Genetic factors for resistance to diet-induced obesity and associated metabolic traits on mouse chromosome 17

Abstract

Obesity is associated with increased susceptibility to dyslipidemia, insulin resistance, and hypertension, a combination of traits that comprise the traditional definition of the metabolic syndrome. Recent evidence suggests that obesity is also associated with the development of nonalcoholic fatty liver disease (NAFLD). Despite the high prevalence of obesity and its related conditions, their etiologies and pathophysiology remains unknown. Both genetic and environmental factors contribute to the development of obesity and NAFLD. Previous genetic analysis of high-fat, diet-induced obesity in C57BL/6J (B6) and A/J male mice using a panel of B6-ChrA/J/NaJ chromosome substitution strains (CSSs) demonstrated that 17 CSSs conferred resistance to high-fat, diet-induced obesity. One of these CSS strains, CSS-17, which is homosomic for A/J-derived chromosome 17, was analyzed further and found to be resistant to diet-induced steatosis. In the current study we generated seven congenic strains derived from CCS-17, fed them either a high-fat, simple-carbohydrate (HFSC) or low-fat, simple-carbohydrate (LFSC) diet for 16 weeks and then analyzed body weight and related traits. From this study we identified several quantitative trait loci (QTLs). On a HFSC diet, Obrq13 protects against diet-induced obesity, steatosis, and elevated fasting insulin and glucose levels. On the LFSC diet, Obrq13 confers lower hepatic triglycerides, suggesting that this QTL regulates liver triglycerides regardless of diet. Obrq15 protects against diet-induced obesity and steatosis on the HFSC diet, and Obrq14 confers increased final body weight and results in steatosis and insulin resistance on the HFSC diet. In addition, on the LFSC diet, Obrq 16 confers decreased hepatic triglycerides and Obrq17 confers lower plasma triglycerides on the LFSC diet. These congenic strains provide mouse models to identify genes and metabolic pathways that are involved in the development of NAFLD and aspects of diet-induced metabolic syndrome.

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

“…2). Remarkably, similar numbers of sub-QTLs are identified within QTLs that were entire chromosomes or only several Mb (Youngren et al 2003;Shao et al 2008;Millward et al 2009;Yazbek et al 2011;Kato et al 2014;Zhu and Matin 2014). For example, a congenic panel of nine strains for a plasma cholesterol QTL that initially spanned Chromosome 10 found four sub-QTLs (Shao et al 2008).…”

“…A congenic panel of seven strains spanning a body weight QTL that spanned Chromosome 17 found three subQTLs ( Fig. 2C,D ;Millward et al 2009). A panel of 15 congenic strains spanning a body weight QTL spanning Chromosome 6 found four sub-QTLs (Shao et al 2008).…”

“…Mapping a QTL to a chromosome will likely require higher resolution mapping to reach the point at which candidate gene analysis is feasible, either with additional crosses or by generating congenic and subcongenic panels. Analysis of subcongenic panels are most likely to lead to gene identification when the interval they span is just a few Mb (Shao et al 2008;Millward et al 2009;Bhatnagar et al 2011;Yazbek et al 2011;Buchner et al 2012). Generating and phenotyping subcongenic panels at this level of resolution requires approximately 1500 mice per panel (Yazbek et al 2011;Buchner et al 2012).…”

Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis

“…Nevertheless, we found many large effect QTLs, especially compared with those detected in other QTL mapping paradigms. Analysis of additional CSSs and congenic strains suggests that other traits and chromosomes show similar genetic architectures, in terms of QTL number, context-dependence, and strong phenotypic effects (Youngren et al 2003;Shao et al 2008;Millward et al 2009). Whether this genetic complexity compromises gene discovery is an open question.…”

“…Measurements were compared using an unpaired Student t-test with Welch's correction. Analysis of body weight, insulin level, glucose level, and HOMA in the congenic panels was based on the sequential analysis method as previously described (Shao et al 2008(Shao et al , 2010Millward et al 2009). Briefly, each congenic strain was compared to the most genetically similar congenic strain with phenotypic differences (after Bonferoni correction for multiple hypothesis testing) attributed to sequence variation within the regions of DNA sequence that differ between the two strains.…”