OBJECTIVE: Atherosclerosis susceptibility is a genetic trait that varies between mouse strains. The goal of this study was to use a public mouse single nucleotide polymorphism (SNP) database to define the genetic loci that are associated with this trait, without the need to perform strain intercrosses that are normally required to obtain these loci. METHODS AND RESULTS: Apolipoprotein E (apoE)-deficient mice on 6 inbred genetic backgrounds were compared for atherosclerosis lesion size in the aortic root in 2 independent studies. After normalization to the C57BL/6 strain that was used in both studies, lesion areas were found in the following rank order: DBA/2J>C57BL/6>129/SV-ter>AKR/J approximately BALB/cByJ approximately C3H/HeJ. The log lesion difference in phenotypes between each of the 15 heterologous strain pairs was determined. A mouse SNP database was then used to calculate the genetic differences between the 15 strain pairs in partially overlapping 30-cM bins across the mouse genome. Correlation analyses were preformed to analyze the genetic and phenotypic differences among the strain pairs for each genetic region. The genetic regions with the highest correlations define the in silico quantitative trait loci (QTL) associated with the atherosclerosis phenotype. Five in silico atherosclerosis QTL were identified on chromosomes 1, 10, 14, 15, and 18. The loci on chromosomes 1, 10, 14, and 18 overlap with suggestive atherosclerosis QTL identified through analyses of an F(2) cohort derived from apoE-deficient mice on the C57BL/6 and FVB/N strains. CONCLUSIONS: The 5 identified in silico QTL are candidates for further study to confirm the presence and identity of atherosclerosis susceptibility genes within these loci.
OBJECTIVE:Atherosclerosis susceptibility is a genetic trait that varies between mouse strains. The goal of this study was to use a public mouse single nucleotide polymorphism (SNP) database to define the genetic loci that are associated with this trait, without the need to perform strain intercrosses that are normally required to obtain these loci. METHODS AND RESULTS:Apolipoprotein E (apoE)-deficient mice on 6 inbred genetic backgrounds were compared for atherosclerosis lesion size in the aortic root in 2 independent studies. After normalization to the C57BL/6 strain that was used in both studies, lesion areas were found in the following rank order: DBA/2J>C57BL/6>129/SV-ter>AKR/J approximately BALB/cByJ approximately C3H/HeJ. The log lesion difference in phenotypes between each of the 15 heterologous strain pairs was determined. A mouse SNP database was then used to calculate the genetic differences between the 15 strain pairs in partially overlapping 30-cM bins across the mouse genome. Correlation analyses were preformed to analyze the genetic and phenotypic differences among the strain pairs for each genetic region. The genetic regions with the highest correlations define the in silico quantitative trait loci (QTL) associated with the atherosclerosis phenotype. Five in silico atherosclerosis QTL were identified on chromosomes 1, 10, 14, 15, and 18. The loci on chromosomes 1, 10, 14, and 18 overlap with suggestive atherosclerosis QTL identified through analyses of an F(2) cohort derived from apoE-deficient mice on the C57BL/6 and FVB/N strains. CONCLUSIONS: The 5 identified in silico QTL are candidates for further study to confirm the presence and identity of atherosclerosis susceptibility genes within these loci.
Authors: David L Barker; Mark S T Hansen; A Fawad Faruqi; Diane Giannola; Orlando R Irsula; Roger S Lasken; Martin Latterich; Vladimir Makarov; Arnold Oliphant; Jonathon H Pinter; Richard Shen; Irina Sleptsova; William Ziehler; Eric Lai Journal: Genome Res Date: 2004-05 Impact factor: 9.043
Authors: Amin Majdalawieh; Lei Zhang; Ilia V Fuki; Daniel J Rader; Hyo-Sung Ro Journal: Proc Natl Acad Sci U S A Date: 2006-02-06 Impact factor: 11.205
Authors: Peggy Robinet; Dianna M Milewicz; Lisa A Cassis; Nicholas J Leeper; Hong S Lu; Jonathan D Smith Journal: Arterioscler Thromb Vasc Biol Date: 2018-01-04 Impact factor: 8.311
Authors: Doyon Won; Su-Ning Zhu; Mian Chen; Anouk-Martine Teichert; Jason E Fish; Charles C Matouk; Michael Bonert; Matadial Ojha; Philip A Marsden; Myron I Cybulsky Journal: Am J Pathol Date: 2007-11 Impact factor: 4.307