Genetic influences on growth and body composition in mice: multilocus interactions

VARIABLE PRIORITIZATION IN NONLINEAR BLACK BOX METHODS: A GENETIC ASSOCIATION CASE STUDY1

The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel and interpretable way to summarize the relative importance of predictor variables. Methodologically, we develop the "RelATive cEntrality" (RATE) measure to prioritize candidate genetic variants that are not just marginally important, but whose associations also stem from significant covarying relationships with other variants in the data. We illustrate RATE through Bayesian Gaussian process regression, but the methodological innovations apply to other "black box" methods. It is known that nonlinear models often exhibit greater predictive accuracy than linear models, particularly for phenotypes generated by complex genetic architectures. With detailed simulations and two real data association mapping studies, we show that applying RATE enables an explanation for this improved performance.

A longitudinal quantitative trait locus mapping of chicken growth traits

Since the growth traits of chickens are largely related to the production of meat and eggs, it is definitely important to understand genetic basis of growth traits. Although many quantitative trait loci (QTLs) that affect growth traits have recently been reported in chickens, little is known about genetic architecture of growth traits across all growth stages. Therefore, we conducted a longitudinal QTL study of growth traits measured from 0 to 64 weeks of age using 134 microsatellite DNA markers on 26 autosomes from 406 F₂ females, which resulted from an intercross of Oh-Shamo and White Leghorn chicken breeds. We found 27 and 21 independent main-effect QTLs for body weight and shank length, respectively. Moreover, 15 and 4 pairs of epistatic QTLs were found for body weight and shank length, respectively. Taken together, the present study revealed 48 QTLs for growth traits on 21 different autosomes, and these loci clearly have age-specific effects on phenotypes throughout stages that are important for meat and egg productions. Approximately 60% of Oh-Shamo-derived alleles increased the phenotypic values, corresponding to the fact that Oh-Shamo traits were higher than those of White Leghorn. On the other hand, remaining Oh-Shamo alleles decreased the phenotypic values. Our results clearly indicated that the growth traits of chickens are regulated by several main and epistatic QTLs that are widely distributed in the chicken genome, and that the QTLs have age-dependent manners of controlling the traits. This study implies importance of not only cross-sectional but also longitudinal growth data for further understanding of the complex genetic architecture in animal.

Mapping novel genetic loci associated with female liver weight variations using Collaborative Cross mice

BACKGROUND

Liver weight is a complex trait, controlled by polygenic factors and differs within populations. Dissecting the genetic architecture underlying these variations will facilitate the search for key role candidate genes involved directly in the hepatomegaly process and indirectly involved in related diseases etiology.

METHODS

Liver weight of 506 mice generated from 39 different Collaborative Cross (CC) lines with both sexes at age 20 weeks old was determined using an electronic balance. Genomic DNA of the CC lines was genotyped with high-density single nucleotide polymorphic markers.

RESULTS

Statistical analysis revealed a significant (P < 0.05) variation of liver weight between the CC lines, with broad sense heritability (H 2) of 0.32 and genetic coefficient of variation (CVG) of 0.28. Subsequently, quantitative trait locus (QTL) mapping was performed, and results showed a significant QTL only for females on chromosome 8 at genomic interval 88.61-93.38 Mb (4.77 Mb). Three suggestive QTL were mapped at chromosomes 4, 12 and 13. The four QTL were designated as LWL1-LWL4 referring to liver weight loci 1-4 on chromosomes 8, 4, 12 and 13, respectively.

CONCLUSION

To our knowledge, this report presents, for the first time, the utilization of the CC for mapping QTL associated with baseline liver weight in mice. Our findings demonstrate that liver weight is a complex trait controlled by multiple genetic factors that differ significantly between sexes.

