Jane P. Kenney-Hunt

Pleiotropic scaling of gene effects and the 'cost of complexity'.

As perceived by Darwin, evolutionary adaptation by the processes of mutation and selection is difficult to understand for complex features that are the product of numerous traits acting in concert, for example the eye or the apparatus of flight. Typically, mutations simultaneously affect multiple phenotypic characters. This phenomenon is known as pleiotropy. The impact of pleiotropy on evolution has for decades been the subject of formal analysis. Some authors have suggested that pleiotropy can impede evolutionary progress (a so-called ‘cost of complexity’). The plausibility of various phenomena attributed to pleiotropy depends on how many traits are affected by each mutation and on our understanding of the correlation between the number of traits affected by each gene substitution and the size of mutational effects on individual traits. Here we show, by studying pleiotropy in mice with the use of quantitative trait loci (QTLs) affecting skeletal characters, that most QTLs affect a relatively small subset of traits and that a substitution at a QTL has an effect on each trait that increases with the total number of traits affected. This suggests that evolution of higher organisms does not suffer a ‘cost of complexity’ because most mutations affect few traits and the size of the effects does not decrease with pleiotropy.

Quantitative trait loci for body size components in mice

Do body size components, such as weights of internal organs and long bone lengths, with different functions and different developmental histories also have different genetic architectures and pleiotropic patterns? We examine murine quantitative trait loci (QTL) for necropsy weight, four long bone lengths, and four organ weights in the LG/J × SM/J intercross. Differences between trait categories were found in number of QTL, dominance, and pleiotropic patterns. Ninety-seven QTLs for individual traits were identified: 52 for long bone lengths, 30 for organ weights, and 15 for necropsy weight. Results for long bones are typically more highly significant than for organs. Organ weights were more frequently over- or underdominant than long bone lengths or necropsy weight. The single-trait QTLs map to 35 pleiotropic loci. Long bones are much more frequently affected in groups while organs tend to be affected singly or in pairs. Organs and long bones are found at the same locus in only 11 cases, 8 of which also include necropsy weight. Our results suggest mainly separate genetic modules for organ weights and long bone lengths, with a few loci that affect overall body size. Antagonistic pleiotropy, in which a locus has opposite effects on different characteristics, is uncommon.

Diet-Dependent Genetic and Genomic Imprinting Effects on Obesity in Mice

Although the current obesity epidemic is of environmental origin, there is substantial genetic variation in individual response to an obesogenic environment. In this study, we perform a genome‐wide scan for quantitative trait loci (QTLs) affecting obesity per se, or an obese response to a high‐fat diet in mice from the LG/J by SM/J Advanced Intercross (AI) Line (Wustl:LG, SM‐G16). A total of 1,002 animals from 78 F16 full sibships were weaned at 3 weeks of age and half of each litter placed on high‐ and low‐fat diets. Animals remained on the diet until 20 weeks of age when they were necropsied and the weights of the reproductive, kidney, mesenteric, and inguinal fat depots were recorded. Effects on these phenotypes, along with total fat depot weight and carcass weight at necropsy, were mapped across the genome using 1,402 autosomal single‐nucleotide polymorphism (SNP) markers. Haplotypes were reconstructed and additive, dominance, and imprinting genotype scores were derived every 1 cM along the F16 map. Analysis was performed using a mixed model with additive, dominance, and imprinting genotype scores, their interactions with sex, diet, and with sex‐by‐diet as fixed effects and with family and its interaction with sex, diet, and sex‐by‐diet as random effects. We discovered 95 trait‐specific QTLs mapping to 40 locations. Most QTLs had additive effects with dominance and imprinting effects occurring at two‐thirds of the loci. Nearly every locus interacted with sex and/or diet in important ways demonstrating that gene effects are primarily context dependent, changing depending on sex and/or diet.

The importance of context to the genetic architecture of diabetes-related traits is revealed in a genome-wide scan of a LG/J × SM/J murine model.

