title: Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis.
abstract: Although central to many studies of phenotypic variation and disease susceptibility, characterizing the genetic architecture of complex traits has been unexpectedly difficult. For example, most of the susceptibility genes that contribute to highly heritable conditions such as obesity and type 2 diabetes (T2D) remain to be identified despite intensive study. We took advantage of mouse models of diet-induced metabolic disease in chromosome substitution strains (CSSs) both to characterize the genetic architecture of diet-induced obesity and glucose homeostasis and to test the feasibility of gene discovery. Beginning with a survey of CSSs, followed with genetic and phenotypic analysis of congenic, subcongenic, and subsubcongenic strains, we identified a remarkable number of closely linked, phenotypically heterogeneous quantitative trait loci (QTLs) on mouse chromosome 6 that have unexpectedly large phenotypic effects. Although fine-mapping reduced the genomic intervals and gene content of these QTLs over 3000-fold, the average phenotypic effect on body weight was reduced less than threefold, highlighting the "fractal" nature of genetic architecture in mice. Despite this genetic complexity, we found evidence for 14 QTLs in only 32 recombination events in less than 3000 mice, and with an average of four genes located within the three body weight QTLs in the subsubcongenic strains. For Obrq2a1, genetic and functional studies collectively identified the solute receptor Slc35b4 as a regulator of obesity, insulin resistance, and gluconeogenesis. This work demonstrated the unique power of CSSs as a platform for studying complex genetic traits and identifying QTLs.
authors.affiliation: Department of Genetics, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
authors.forename: Soha N
authors.initials: SN
authors.lastname: Yazbek
authors.forename: David A
authors.initials: DA
authors.lastname: Buchner
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Geisinger
authors.forename: Lindsay C
authors.initials: LC
authors.lastname: Burrage
authors.forename: Sabrina H
authors.initials: SH
authors.lastname: Spiezio
authors.forename: Gabriel E
authors.initials: GE
authors.lastname: Zentner
authors.forename: Chang-Wen
authors.initials: CW
authors.lastname: Hsieh
authors.forename: Peter C
authors.initials: PC
authors.lastname: Scacheri
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Croniger
authors.forename: Joseph H
authors.initials: JH
authors.lastname: Nadeau
mesh_terms: D000818:Animals; D001835:Body Weight / Q000235:genetics; D002874:Chromosome Mapping; D033481:Chromosomes, Mammalian / Q000235:genetics; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D004032:Diet; D005786:Gene Expression Regulation; D005943:Gluconeogenesis / Q000235:genetics; D005947:Glucose / Q000378:metabolism*; D056945:Hep G2 Cells; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006706:Homeostasis / Q000235:genetics*; D006801:Humans; D007333:Insulin Resistance / Q000235:genetics; D008297:Male; D051379:Mice; D020297:Mice, Congenic; D023421:Models, Animal; D033704:Nucleotide Transport Proteins / Q000235:genetics* / Q000378:metabolism; D009765:Obesity / Q000235:genetics*; D010641:Phenotype; D040641:Quantitative Trait Loci; D017422:Sequence Analysis, DNA
chemical_list: D033704:Nucleotide Transport Proteins; C503492:SLC35B4 protein, human; D005947:Glucose