Further mapping of quantitative trait loci for postnatal growth in an inter-sub-specific backcross of wild Mus musculus castaneus and C57BL/6J mice

We performed a quantitative trait locus (QTL) analysis of eight body weights recorded weekly from 3 weeks to 10 weeks after birth and two weight gains recorded between 3 weeks and 6 weeks, and between 6 weeks and 10 weeks in an inter-sub-specific backcross population of wild Mus musculus castaneus mice captured in the Philippines and the common inbred strain C57BL/6J ( M. musculus domesticus ), to elucidate the complex genetic architecture of body weight and growth. Interval mapping identified 17 significant QTLs with main effects on 11 chromosomes. In particular, the main effect of the most potent QTL on proximal chromosome 2 increased linearly with age, whereas other QTLs exerted effects on either the early or late growth period. Surprisingly, although wild mice displayed 60% of the body size of their C57BL/6J counterparts, the wild-derived allele enhanced growth at two QTLs. Interestingly, five of the 17 main-effect QTLs identified had significant epistatic interaction effects. Five new epistatic QTLs with no main effects were identified on different chromosomes or regions. For one pair of epistatic QTLs, mice that were heterozygous for the wild-derived allele at one QTL and homozygous for that allele at another QTL exhibited the most rapid growth in all four possible genotypic combinations. Out of the identified QTLs, several showed significant sex-specific effects.     

Characterization of epistasis influencing complex spontaneous obesity in the BSB model

There is growing awareness that complex interactions among multiple genes and environmental factors play an important role in controlling obesity traits. The BSB mouse, which is produced by the backcross of (lean C57BL/6J x lean Mus spretus) x C57BL/6J, provides an excellent model of epistatic obesity. To evaluate potential epistatic interactions among six chromosomal regions previously determined to influence obesity phenotypes, we performed novel Bayesian analyses on the basis of both epistatic and nonepistatic models for four obesity traits: percentage of body fat, adiposity index, total fat mass, and body weight, and also for plasma total cholesterol. The epistatic analysis detected at least one more QTL than the nonepistatic analysis did for all obesity traits. These obesity traits were variously influenced by QTL on chromosomes 2, 7, 12, 15, and 16. Interaction between genes on chromosomes 2 and 12 was present for all obesity traits, accounting for 3-4.8% of the phenotypic variation. Chromosome 12 was found to have weak main effects on all obesity traits. Several different epistatic interactions were also detected for percentage of body fat, adiposity index, and total fat mass. Chromosomes 6 and 12 have not only main effects but also strong epistatic effects on plasma total cholesterol. Our results emphasize the importance of modeling epistasis for discovery of obesity genes.     

Bayesian model selection for genome-wide epistatic quantitative trait loci analysis

The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.     

QTLs for pre- and postweaning body weight and body composition in selected mice

In an intercross between the high-body-weight-selected mouse line NMRI8 and the inbred line DBA/2, we analyzed genetic effects on growth during the suckling period and after weaning during the juvenile phase of development. QTL mapping results indicated that a switch of gene activation might occur at the age of three weeks when animals are weaned. We found QTLs for body weight with major effects at the age of two and three weeks when animals are fed by their mothers, and QTLs with highest effects after weaning when animals have to live on their own under ad libitum access to food. Specific epistatic effects on body weight at two and three weeks and epistatic interaction influencing growth after weaning support this finding. QTL effects explained the greatest variance during puberty when animals grow fastest and become fertile. In the present study, all except one QTL effect for early body weight had dominance variance components. These might result from direct single-locus-dominant allelic expression, but also from the identified epistatic interaction between different QTLs that we have found for body weight at all ages. Beside body weight, body composition traits (muscle weight, reproductive fat weight, weight of inner organs) were analyzed. Sex-dimorphic QTLs were found for body weight and fat deposition. The identified early-growth QTLs could be the target of epigenetic modifications which might influence body weight at later ages.     

