thousands of genetic polymorphisms on employing family-based association designs to search for complex disease genes.