Kenneth A. Walsh

Quantitative Trait Loci Analysis for Plasma HDL-Cholesterol Concentrations and Atherosclerosis Susceptibility Between Inbred Mouse Strains C57BL/6J and 129S1/SvImJ

Objective—The C57BL/6 (B6) and 129 mouse inbred strains differ markedly in plasma HDL-cholesterol concentrations and atherosclerosis susceptibility after a high-fat diet consumption. To identify loci controlling these traits, we performed quantitative trait loci (QTL) analysis. Methods and Results—We fed a high-fat diet to 294 (B6x129S1/SvImJ)F2 females for 14 weeks, measured plasma HDL concentrations and size of aortic fatty-streak lesions, genotyped F2 females, and performed QTL analysis. HDL concentrations were affected by six loci:Hdlq14 and Hdlq15 on chromosome 1 (peaks cM 80 and cM 104, logarithm of odds [LOD] 5.3 and 9.7, respectively); Hdlq16 on chromosome 8 (cM 44, LOD 2.6); Hdlq17 on chromosome 9 (cM 24, LOD 2.9); Hdlq18 on chromosome 12 (cM 20, LOD 5.9); and Hdlq19 on chromosome 2 (cM 90), which interacted with Hdlq15. Atherosclerosis susceptibility was affected by five loci:Ath17 on chromosome 10 (cM 34, LOD 6.6); Ath18 on chromosome 12 (cM 16, LOD 3.7); Ath19 (chromosome 11, cM 60), which interacted with Ath18; and Ath20 (chromosome 10, cM 10), which interacted with Ath21 (chromosome 12, cM 50). Conclusions—We identified six loci for HDL and five loci for atherosclerosis susceptibility in a (B6x129S1/SvImJ)F2 intercross.

Quantitative Trait Loci That Determine BMD in C57BL/6J and 129S1/SvImJ Inbred Mice.

BMD is highly heritable; however, little is known about the genes. To identify loci controlling BMD, we conducted a QTL analysis in a (B6 × 129) F2 population of mice. We report on additional QTLs and also narrow one QTL by combining the data from multiple crosses and through haplotype analysis.

Lith6 a new QTL for cholesterol gallstones from an intercross of CAST/Ei and DBA/2J inbred mouse strains,,

A complex genetic basis determines the individual predisposition to develop cholesterol gallstones in response to environmental factors. We employed quantitative trait locus/loci (QTL) analyses of an intercross between inbred strains CAST/Ei (susceptible) and DBA/2J (resistant) to determine the subset of gallstone susceptibility (Lith) genes these strains possess. Parental and first filial generation mice of both genders and male intercross offspring were evaluated for gallstone formation after feeding a lithogenic diet. Linkage analysis was performed using a form of multiple interval mapping. One significant QTL colocalized with Lith1 [chromosome (chr) 2, 50 cM], a locus identified previously. Significantly, new QTL were detected and named Lith10 (chr 6, 4 cM), Lith6 (chr 6, 54 cM), and Lith11 (chr 8, 58 cM). Statistical and genetic analyses suggest that Lith6 comprises two QTL in close proximity. Our molecular and genetic data support the candidacy of peroxisome proliferator-activated receptor γ (Pparg) and Slc21a1, encoding Pparg, and the basolateral bile acid transporter SLC21A1 (Slc21a1/Oatp1), respectively, as genes underlying Lith6.

Single and interacting QTLs for cholesterol gallstones revealed in an intercross between mouse strains NZB and SM.

Quantitative trait locus (QTL) mapping was employed to investigate the genetic determinants of cholesterol gallstone formation in a large intercross between mouse strains SM/J (resistant) and NZB/B1NJ (susceptible). Animals consumed a gallstonepromoting diet for 18 weeks. QTL analyses were performed using gallstone weight and gallstone absence/presence as phenotypes; various models were explored for genome scans. We detected seven single QTLs: three new, significant QTLs were named Lith17 [chromosome (Chr) 5, peaku2009=u200960 cM, LODu2009=u20095.4], Lith18 (Chr 5, 76 cM, LODu2009=u20094.3), and Lith19 (Chr 8, 0 cM, LODu2009=u20095.3); two confirmed QTLs identified previously and were named Lith20 (Chr 9, 44 cM, LODu2009=u20092.7) and Lith21 (Chr 10, 24 cM, LODu2009=u20092.9); one new, suggestive QTL (Chr 17) remains unnamed. Upon searching for epistatic interactions that contributed to gallstone susceptibility, the final suggestive QTL on Chr 7 was determined to interact significantly with Lith18 and, therefore, was named Lith22 (65 cM). A second interaction was identified between Lith19 and a locus on Chr 11; this QTL was named Lith23 (13 cM). mRNA expression analyses and amino acid haplotype analyses likely eliminated Slc10a2 as a candidate gene for Lith19. The QTLs identified herein largely contributed to gallstone formation rather than gallstone severity. Cloning the genes underlying these murine QTLs should facilitate prediction and cloning of the orthologous human genes.

The mouse QTL map helps interpret human genome-wide association studies for HDL cholesterol.

