Rafael A. Nafikov, Harkirat K. Sohi, Alejandro Q. Nato Jr., Andrea R. Horimoto, Tyler R. C. Day, Thomas D. Bird, Anita L. DeStefano, Elizabeth E. Blue, Ellen M. Wijsman
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引用次数: 0
Abstract
Whole genome sequence (WGS) data provides opportunities for comprehensive evaluation of variants that may influence complex traits. However, prioritizing the large number of variants, particularly those in non-coding regions, is a challenge. Here we present an approach that uses pedigree-based haplotyping to identify the risk haplotype and resulting set of prioritized variants in a region of interest (ROI) defined by identity-by-descent (IBD) sharing among familial cases. The approach is applicable for use in both a full range of pedigree sizes and for the full allele frequency spectrum of variants without the need for a large reference sample. By determining haplotype sharing among individuals with WGS data, we demonstrate the ability to accurately identify a risk haplotype and a strongly reduced list of potential risk alleles for a trait of interest along with the cases who carry the risk haplotype. This is important in the context of complex traits where the disease may be etiologically heterogeneous even within a single pedigree. Application to both simulated and real Alzheimer's disease family data shows that the approach leads to accurate risk-haplotype identification with marked reduction in the number of potential trait-associated variants. Simulation also shows that the approach provides accurate risk haplotypes in ROIs.
期刊介绍:
Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations.
Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.