Quantitative Trait Loci Effect Sizes Research Articles

Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population.

Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes.

Identification of novel genetic regions associated with resistance to European canker in apple

BackgroundEuropean canker, caused by the fungal pathogen Neonectria ditissima, is an economically damaging disease in apple producing regions of the world – especially in areas with moderate temperatures and high rainfall. The pathogen has a wide host range of hardwood perennial species, causing trunk cankers, dieback and branch lesions in its hosts. Although apple scion germplasm carrying partial resistance to the disease has been described, little is still known of the genetic basis for this quantitative resistance.ResultsResistance to Neonectria ditissima was studied in a multiparental population of apple scions using several phenotyping methods. The studied population consists of individuals from multiple families connected through a common pedigree. The degree of disease of each individual in the population was assessed in three experiments: artificial inoculations of detached dormant shoots, potted trees in a glasshouse and in a replicated field experiment. The genetic basis of the differences in disease was studied using a pedigree-based analysis (PBA). Three quantitative trait loci (QTL), on linkage groups (LG) 6, 8 and 10 were identified in more than one of the phenotyping strategies. An additional four QTL, on LG 2, 5, 15 and 16 were only identified in the field experiment. The QTL on LG2 and 16 were further validated in a biparental population. QTL effect sizes were small to moderate with 4.3 to 19% of variance explained by a single QTL. A subsequent analysis of QTL haplotypes revealed a dynamic response to this disease, in which the estimated effect of a haplotype varied over the field time-points.ConclusionsThis study describes the first identified QTL associated with resistance to N. ditissima in apple scion germplasm. The results from this study show that QTL present in germplasm commonly used in apple breeding have a low to medium effect on resistance to N. ditissima. Hence, multiple QTL will need to be considered to improve resistance through breeding.

Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes.

The biology of how faces are built and come to differ from one another is complex. Discovering normal variants that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal and facial development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL’s peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.

Parallel genomic architecture underlies repeated sexual signal divergence in Hawaiian Laupala crickets.

When the same phenotype evolves repeatedly, we can explore the predictability of genetic changes underlying phenotypic evolution. Theory suggests that genetic parallelism is less likely when phenotypic changes are governed by many small-effect loci compared to few of major effect, because different combinations of genetic changes can result in the same quantitative outcome. However, some genetic trajectories might be favoured over others, making a shared genetic basis to repeated polygenic evolution more likely. To examine this, we studied the genetics of parallel male mating song evolution in the Hawaiian cricket Laupala. We compared quantitative trait loci (QTL) underlying song divergence in three species pairs varying in phenotypic distance. We tested whether replicated song divergence between species involves the same QTL and whether the likelihood of QTL sharing is related to QTL effect size. Contrary to theoretical predictions, we find substantial parallelism in polygenic genetic architectures underlying repeated song divergence. QTL overlapped more frequently than expected based on simulated QTL analyses. Interestingly, QTL effect size did not predict QTL sharing, but did correlate with magnitude of phenotypic divergence. We highlight potential mechanisms driving these constraints on cricket song evolution and discuss a scenario that consolidates empirical quantitative genetic observations with micro-mutational theory.

Determinants of QTL Mapping Power in the Realized Collaborative Cross.

The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses from extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL. We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments.

A High-Resolution Genetic Map for the Laboratory Rat.

An accurate and high-resolution genetic map is critical for mapping complex traits, yet the resolution of the current rat genetic map is far lower than human and mouse, and has not been updated since the original Jensen-Seaman map in 2004. For the first time, we have refined the rat genetic map to sub-centimorgan (cM) resolution (<0.02 cM) by using 95,769 genetic markers and 870 informative meioses from a cohort of 528 heterogeneous stock (HS) rats. Global recombination rates in the revised sex-averaged map (0.66 cM/Mb) did not differ compared to the historical map (0.65 cM/Mb); however, substantial refinement was made to the localization of highly recombinant regions within the revised map. Also for the first time, sex-specific rat genetic maps were generated, which revealed both genomewide and fine-scale variation in recombination rates between male and female rats. Reanalysis of multiple quantitative trait loci (QTL) using the historical and refined rat genetic maps demonstrated marked changes to QTL localization, shape, and effect size. As a resource to the rat research community, we have provided revised centimorgan positions for all physical positions within the rat genome and commonly used genetic markers for trait mapping, including 44,828 SSLP markers and the RATDIV genotyping array. Collectively, this study provides a substantial improvement to the rat genetic map and an unprecedented resource for analysis of complex traits and recombination in the rat.

Whole-genome QTL analysis for MAGIC.

An efficient whole genome method of QTL analysis is presented for Multi-parent advanced generation integrated crosses. Multi-parent advanced generation inter-cross (MAGIC) populations have been developed for mice and several plant species and are useful for the genetic dissection of complex traits. The analysis of quantitative trait loci (QTL) in these populations presents some additional challenges compared with traditional mapping approaches. In particular, pedigree and marker information need to be integrated and founder genetic data needs to be incorporated into the analysis. Here, we present a method for QTL analysis that utilizes the probability of inheriting founder alleles across the whole genome simultaneously, either for intervals or markers. The probabilities can be found using three-point or Hidden Markov Model (HMM) methods. This whole-genome approach is evaluated in a simulation study and it is shown to be a powerful method of analysis. The HMM probabilities lead to low rates of false positives and low bias of estimated QTL effect sizes. An implementation of the approach is available as an R package. In addition, we illustrate the approach using a bread wheat MAGIC population.

