Multidimensional genome scans identify the combinations of genetic loci linked to diabetes-related phenotypes in mice | Zendy

Katsuhiko Togawa | Zendy; Maki Moritani | Zendy; Hiroshi Yaguchi | Zendy; Mitsuo Itakura | Zendy

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Multidimensional genome scans identify the combinations of genetic loci linked to diabetes-related phenotypes in mice

Author(s) -

Katsuhiko Togawa,

Maki Moritani,

Hiroshi Yaguchi,

Mitsuo Itakura

Publication year - 2005

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/ddi433

Subject(s) - biology , quantitative trait locus , genetics , epistasis , locus (genetics) , congenic , population , phenotype , genetic linkage , gene mapping , pairwise comparison , genome scan , genome , genetic variation , allele , gene , chromosome , microsatellite , statistics , demography , mathematics , sociology

Most quantitative trait loci (QTL) studies have focused on detecting the genetic effects of individual QTLs. This study thoroughly dissected the genetic components of type 2 diabetic mice, including a search for epistatic interactions and multi-locus additive effects that result in variation in diabetes-related phenotypes. F2 population was generated from BKS.Cg-Leprdb+/+m and DBA/2 intercross and separated into six subpopulations by sex and the db-dependent diabetes severity. Single-locus and pairwise genome scans first identified the QTLs in these F2 subpopulations, and next covariate-dependent scans confirmed their sex-, db- and sex-by-db-specific effects in the combined populations. Single-locus genome scans detected four QTLs (QBIS1, QBIS2, QBIS3 and QBIS4) that presented their genetic effects beyond sex, but most QTLs showed their effects specifically in limited conditions. This highly conditional feature of the QTLs was accentuated in the pairwise analysis. The pairwise genome scans uncovered a total of 27 significantly interacting or additively acting pairs of loci, showing a better fit to explain the total phenotypic variation of the traits. These significant pairs affected the traits under constantly varying combinations of loci in a time series or in both sexes. In addition, pairwise analysis indicated the appropriate genetic background in constructing congenic strains to obtain the maximum power in the replication of phenotypes. Our study showed high degree of complexity in the genetics of type 2 diabetes in mice, and it suggested that a comprehensive understanding of the multi-locus effects was essential to disentangle the complex genetics of diabetes and obesity in humans.

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