Advances in comparative genetics: influence of genetics on obesity

Obesity has reached epidemic proportions and is recognised as a significant global health problem. Increased food intake and decreased physical activity are traditionally to blame for the development of obesity; however, many variables such as behaviour, diet, environment, social structures and genetics also contribute to this multifactorial disease. Complex interactions among these variables (for example, gene-environment, gene-diet and gene-gene) contribute not only to individual differences in the development of obesity, but also in treatment response. Mouse models have historically played valuable roles in understanding the genetics of traits related to energy balance and obesity. In the present review, we survey past use and examine new advances in mouse models designed to uncover the genetic architecture of obesity and its component traits. We discuss traditional models such as inbred strains and selectively bred lines and their contributions and shortcomings. We consider the evolution of mouse models into more informative resources such as outbred crosses and the Hybrid Mouse Diversity Panel, as well as novel next-generation approaches such as the Collaborative Cross. Moreover, the genetic architecture of voluntary exercise and the interactive relationship between host genetics and the gut microbiome are presented as novel phenotypes that augment studies using body weight and body fat percentage as endpoints. Understanding the intricate network of phenotypic, genotypic and environmental variables that predispose individuals to obesity will elucidate biological networks involved in the development of obesity. Knowledge obtained from advances in mouse models will inform human health and provide insight into inter-individual variability in the aetiology of obesity-related diseases.

Systems genetics analysis of body weight and energy metabolism traits in Drosophila melanogaster

Background

Obesity and phenotypic traits associated with this condition exhibit significant heritability in natural populations of most organisms. While a number of genes and genetic pathways have been implicated to play a role in obesity associated traits, the genetic architecture that underlies the natural variation in these traits is largely unknown. Here, we used 40 wild-derived inbred lines of Drosophila melanogaster to quantify genetic variation in body weight, the content of three major metabolites (glycogen, triacylglycerol, and glycerol) associated with obesity, and metabolic rate in young flies. We chose these lines because they were previously screened for variation in whole-genome transcript abundance and in several adult life-history traits, including longevity, resistance to starvation stress, chill-coma recovery, mating behavior, and competitive fitness. This enabled us not only to identify candidate genes and transcriptional networks that might explain variation for energy metabolism traits, but also to investigate the genetic interrelationships among energy metabolism, behavioral, and life-history traits that have evolved in natural populations.

Results

We found significant genetically based variation in all traits. Using a genome-wide association screen for single feature polymorphisms and quantitative trait transcripts, we identified 337, 211, 237, 553, and 152 novel candidate genes associated with body weight, glycogen content, triacylglycerol storage, glycerol levels, and metabolic rate, respectively. Weighted gene co-expression analyses grouped transcripts associated with each trait in significant modules of co-expressed genes and we interpreted these modules in terms of their gene enrichment based on Gene Ontology analysis. Comparison of gene co-expression modules for traits in this study with previously determined modules for life-history traits identified significant modular pleiotropy between glycogen content, body weight, competitive fitness, and starvation resistance.

Conclusions

Combining a large phenotypic dataset with information on variation in genome wide transcriptional profiles has provided insight into the complex genetic architecture underlying natural variation in traits that have been associated with obesity. Our findings suggest that understanding the maintenance of genetic variation in metabolic traits in natural populations may require that we understand more fully the degree to which these traits are genetically correlated with other traits, especially those directly affecting fitness.

Something old and something new: wedding recombinant inbred lines with traditional line cross analysis increases power to describe gene interactions

In this paper we present a novel approach to quantifying genetic architecture that combines recombinant inbred lines (RIL) with line cross analysis (LCA). LCA is a method of quantifying directional genetic effects (i.e. summed effects of all loci) that differentiate two parental lines. Directional genetic effects are thought to be critical components of genetic architecture for the long term response to selection and as a cause of inbreeding depression. LCA typically begins with two inbred parental lines that are crossed to produce several generations such as F1, F2, and backcrosses to each parent. When a RIL population (founded from the same P1 and P2 as was used to found the line cross population) is added to the LCA, the sampling variance of several nonadditive genetic effect estimates is greatly reduced. Specifically, estimates of directional dominance, additive x additive, and dominance x dominance epistatic effects are reduced by 92%, 94%, and 56% respectively. The RIL population can be simultaneously used for QTL identification, thus uncovering the effects of specific loci or genomic regions as elements of genetic architecture. LCA and QTL mapping with RIL provide two qualitatively different measures of genetic architecture with the potential to overcome weaknesses of each approach alone. This approach provides cross-validation of the estimates of additive and additive x additive effects, much smaller confidence intervals on dominance, additive x additive and dominance x dominance estimates, qualitatively different measures of genetic architecture, and the potential when used together to balance the weaknesses of LCA or RIL QTL analyses when used alone.

