Family-based approaches: design, imputation, analysis, and beyond

Abstract

Participants in the family-based analysis group at Genetic Analysis Workshop 19 addressed diverse topics, all of which used the family data. Topics addressed included questions of study design and data quality control (QC), genotype imputation to augment available sequence data, and linkage and/or association analyses. Results show that pedigree-based tests that are sensitive to genotype error may be useful for QC. Imputation quality improved with inclusion of small amounts of pedigree information used to phase the data in evaluation of 5 commonly used approaches for imputation in samples of (typically) unrelated subjects. It improved still further when pedigree-based imputation using larger pedigrees was also added. An important distinction was made between methods that do versus do not make use of Mendelian transmission in pedigrees, because this serves as a key difference between underlying models and assumptions. Methods that model relatedness generally had higher power in association testing than did analyses that carry out testing in the presence of a transmission model, but this may reflect details of implementation and/or ability of more general methods to jointly include data from larger pedigrees. In either case, for single nucleotide polymorphism–set approaches, weights that incorporate information on functional effects may be more useful than those that are based only on allele frequencies. The overall results demonstrate that family data continue to provide important information in the search for trait loci.

Genome-Wide Association Study for Muscle Fat Content and Abdominal Fat Traits in Common Carp (Cyprinus carpio)

“…It is generally accepted that association analysis using unrelated individuals is more powerful than using related individuals. However, family-based designs have several advantages, including quality control, well-known robustness to population stratification [34] and the ability to offer a variety of genetic analyses that cannot be achieved by using a sample of unrelated individuals, such as testing of parent-of-origin effects [35], detecting Mendelian errors, testing whether a genetic variant is inherited or de novo and combined linkage and association analysis [36]. Recently, a large number of family-based GWAS have been conducted on livestock [37].…”

Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19

“…At the workshop, investigators used these data to address a wide variety of topics. Analytical issues addressed included methods for population- [14] and family-based [15] association, machine learning and data mining approaches to gene localization [16], and methods for joint analysis of mutiple phenotypes [17]. Some groups concentrated on approaches to dealing with multiple testing in these high dimensional sequence data by filtering sequence variants or placing informative priors for association analyses [18], by pathway-based approaches for gene localization [19], or by other variant collapsing approaches [20].…”

A Perception on Genome-Wide Genetic Analysis of Metabolic Traits in Arab Populations

“…Both the abovementioned studies confirmed well-established gene loci, but failed to identify any novel “rare” variants. Considerable attention needs to be paid to appropriate study designs as family data continue to provide important information in the search for trait loci (107). It is ideal if the recruitment of large-pedigrees/extended families, particularly those containing several sub-families suitable for both parent-offspring design or for sibling design, with high inbreeding and roots traceable up to at least six generations with deduced consanguinity data is possible.…”