Contrasting genetic architectures in different mouse reference populations used for studying complex traits

被引:46
作者
Buchner, David A. [1 ]
Nadeau, Joseph H. [2 ]
机构
[1] Case Western Reserve Univ, Dept Biochem, Dept Genet & Genome Sci, Cleveland, OH 44106 USA
[2] Pacific Northwest Diabet Res Inst, Seattle, WA 98122 USA
基金
美国国家卫生研究院;
关键词
CHROMOSOME SUBSTITUTION STRAINS; GENOME-WIDE ASSOCIATION; TRANSGENERATIONAL EPIGENETIC INHERITANCE; RECOMBINANT-INBRED STRAINS; COLLABORATIVE CROSS MICE; TRIMETHYLAMINE-N-OXIDE; BARDET-BIEDL-SYNDROME; DIET-INDUCED OBESITY; MISSING HERITABILITY; QUANTITATIVE TRAITS;
D O I
10.1101/gr.187450.114
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.
引用
收藏
页码:775 / 791
页数:17
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