Epistatic interaction between two nonstructural loci on chromosomes 7 and 3 influences hepatic lipase activity in BSB mice

BSB mice exhibit a wide range of obesity despite being produced by a backcross of lean C57BL/6J (B) x lean Mus spretus (SPRET/Pt) F1 animals x B. Previous linkage studies identified a quantitative trait locus (QTL) on mouse chromosome 7 with coincident peaks for hepatic lipase activity, obesity, and plasma cholesterol. However, these mice were not analyzed for gene x gene epistasis. Hepatic lipase activity is correlated with obesity and plasma cholesterol levels. In this study, we identified QTLs for plasma hepatic lipase activity with three statistical mapping methods: maximum likelihood interval mapping, Bayesian nonepistatic mapping, and Bayesian epistatic mapping. Bayesian epistatic mapping detected not only the QTL on chromosome 7 but also an additional QTL on chromosome 3, which has a weak main effect but a strong interaction with chromosome 7. SPRET/Pt alleles of the QTL on each chromosome promote hepatic lipase activity. The proportion of phenotypic variance explained by the epistatic effect is higher than that explained by the main effect of the QTL on chromosome 7.     

Genotype × diet interactions in mice predisposed to mammary cancer. I. Body weight and fat

High dietary fat intake and obesity may increase susceptibility to certain forms of cancer. To study the interactions of dietary fat, obesity, and metastatic mammary cancer, we created a population of F₂ mice cosegregating obesity QTL and the MMTV-PyMT transgene. We fed the F₂ mice either a very-high-fat or a matched-control-fat diet and measured growth, body composition, age at mammary tumor onset, tumor number and severity, and formation of pulmonary metastases. SNP genotyping across the genome facilitated analyses of QTL and QTL × diet interaction effects. Here we describe development of the F₂ population (n = 615) which resulted from a cross between the polygenic obesity model M16i and FVB/NJ-TgN (MMTV-PyMT)^634Mul, effects of diet on growth and body composition, and QTL and QTL × diet and/or gender interaction effects for growth and obesity-related phenotypes. We identified 38 QTL for body composition traits that were significant at the genome-wide 0.05 level, likely representing nine distinct loci after accounting for pleiotropic effects. QTL × diet and/or gender interactions were present at 15 of these QTL, indicating that such interactions play a significant role in defining the genetic architecture of complex traits such as body weight and obesity.     

Characterization of a major X-linked quantitative trait locus influencing body weight of mice

Growth rate in mice is an archetypal quantitative trait that has long been studied genetically, physiologically, and metabolically, but its genetic basis is still poorly understood due to its complex inheritance and the influence of environment. We measured differences in 17 growth-related traits between a pair of partially congenic lines that differ for a segment of the X chromosome containing a quantitative trait locus (QTL) that we identified in a genomewide QTL scan. The QTL has a large effect on mean body weight of approximately 20% at all ages, and affects early growth rate to a greater extent than late growth rate. Feed is converted to body mass more efficiently in the high chromosome segment-bearing line than the low line. The weights of various internal organs are affected to a somewhat greater extent by the QTL than body weight. The proportional change in body length is smaller than body weight, but this may be an effect of scale. Body weight at late ages appears to allow the most efficient detection of allelic differences at the QTL, although assignment of genotypic state based on phenotype is never unambiguous.     

Bayesian analysis for genetic architecture of dynamic traits

The dissection of the genetic architecture of quantitative traits, including the number and locations of quantitative trait loci (QTL) and their main and epistatic effects, has been an important topic in current QTL mapping. We extend the Bayesian model selection framework for mapping multiple epistatic QTL affecting continuous traits to dynamic traits in experimental crosses. The extension inherits the efficiency of Bayesian model selection and the flexibility of the Legendre polynomial model fitting to the change in genetic and environmental effects with time. We illustrate the proposed method by simultaneously detecting the main and epistatic QTLs for the growth of leaf age in a doubled-haploid population of rice. The behavior and performance of the method are also shown by computer simulation experiments. The results show that our method can more quickly identify interacting QTLs for dynamic traits in the models with many numbers of genetic effects, enhancing our understanding of genetic architecture for dynamic traits. Our proposed method can be treated as a general form of mapping QTL for continuous quantitative traits, being easier to extend to multiple traits and to a single trait with repeat records.     