Variations in diabetic phenotypes are caused by complex interactions of genetic effects, environmental factors, and the interplay between the two. We tease apart these complex interactions by examining genome-wide genetic and epigenetic effects on diabetes-related traits among different sex, diet, and sex-by-diet cohorts in a Mus musculus model. We conducted a genome-wide scan for quantitative trait loci that affect serum glucose and insulin levels and response to glucose stress in an F16 Advanced Intercross Line of the LG/J and SM/J intercross (Wustl:LG,SM-G16). Half of each sibship was fed a high-fat diet and half was fed a relatively low-fat diet. Context-dependent genetic (additive and dominance) and epigenetic (parent-of-origin imprinting) effects were characterized by partitioning animals into sex, diet, and sex-by-diet cohorts. We found that different cohorts often have unique genetic effects at the same loci, and that genetic signals can be masked or erroneously assigned to specific cohorts if they are not considered individually. Our data demonstrate that the effects of genes on complex trait variation are highly context-dependent and that the same genomic sequence can affect traits differently depending on an individual’s sex and/or dietary environment. Our results have important implications for studies of complex traits in humans.

Genetic, epigenetic, and gene-by-diet interaction effects underlie variation in serum lipids in a LG/JxSM/J murine model.

Variation in serum cholesterol, free-fatty acids, and triglycerides is associated with cardiovascular disease (CVD) risk factors. There is great interest in characterizing the underlying genetic architecture of these risk factors, because they vary greatly within and among human populations and between the sexes. We present results of a genome-wide scan for quantitative trait loci (QTL) affecting serum cholesterol, free-fatty acids, and triglycerides in an F16 advanced intercross line of LG/J and SM/J (Wustl:LG,SM-G16). Half of the population was fed a high-fat diet and half was fed a relatively low-fat diet. Context-dependent genetic (additive and dominance) and epigenetic (imprinting) effects were characterized by partitioning animals into sex, diet, and sex-by-diet cohorts. Here we examine genetic, environmental, and genetic-by-environmental interactions of QTL overlapping previously identified loci associated with CVD risk factors, and we add to the serum lipid QTL landscape by identifying new loci.

Genetic variation and correlation of dietary response in an advanced intercross mouse line produced from two divergent growth lines

Levels of human obesity have increased over the past 20 years worldwide, primarily due to changes in diet and activity levels. Although environmental changes are clearly responsible for the increasing prevalence of obesity, individuals may show genetic variation in their response to an obesogenic environment. Here, we measure genetic variation in response to a high-fat diet in a mouse model, an F16 Advanced Intercross Line derived from the cross of SM/J and LG/J inbred mouse strains. The experimental population was separated by sex and fed either a high-fat (42% of energy from fat) or low-fat (15% of energy from fat) diet. A number of phenotypic traits related to obesity and diabetes such as growth rate, glucose tolerance traits, organ weights and fat pad weights were collected and analysed in addition to serum levels of insulin, free fatty acids, cholesterol and triglycerides. Most traits are different between the sexes and between dietary treatments and for a few traits, including adult growth, fat pad weights, insulin and glucose tolerance, the dietary effect is stronger in one sex than the other. We find that fat pad weights, liver weight, serum insulin levels and adult growth rates are all phenotypically and genetically correlated with one another in both dietary treatments. Critically, these traits have relatively low genetic correlations across environments (average r =0.38). Dietary responses are also genetically correlated across these traits. We found substantial genetic variation in dietary response and low cross environment genetic correlations for traits aligned with adiposity. Therefore, genetic effects for these traits are different depending on the environment an animal is exposed to.