Genetic resistance to diet-induced obesity in chromosome substitution strains of mice

Discovery of genes that confer resistance to diseases such as diet-induced obesity could have tremendous therapeutic impact. We previously demonstrated that the C57BL/6J-Chr(A/J)/NaJ panel of chromosome substitution strains (CSSs) is a unique model for studying resistance to diet-induced obesity. In the present study, three replicate CSS surveys showed remarkable consistency, with 13 A/J-derived chromosomes reproducibly conferring resistance to high-fat-diet-induced obesity. Twenty CSS intercrosses, one derived from each of the 19 autosomes and chromosome X, were used to determine the number and location of quantitative trait loci (QTLs) on individual chromosomes and localized six QTLs. However, analyses of mean body weight in intercross progeny versus C57BL/6J provided strong evidence that many QTLs discovered in the CSS surveys eluded detection in these CSS intercrosses. Studies of the temporal effects of these QTLs suggest that obesity resistance was dynamic, with QTLs acting at different ages or after different durations of diet exposure. Thus, these studies provide insight into the genetic architecture of complex traits such as resistance to diet-induced obesity in the C57BL/6J-Chr(A/J)/NaJ CSSs. Because some of the QTLs detected in the CSS intercrosses were not detected using a traditional C57BL/6J x A/J intercross, our results demonstrate that surveys of CSSs and congenic strains derived from them are useful complementary tools for analyzing complex traits.

Genetic analysis of daily physical activity using a mouse chromosome substitution strain

There is considerable evidence for a genetic basis underlying individual differences in spontaneous physical activity in humans and animals. Previous publications indicate that the physical activity level and pattern vary among inbred strains of mice and identified a genomic region on chromosome 13 as quantitative trait loci (QTL) for physical activity. To confirm and further characterize the role of chromosome 13 in regulating daily physical activity level and pattern, we conducted a comprehensive phenotypic study in the chromosome 13 substitution strain (CSS-13) in which the individual chromosome 13 from the A/J strain was substituted into an otherwise complete C57BL/6J (B6) genome. The B6 and A/J parental strains exhibited pronounced differences in daily physical activity, sleep-wake structure, circadian period and body weight. Here we report that a single A/J chromosome 13 in the context of a B6 genetic background conferred a profound reduction in both total cage activity and wheel-running activity under a 14:10-h light-dark cycle, as well as in constant darkness, compared with B6 controls. Additionally, CSS-13 mice differed from B6 controls in the diurnal distribution of activity and the day-to-day variability in activity onset. We further performed a linkage analysis and mapped a significant QTL on chromosome 13 regulating the daily wheel running activity level in mice. Taken together, our findings indicate a QTL on chromosome 13 with dramatic and specific effects on daily voluntary physical activity, but not on circadian period, sleep, or other aspects of activity that are different between B6 and A/J strains.

Genetic architecture of quantitative traits in mice, flies, and humans

We compare and contrast the genetic architecture of quantitative phenotypes in two genetically well-characterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, with that found in our own species from recent successes in genome-wide association studies. We show that the current model of large numbers of loci, each of small effect, is true for all species examined, and that discrepancies can be largely explained by differences in the experimental designs used. We argue that the distribution of effect size of common variants is the same for all phenotypes regardless of species, and we discuss the importance of epistasis, pleiotropy, and gene by environment interactions. Despite substantial advances in mapping quantitative trait loci, the identification of the quantitative trait genes and ultimately the sequence variants has proved more difficult, so that our information on the molecular basis of quantitative variation remains limited. Nevertheless, available data indicate that many variants lie outside genes, presumably in regulatory regions of the genome, where they act by altering gene expression. As yet there are very few instances where homologous quantitative trait loci, or quantitative trait genes, have been identified in multiple species, but the availability of high-resolution mapping data will soon make it possible to test the degree of overlap between species.