Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice

<p>Abstract</p> <p>Background</p> <p>Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia.</p> <p>Results</p> <p>In order to determine the genetic factors that contribute to these T2D related characteristics in TH mice, we interbred TH mice with C57BL/6J (B6) mice. The parental, F1, and F2 mice were phenotyped at 8, 12, 16, 20, and 24 weeks of age for 4-hour fasting plasma triglyceride, cholesterol, insulin, and glucose levels and body, fat pad and carcass weights. The F2 mice were genotyped genome-wide and used for quantitative trait locus (QTL) mapping. We also applied a genetical genomic approach using a subset of the F2 mice to seek candidate genes underlying the QTLs. Major QTLs were detected on chromosomes (Chrs) 1, 11, 4, and 8 for hypertriglyceridemia, 1 and 3 for hypercholesterolemia, 4 for hyperglycemia, 11 and 1 for body weight, 1 for fat pad weight, and 11 and 14 for carcass weight. Most alleles, except for Chr 3 and 14 QTLs, increased phenotypic values when contributed by the TH strain. Fourteen pairs of interacting loci were detected, none of which overlapped the major QTLs. The QTL interval linked to hypercholesterolemia and hypertriglyceridemia on distal Chr 1 contains <it>Apoa2 </it>gene. Sequencing analysis revealed polymorphisms of <it>Apoa2 </it>in TH mice, suggesting <it>Apoa2 </it>as the candidate gene for the hyperlipidemia QTL. Gene expression analysis added novel information and aided in selection of candidates underlying the QTLs.</p> <p>Conclusions</p> <p>We identified several genetic loci that affect the quantitative variations of plasma lipid and glucose levels and obesity traits in a TH × B6 intercross. Polymorphisms in <it>Apoa2 </it>gene are suggested to be responsible for the Chr 1 QTL linked to hypercholesterolemia and hypertriglyceridemia. Further, genetical genomic analysis led to potential candidate genes for the QTLs.</p

Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

Background: Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. Methods: We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. Results: WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P <0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E(-7)), and immune-related complications (e. g. Natural killer cell mediated cytotoxity, P = 3.8E(-5); B cell receptor signaling pathway, P = 7.2E(-5)). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be associated with obesity in humans and rodents, e.g. CSF1R and MARC2. Conclusions: To our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory genes related to obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis

Combining QTL data for HDL cholesterol levels from two different species leads to smaller confidence intervals.

Quantitative trait locus (QTL) analysis detects regions of a genome that are linked to a complex trait. Once a QTL is detected, the region is narrowed by positional cloning in the hope of determining the underlying candidate gene-methods used include creating congenic strains, comparative genomics and gene expression analysis. Combined cross analysis may also be used for species such as the mouse, if the QTL is detected in multiple crosses. This process involves the recoding of QTL data on a per-chromosome basis, with the genotype recoded on the basis of high- and low-allele status. The data are then combined and analyzed; a successful analysis results in a narrowed and more significant QTL. Using parallel methods, we show that it is possible to narrow a QTL by combining data from two different species, the rat and the mouse. We combined standardized high-density lipoprotein phenotype values and genotype data for the rat and mouse using information from one rat cross and two mouse crosses. We successfully combined data within homologous regions from rat Chr 6 onto mouse Chr 12, and from rat Chr 10 onto mouse Chr 11. The combinations and analyses resulted in QTL with smaller confidence intervals and increased logarithm of the odds ratio scores. The numbers of candidate genes encompassed by the QTL on mouse Chr 11 and 12 were reduced from 1343 to 761 genes and from 613 to 304 genes, respectively. This is the first time that QTL data from different species were successfully combined; this method promises to be a useful tool for narrowing QTL intervals

Candidate genes for obesity revealed from a C57BL/6J x 129S1/SvImJ intercross.

OBJECTIVE: To identify the genes controlling body fat, we carried out a quantitative trait locus (QTL) analysis using C57BL/6J (B6) and 129S1/SvImJ (129) mice, which differ in obesity susceptibility after consuming an atherogenic diet. METHODS: Mice were fed chow until 8 weeks and an atherogenic diet from 8 to 16 weeks; body fatness was measured by X-ray absorptiometry in 528 (B6 x 129) F(2) at 8 and 16 weeks. A high-density genome scan was performed using 508 polymorphic markers. After identifying the genetic loci, we narrowed the QTL using comparative genomics and bioinformatics. RESULTS: The percentage of body fat was significantly linked to loci on chromosomes (Chr) 1 (22, 68 and 173 Mb), 4 (74 Mb), 5 (73 Mb), 7 (88 Mb), 8 (43 and 80 Mb), 9 (55 Mb), 11 (115 Mb) and 12 (32 Mb); three suggestive loci on Chrs 6 (76 Mb), 9 (30 Mb) and 16 (26 Mb) and two pairs of interacting loci (Chr 2 at 99.8 Mb with Chr 7; Chr 1 at 68 Mb with Chr 11). Comparative genomics narrowed the QTL intervals by 20-57% depending on the chromosome; in most cases, haplotype analysis further narrowed them by about 90%. CONCLUSIONS: Our analysis identified 15 QTL for percentage of body fat. We narrowed the QTL using comparative genomics and haplotype analysis and suggest several candidate genes: Apcs on Chr 1, Ppargc1a on Chr 5, Ucp1 on Chr 8, Angptl6 on Chr 9 and Lpin1 on Chr 12