Genome-wide association (GWA) studies represent a powerful strategy for identifying susceptibility genes for complex diseases in human populations but results must be confirmed and replicated. Because of the close homology between mouse and human genomes, the mouse can be used to add evidence to genes suggested by human studies. We used the mouse quantitative trait loci (QTL) map to interpret results from a GWA study for genes associated with plasma HDL cholesterol levels. We first positioned single nucleotide polymorphisms (SNPs) from a human GWA study on the genomic map for mouse HDL QTL. We then used mouse bioinformatics, sequencing, and expression studies to add evidence for one well-known HDL gene (Abca1) and three newly identified genes (Galnt2, Wwox, and Cdh13), thus supporting the results of the human study. For GWA peaks that occur in human haplotype blocks with multiple genes, we examined the homologous regions in the mouse to prioritize the genes using expression, sequencing, and bioinformatics from the mouse model, showing that some genes were unlikely candidates and adding evidence for candidate genes Mvk and Mmab in one haplotype block and Fads1 and Fads2 in the second haplotype block. Our study highlights the value of mouse genetics for evaluating genes found in human GWA studies.

An experimental assessment of in silico haplotype association mapping in laboratory mice

BackgroundTo assess the utility of haplotype association mapping (HAM) as a quantitative trait locus (QTL) discovery tool, we conducted HAM analyses for red blood cell count (RBC) and high density lipoprotein cholesterol (HDL) in mice. We then experimentally tested each HAM QTL using published crosses or new F2 intercrosses guided by the haplotype at the HAM peaks.ResultsThe HAM for RBC, using 33 classic inbred lines, revealed 8 QTLs; 2 of these were true positives as shown by published crosses. A HAM-guided (C57BL/6J × CBA/J)F2 intercross we carried out verified 2 more as true positives and 4 as false positives. The HAM for HDL, using 81 strains including recombinant inbred lines and chromosome substitution strains, detected 46 QTLs. Of these, 36 were true positives as shown by published crosses. A HAM-guided (C57BL/6J × A/J)F2 intercross that we carried out verified 2 more as true positives and 8 as false positives. By testing each HAM QTL for RBC and HDL, we demonstrated that 78% of the 54 HAM peaks were true positives and 22% were false positives. Interestingly, all false positives were in significant allelic association with one or more real QTL.ConclusionBecause type I errors (false positives) can be detected experimentally, we conclude that HAM is useful for QTL detection and narrowing. We advocate the powerful and economical combined approach demonstrated here: the use of HAM for QTL discovery, followed by mitigation of the false positive problem by testing the HAM-predicted QTLs with small HAM-guided experimental crosses.

Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice

To identify genetic loci influencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identified one significant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to find cis-regulated expression QTL. We then narrowed the list of candidate genes under significant QTL using a “toolbox” of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis-regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, five genes (Polr3 h, Cyp2d22, Cyp2d26, Tspo, and Ttll12) as candidate genes for Chr 15 QTL. This study shows how bioinformatics can be used effectively to reduce candidate gene lists for QTL related to complex traits.

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ × SM/J intercross

A higher incidence of coronary artery disease is associated with a lower level of HDL-cholesterol. We searched for genetic loci influencing HDL-cholesterol in F2 mice from a cross between MRL/MpJ and SM/J mice. Quantitative trait loci (QTL) mapping revealed one significant HDL QTL (Apoa2 locus), four suggestive QTL on chromosomes 10, 11, 13, and 18 and four additional QTL on chromosomes 1 proximal, 3, 4, and 7 after adjusting HDL for the strong Apoa2 locus. A novel nonsynonymous polymorphism supports Lipg as the QTL gene for the chromosome 18 QTL, and a difference in Abca1 expression in liver tissue supports it as the QTL gene for the chromosome 4 QTL. Using weighted gene co-expression network analysis, we identified a module that after adjustment for Apoa2, correlated with HDL, was genetically determined by a QTL on chromosome 11, and overlapped with the HDL QTL. A combination of bioinformatics tools and systems genetics helped identify several candidate genes for both the chromosome 11 HDL and module QTL based on differential expression between the parental strains, cis regulation of expression, and causality modeling. We conclude that integrating systems genetics to a more-traditional genetics approach improves the power of complex trait gene identification.

A major X-linked locus affects kidney function in mice

Chronic kidney disease is a common disease with increasing prevalence in the western population. One common reason for chronic kidney failure is diabetic nephropathy. Diabetic nephropathy and hyperglycemia are characteristics of the mouse inbred strain KK/HlJ, which is predominantly used as a model for metabolic syndrome due to its inherited glucose intolerance and insulin resistance. We used KK/HlJ, an albuminuria-sensitive strain, and C57BL/6J, an albuminuria-resistant strain, to perform a quantitative trait locus (QTL) cross to identify the genetic basis for chronic kidney failure. Albumin–creatinine ratio (ACR) was measured in 130 F2 male offspring. One significant QTL was identified on chromosome (Chr) X and four suggestive QTL were found on Chrs 6, 7, 12, and 13. Narrowing of the QTL region was focused on the X-linked QTL and performed by incorporating genotype and expression analyses for genes located in the region. From the 485 genes identified in the X-linked QTL region, a few candidate genes were identified using a combination of bioinformatic evidence based on genomic comparison of the parental strains and known function in urine homeostasis. Finally, this study demonstrates the significance of the X chromosome in the genetic determination of albuminuria.