Simultaneous mapping of epistatic QTL in DU6i × DBA/2 mice

We have mapped epistatic quantitative trait loci (QTL) in an F₂ cross between DU6i × DBA/2 mice. By including these epistatic QTL and their interaction parameters in the genetic model, we were able to increase the genetic variance explained substantially (8.8%–128.3%) for several growth and body composition traits. We used an analysis method based on a simultaneous search for epistatic QTL pairs without assuming that the QTL had any effect individually. We were able to detect several QTL that could not be detected in a search for marginal QTL effects because the epistasis cancelled out the individual effects of the QTL. In total, 23 genomic regions were found to contain QTL affecting one or several of the traits and eight of these QTL did not have significant individual effects. We identified 44 QTL pairs with significant effects on the traits, and, for 28 of the pairs, an epistatic QTL model fit the data significantly better than a model without interactions. The epistatic pairs were classified by the significance of the epistatic parameters in the genetic model, and visual inspection of the two-locus genotype means identified six types of related genotype–phenotype patterns among the pairs. Five of these patterns resembled previously published patterns of QTL interactions.     

Bayesian mapping of genomewide interacting quantitative trait loci for ordinal traits

Development of statistical methods and software for mapping interacting QTL has been the focus of much recent research. We previously developed a Bayesian model selection framework, based on the composite model space approach, for mapping multiple epistatic QTL affecting continuous traits. In this study we extend the composite model space approach to complex ordinal traits in experimental crosses. We jointly model main and epistatic effects of QTL and environmental factors on the basis of the ordinal probit model (also called threshold model) that assumes a latent continuous trait underlies the generation of the ordinal phenotypes through a set of unknown thresholds. A data augmentation approach is developed to jointly generate the latent data and the thresholds. The proposed ordinal probit model, combined with the composite model space framework for continuous traits, offers a convenient way for genomewide interacting QTL analysis of ordinal traits. We illustrate the proposed method by detecting new QTL and epistatic effects for an ordinal trait, dead fetuses, in a F(2) intercross of mice. Utility and flexibility of the method are also demonstrated using a simulated data set. Our method has been implemented in the freely available package R/qtlbim, which greatly facilitates the general usage of the Bayesian methodology for genomewide interacting QTL analysis for continuous, binary, and ordinal traits in experimental crosses.     

The contribution of epistatic pleiotropy to the genetic architecture of covariation among polygenic traits in mice

The contribution that pleiotropic effects of individual loci make to covariation among traits is well understood theoretically and is becoming well documented empirically. However, little is known about the role of epistasis in determining patterns of covariation among traits. To address this problem we combine a quantitative trait locus (QTL) analysis with a two-locus model to assess the contribution of epistasis to the genetic architecture of variation and covariation of organ weights and limb bone lengths in a backcross population of mice created from the M16i and CAST/Ei strains. Significant epistasis was exhibited by 14 pairwise combinations of QTL for organ weights and 10 combinations of QTL for limb bone lengths, which contributed, on average, about 5% of the variation in organ weights and 8% in limb bone lengths beyond that of single-locus QTL effects. Epistatic pleiotropy was much more common in the limb bones (seven of 10 epistatic combinations affecting limb bone lengths were pleiotropic) than the organs (three of the 14 epistatic combinations affecting organ weights were pleiotropic). In both cases, epistatic pleiotropy was less common than single-locus pleiotropy. Epistatic pleiotropy accounted for an average of 6% of covariation among organ weights and 21% of covariation among limb bone lengths, which represented an average of one-fifth (for organ weights) and one-third (for limb bone lengths) of the total genetic covariance between traits. Thus, although epistatic pleiotropy made a smaller contribution than single-locus pleiotropy, it clearly made a significant contribution to the genetic architecture of variation/covariation.     

Multiple-trait QTL mapping for body and organ weights in a cross between NMRI8 and DBA/2 mice

Multiple-trait analyses have been shown to improve the detection of quantitative trait loci (QTLs) with multiple effects. Here we applied a multiple-trait approach on obesity- and growth-related traits that were surveyed in 275 F2 mice generated from an intercross between the high body weight selected line NMRI8 and DBA/2 as lean control. The parental lines differed 2.5-fold in body weight at the age of 6 weeks. Within the F2 population, the correlations between body weight and weights of abdominal fat weight, muscle, liver and kidney at the age of 6 weeks were about 0.8. A least squares multiple-trait QTL analysis was performed on these data to understand more precisely the cause of the genetic correlation between body weight, body composition traits and weights of inner organs. Regions on Chr 1, 2, 7 and 14 for body weights at different early ages and regions on Chr 1, 2, 4, 7, 14, 17 and 19 for organ weights at 6 weeks were found to have significant multiple effects at the genome-wide level.     