DIFFERENTIAL DOMINANCE OF PLEIOTROPIC LOCI FOR MOUSE SKELETAL TRAITS

The term “differential dominance” describes the situation in which the dominance effects at a pleiotropic locus vary between traits. Directional selection on the phenotype can lead to balancing selection on differentially dominant pleiotropic loci. Even without any individual overdominant traits, some linear combination of traits will display overdominance at a locus displaying differential dominance. Multivariate overdominance may be responsible, in part, for high levels of heterozygosity found in natural populations. We examine differential dominance of 70 mouse skeletal traits at 92 quantitative trait loci (QTL). Our results indicate moderate to strong additive and dominance effects at pleiotropic loci, low levels of individual-trait overdominance, and universal multivariate overdominance. Multivariate overdominance affects a range of 6% to 81% of morphospace, with a mean of 32%. Multivariate overdominance tends to affect a larger percentage of morphospace at pleiotropic loci with antagonistic effects on multiple traits (42%). We conclude that multivariate overdominance is common and should be considered in models and in empirical studies of the role of genetic variation in evolvability.

Weak genetic relationship between trabecular bone morphology and obesity in mice

Obesity, in addition to being associated with metabolic diseases, such as diabetes, has also been found to lower the risk of osteoporotic fractures. The relationship between obesity and bone trabecular structure is complex, involving responses to mechanical loading and the effects of adipocyte-derived hormones, both directly interacting with bone tissue and indirectly through central nervous system signaling. Here we examine the effects of sex, a high fat diet, and genetics on the trabecular density and structure of the lumbar and caudal vertebra and the proximal tibia along with body weight, fat pad weight, and serum leptin levels in a murine obesity model, the LGXSM recombinant inbred (RI) mouse strains. The sample included 481 mice from 16 RI strains. We found that vertebral trabecular density was higher in males while the females had higher tibial trabecular density. The high fat diet led to only slightly higher trabecular density in both sexes despite its extreme effects on obesity and serum leptin levels. Trait heritabilities are moderate to strong and genetic correlations among trabecular features are high. Most genetic variation contrasts strains with large numbers of thick, closely-spaced, highly interconnected, plate-like trabeculae with a high bone volume to total volume ratio against strains displaying small numbers of thin, widely-spaced, sparsely connected, rod-like trabeculae with a low bone volume to total volume ratio. Genetic correlations between trabecular and obesity-related traits were low and not statistically significant. We mapped trabecular properties to 20 genomic locations. Only one-quarter of these locations also had effects on obesity. In this population obesity has a relatively minor effect on trabecular bone morphology.

Quantitative Trait Loci Affecting Liver Fat Content in Mice

Nonalcoholic fatty liver disease, a condition in which excess fat accumulates in the liver, is strongly associated with the metabolic syndrome, including obesity and other related conditions. This disease has the potential to progress from steatosis to steatohepatitis, fibrosis, and cirrhosis. The recent increase in the prevalence of the metabolic syndrome is largely driven by changes in diet and activity levels. Individual variation in the response to this obesogenic environment, however, is attributable in part to genetic variation between individuals, but very few mammalian genetic loci have been identified with effects on fat accumulation in the liver. To study the genetic basis for variation in liver fat content in response to dietary fat, liver fat proportion was determined using quantitative magnetic resonance imaging in 478 mice from 16 LG/J X SM/J recombinant inbred strains fed either a high-fat (42% kcal from fat) or low-fat (15% kcal from fat) diet. An analysis of variance confirmed that there is a genetic basis for variation in liver fat content within the population with significant effects of sex and diet. Three quantitative trail loci that contribute to liver fat content also were mapped.

Wagner et al. reply

Reply to: J. Hermisson & A. P. McGregor 456, 10.1038/nature07452 (2008)In our paper on pleiotropic scaling and the cost of complexity, we presented evidence for three findings: first, most genes affect a small number of traits (the degree of pleiotropy is low); second, the total effect of a quantitative trait locus (QTL) increases with the degree of pleiotropy, refuting the constant total effect model; and third, the increase in total effect (defined as , where Ai is the effect on character i, that is, half the difference between the genotypic values of the homozygous genotypes) seems to be stronger than predicted by the superposition model of pleiotropic effects. Hermisson and McGregor point out that the last result could be due to multiple mutations being mapped to the same QTL, but only if these mutations affect overlapping sets of traits. We agree that this is a possibility that we could not address with the data at hand.