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Leveraging family history in population-based case-control association studies.

ABSTRACT: Population-based epidemiologic studies often gather information from study participants on disease history among their family members. Although investigators widely recognize that family history will be associated with genotypes of the participants at disease susceptibility loci, they commonly ignore such information in primary genetic association analyses. In this report, we propose a simple approach to association testing by incorporating family history information as a "phenotype." We account for the expected attenuation in strength of association of the genotype of study participants with family history under Mendelian transmission. The proposed analysis can be performed using standard statistical software adopting either a meta- or pooled-analysis framework. Re-analysis of a total of 115 known susceptibility single-nucleotide polymorphisms, discovered through genome-wide association studies for several disease traits, indicates that incorporation of family history information can increase efficiency by as much as 40%. Efficiency gain depends on the type of design used for conducting the primary study, extent of family history, and accuracy and completeness of reporting.

SUBMITTER: Ghosh A

PROVIDER: S-EPMC6314034 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Leveraging family history in population-based case-control association studies.

Ghosh Arpita A Hartge Patricia P Kraft Peter P Joshi Amit D AD Ziegler Regina G RG Barrdahl Myrto M Chanock Stephen J SJ Wacholder Sholom S Chatterjee Nilanjan N

Genetic epidemiology 20140109 2

Population-based epidemiologic studies often gather information from study participants on disease history among their family members. Although investigators widely recognize that family history will be associated with genotypes of the participants at disease susceptibility loci, they commonly ignore such information in primary genetic association analyses. In this report, we propose a simple approach to association testing by incorporating family history information as a "phenotype." We account ...[more]

PMID: 24408355

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Project description:BackgroundConsidering relatives' health history in logistic regression for case-control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case-control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS.ResultsIn our simulation scenario of trait with modest T2D heritability (h2 = 0.28), variant minor allele frequency ranging from 1% to 50%, and 1% of phenotype variance explained by the genetic variants, Fam-meta had the highest overall power, while both methods incorporating family history were more powerful than CC-GWAS. All three methods had controlled type I error rates, while LT-FH was the most conservative with a lower-than-expected error rate. In addition, we observed a substantial increase in power of the two familial history methods compared to CC-GWAS when the prevalence of the phenotype increased with age. Furthermore, we showed that, when only the phenotypes of more distant relatives were available, Fam-meta still remained more powerful than CC-GWAS, confirming that leveraging disease history of both close and distant relatives can increase power of association analyses. Using FHS data, we confirmed the well-known association of TCF7L2 region with T2D at the genome-wide threshold of P-value < 5 × 10-8, and both familial history methods increased the significance of the region compared to CC-GWAS. We identified two loci at 5q35 (ADAMTS2) and 5q23 (PRR16), not previously reported for T2D using CC-GWAS and Fam-meta; both genes play a role in cardiovascular diseases. Additionally, CC-GWAS detected one more significant locus at 13q31 (GPC6) reported associated with T2D-related traits.ConclusionsOverall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies.

Dataset Information

Leveraging family history in population-based case-control association studies.

Publications

Leveraging family history in population-based case-control association studies.

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