Epistatic QTL pairs associated with meat quality and carcass composition traits in a porcine Duroc × Pietrain population

Background

Quantitative trait loci (QTL) analyses in pig have revealed numerous individual QTL affecting growth, carcass composition, reproduction and meat quality, indicating a complex genetic architecture. In general, statistical QTL models consider only additive and dominance effects and identification of epistatic effects in livestock is not yet widespread. The aim of this study was to identify and characterize epistatic effects between common and novel QTL regions for carcass composition and meat quality traits in pig.

Methods

Five hundred and eighty five F₂ pigs from a Duroc × Pietrain resource population were genotyped using 131 genetic markers (microsatellites and SNP) spread over the 18 pig autosomes. Phenotypic information for 26 carcass composition and meat quality traits was available for all F₂ animals. Linkage analysis was performed in a two-step procedure using a maximum likelihood approach implemented in the QxPak program.

Results

A number of interacting QTL was observed for different traits, leading to the identification of a variety of networks among chromosomal regions throughout the porcine genome. We distinguished 17 epistatic QTL pairs for carcass composition and 39 for meat quality traits. These interacting QTL pairs explained up to 8% of the phenotypic variance.

Conclusions

Our findings demonstrate the significance of epistasis in pigs. We have revealed evidence for epistatic relationships between different chromosomal regions, confirmed known QTL loci and connected regions reported in other studies. Considering interactions between loci allowed us to identify several novel QTL and trait-specific relationships of loci within and across chromosomes.     

Bayesian model choice and search strategies for mapping interacting quantitative trait Loci

Most complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.     

High throughput analyses of epistasis for swine body dimensions and organ weights

High throughput analyses were performed to detect epistatic QTL in 17 body dimension and organ weight traits from a large F₂ pig population derived from a White Duroc and Erhualian intercross. The analyses used a nested test framework to handle multiple tests and a combined search algorithm to map epistatic QTL with empirical genome‐wide thresholds derived via prior permutation. Alternative statistical models (e.g. including vs. excluding carcass weight as a covariate) were tested to develop an in‐depth understanding of the role of epistasis in these kinds of traits. Epistasis signals were detected in only two or three traits under each statistical model studied. The interaction component of each pair of epistatic QTL explained a small proportion (0.7 to 2.1%) of the phenotypic variance in general. About half of the detected epistatic QTL pairs involved one of the two major QTL on porcine chromosomes 7 and 4. In those traits, the Erhualian allele consistently increased the phenotypes for the chromosome 7 QTL but decreased them for the chromosome 4 QTL. Models including carcass weight as covariate detected epistasis in body dimension traits whereas those excluding carcass weight found epistasis in organ weight traits. In addition, the epistasis results suggested that a QTL on chromosome 14 could be important for a number of organ weight traits. Using the high‐throughput analysis tool to examine different statistical models was essential for the generation of a complete picture of epistasis in a whole category of traits.     

Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse

The extent to which epistasis contributes to adaptation, population differentiation, and speciation is a long-standing and important problem in evolutionary genetics. Using recombinant inbred (RI) lines of Arabidopsis thaliana grown under natural field conditions, we have examined the genetic architecture of fitness-correlated traits with respect to epistasis; we identified both single-locus additive and two-locus epistatic QTL for natural variation in fruit number, germination, and seed length and width. For fruit number, we found seven significant epistatic interactions, but only two additive QTL. For seed germination, length, and width, there were from two to four additive QTL and from five to eight epistatic interactions. The epistatic interactions were both positive and negative. In each case, the magnitude of the epistatic effects was roughly double that of the effects of the additive QTL, varying from -41% to +29% for fruit number and from -5% to +4% for seed germination, length, and width. A number of the QTL that we describe participate in more than one epistatic interaction, and some loci identified as additive also may participate in an epistatic interaction; the genetic architecture for fitness traits may be a network of additive and epistatic effects. We compared the map positions of the additive and epistatic QTL for germination, seed width, and seed length from plants grown in both the field and the greenhouse. While the total number of significant additive and epistatic QTL was similar under the two growth conditions, the map locations were largely different. We found a small number of significant epistatic QTL x environment effects when we tested directly for them. Our results support the idea that epistatic interactions are an important part of natural genetic variation and reinforce the need for caution in comparing results from greenhouse-grown and field-grown plants.     

