In search of low-frequency and rare variants affecting complex traits
Author(s) -
Kalliope Panoutsopoulou,
Ioanna Tachmazidou,
Eleftheria Zeggini
Publication year - 2013
Publication title -
human molecular genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.811
H-Index - 276
eISSN - 1460-2083
pISSN - 0964-6906
DOI - 10.1093/hmg/ddt376
Subject(s) - biology , imputation (statistics) , genetic architecture , population stratification , exome , computational biology , genome wide association study , minor allele frequency , allele frequency , genotyping , population , quantitative trait locus , genetics , exome sequencing , missing data , allele , computer science , genotype , single nucleotide polymorphism , phenotype , gene , machine learning , medicine , environmental health
The allelic architecture of complex traits is likely to be underpinned by a combination of multiple common frequency and rare variants. Targeted genotyping arrays and next-generation sequencing technologies at the whole-genome sequencing (WGS) and whole-exome scales (WES) are increasingly employed to access sequence variation across the full minor allele frequency (MAF) spectrum. Different study design strategies that make use of diverse technologies, imputation and sample selection approaches are an active target of development and evaluation efforts. Initial insights into the contribution of rare variants in common diseases and medically relevant quantitative traits point to low-frequency and rare alleles acting either independently or in aggregate and in several cases alongside common variants. Studies conducted in population isolates have been successful in detecting rare variant associations with complex phenotypes. Statistical methodologies that enable the joint analysis of rare variants across regions of the genome continue to evolve with current efforts focusing on incorporating information such as functional annotation, and on the meta-analysis of these burden tests. In addition, population stratification, defining genome-wide statistical significance thresholds and the design of appropriate replication experiments constitute important considerations for the powerful analysis and interpretation of rare variant association studies. Progress in addressing these emerging challenges and the accrual of sufficiently large data sets are poised to help the field of complex trait genetics enter a promising era of discovery.
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