An efficient Bayesian model selection approach for interacting quantitative trait loci models with many effects

We extend our Bayesian model selection framework for mapping epistatic QTL in experimental crosses to include environmental effects and gene-environment interactions. We propose a new, fast Markov chain Monte Carlo algorithm to explore the posterior distribution of unknowns. In addition, we take advantage of any prior knowledge about genetic architecture to increase posterior probability on more probable models. These enhancements have significant computational advantages in models with many effects. We illustrate the proposed method by detecting new epistatic and gene-sex interactions for obesity-related traits in two real data sets of mice. Our method has been implemented in the freely available package R/qtlbim (http://www.qtlbim.org) to facilitate the general usage of the Bayesian methodology for genomewide interacting QTL analysis.     

Epistasis and the evolutionary dynamics of measured genotypic values during simulated serial bottlenecks

The evolutionary effects of epistasis have been primarily explored analytically and most empirical studies have utilized yeast, viral and bacterial populations. Empirical analyses in multi‐cellular organisms are rare because of experimental constraints. Here, we report the results of a genome‐wide scan for two‐way epistasis in 16 traits related to body size and composition in F₂ mice from the LG/J by SM/J intercross. We analyze two‐locus genotypic values at quantitative trait loci (QTL), which provides an especially detailed view of epistatic architectures, to evaluate their predicted evolutionary consequences via Monte Carlo simulations. Epistatic profiles vary, but all traits show complicated genetic architectures which are largely hidden in single locus QTL scans. On average, detected epistatic effects are comparable in size to marginal effects. Simulations demonstrate an expected preservation, and often inflation, of heritable variance across several generations of small effective population size for many identified epistatic pairs over a range of starting allele frequencies.     

Detection of quantitative trait loci for body weights and conformation traits in Beijing ducks

Quantitative trait loci (QTL) for body weights and conformation traits were detected in Beijing ducks. Traits included body weights (BW) at hatching and at 1-7 weeks of age; lengths of the body (BL), keel bone (KBL), shank (SL) and neck (NL) at 7 weeks of age; width of breast (BTW) at 7 weeks; and girths of shank (SG) and breast (BG) at 7 weeks. Using a half-sib analysis with a multiple-QTL model, linkage between the phenotypic traits and 95 microsatellite markers was studied. Six genome-wide suggestive QTL for three body weights and two conformation traits were identified in CAU1, CAU2, CAU6 and CAU12. Chromosome-wide significant QTL influencing one body weight trait and one conformation trait were located in CAU4 and CAU10 respectively. Twelve chromosome-wide suggestive QTL for six body weight traits and four conformation traits were found in seven linkage groups (CAU1, CAU2, CAU3, CAU4, CAU6, CAU10 and CAU12). In addition, the QTL in CAU6 at 21 and 73 cM jointly affected SG and explained 10.6% of the phenotypic variation. This study provides the first evidence for QTL involved in body weights and conformation traits in ducks, and will stimulate further investigations into the genetic architecture of these traits in this species.     

Simultaneous mapping of epistatic QTL in chickens reveals clusters of QTL pairs with similar genetic effects on growth

We used simultaneous mapping of interacting quantitative trait locus (QTL) pairs to study various growth traits in a chicken F2 intercross. The method was shown to increase the number of detected QTLs by 30 % compared with a traditional method detecting QTLs by their marginal genetic effects. Epistasis was shown to be an important contributor to the genetic variance of growth, with the largest impact on early growth (before 6 weeks of age). There is also evidence for a discrete set of interacting loci involved in early growth, supporting the previous findings of different genetic regulation of early and late growth in chicken. The genotype-phenotype relationship was evaluated for all interacting QTL pairs and 17 of the 21 evaluated QTL pairs could be assigned to one of four clusters in which the pairs in a cluster have very similar genetic effects on growth. The genetic effects of the pairs indicate commonly occurring dominance-by-dominance, heterosis and multiplicative interactions. The results from this study clearly illustrate the increase in power obtained by using this novel method for simultaneous detection of epistatic QTL, and also how visualization of genotype-phenotype relationships for epistatic QTL pairs provides new insights to biological mechanisms underlying complex traits.