GENETIC ARCHITECTURE OF SKELETAL EVOLUTION IN EUROPEAN LAKE AND STREAM STICKLEBACK

Advances in genomic techniques are greatly facilitating the study of molecular signatures of selection in diverging natural populations. Connecting these signatures to phenotypes under selection remains challenging, but benefits from dissections of the genetic architecture of adaptive divergence. We here perform quantitative trait locus (QTL) mapping using 488 F2 individuals and 2011 single nucleotide polymorphisms (SNPs) to explore the genetic architecture of skeletal divergence in a lake-stream stickleback system from Central Europe. We find QTLs for gill raker, snout, and head length, vertebral number, and the extent of lateral plating (plate number and height). Although two large-effect loci emerge, QTL effect sizes are generally small. Examining the neighborhood of the QTL-linked SNPs identifies several genes involved in bone formation, which emerge as strong candidate genes for skeletal evolution. Finally, we use SNP data from the natural source populations to demonstrate that some SNPs linked to QTLs in our cross also exhibit striking allele frequency differences in the wild, suggesting a causal role of these QTLs in adaptive population divergence. Our study paves the way for comparative analyses across other (lake-stream) stickleback populations, and for functional investigations of the candidate genes.

Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple

Proof of concept of Bayesian integrated QTL analyses across pedigree-related families from breeding programs of an outbreeding species. Results include QTL confidence intervals, individuals' genotype probabilities and genomic breeding values. Bayesian QTL linkage mapping approaches offer the flexibility to study multiple full sib families with known pedigrees simultaneously. Such a joint analysis increases the probability of detecting these quantitative trait loci (QTL) and provide insight of the magnitude of QTL across different genetic backgrounds. Here, we present an improved Bayesian multi-QTL pedigree-based approach on an outcrossing species using progenies with different (complex) genetic relationships. Different modeling assumptions were studied in the QTL analyses, i.e., the a priori expected number of QTL varied and polygenic effects were considered. The inferences include number of QTL, additive QTL effect sizes and supporting credible intervals, posterior probabilities of QTL genotypes for all individuals in the dataset, and QTL-based as well as genome-wide breeding values. All these features have been implemented in the FlexQTL(™) software. We analyzed fruit firmness in a large apple dataset that comprised 1,347 individuals forming 27 full sib families and their known ancestral pedigrees, with genotypes for 87 SSR markers on 17 chromosomes. We report strong or positive evidence for 14 QTL for fruit firmness on eight chromosomes, validating our approach as several of these QTL were reported previously, though dispersed over a series of studies based on single mapping populations. Interpretation of linked QTL was possible via individuals' QTL genotypes. The correlation between the genomic breeding values and phenotypes was on average 90 %, but varied with the number of detected QTL in a family. The detailed posterior knowledge on QTL of potential parents is critical for the efficiency of marker-assisted breeding.

Paths to selection on life history loci in different natural environments across the native range of Arabidopsis thaliana

Selection on quantitative trait loci (QTL) may vary among natural environments due to differences in the genetic architecture of traits, environment-specific allelic effects or changes in the direction and magnitude of selection on specific traits. To dissect the environmental differences in selection on life history QTL across climatic regions, we grew a panel of interconnected recombinant inbred lines (RILs) of Arabidopsis thaliana in four field sites across its native European range. For each environment, we mapped QTL for growth, reproductive timing and development. Several QTL were pleiotropic across environments, three colocalizing with known functional polymorphisms in flowering time genes (CRY2, FRI and MAF2-5), but major QTL differed across field sites, showing conditional neutrality. We used structural equation models to trace selection paths from QTL to lifetime fitness in each environment. Only three QTL directly affected fruit number, measuring fitness. Most QTL had an indirect effect on fitness through their effect on bolting time or leaf length. Influence of life history traits on fitness differed dramatically across sites, resulting in different patterns of selection on reproductive timing and underlying QTL. In two oceanic field sites with high prereproductive mortality, QTL alleles contributing to early reproduction resulted in greater fruit production, conferring selective advantage, whereas alleles contributing to later reproduction resulted in larger size and higher fitness in a continental site. This demonstrates how environmental variation leads to change in both QTL effect sizes and direction of selection on traits, justifying the persistence of allelic polymorphism at life history QTL across the species range.

FROM BEAVIS TO BEAK COLOR: A SIMULATION STUDY TO EXAMINE HOW MUCH QTL MAPPING CAN REVEAL ABOUT THE GENETIC ARCHITECTURE OF QUANTITATIVE TRAITS

Quantitative trait locus (QTL) mapping is frequently used in evolutionary studies to understand the genetic architecture of continuously varying traits. The majority of studies have been conducted in specially created crosses, in which genetic differences between parental lines are identified by linkage analysis. Detecting QTL segregating within populations is more problematic, especially in wild populations, because these populations typically have complicated and unbalanced multigenerational pedigrees. However, QTL mapping can still be conducted in such populations using a variance components mixed model approach, and the advent of appropriate statistical frameworks and better genotyping methods mean that the approach is gaining popularity. In this study it is shown that all studies described to date report evidence of QTL of major effect on trait variation, but that these findings are probably caused by inflated estimates of QTL effect sizes due to the Beavis effect. Using simulations I show that even the most powerful studies conducted to date are likely to give misleading descriptions of the genetic architecture of a trait. I show that an interpretation of a mapping study of beak color in the zebra finch (Taeniopygia guttata), that suggested genetic variation was determined by a small number of loci of large effect, which are possibly maintained by antagonistic pleiotropy, is likely to be incorrect. More generally, recommendations are made to how QTL mapping can be combined with other approaches to provide more accurate descriptions of a trait's genetic architecture.

Deviance information criterion (DIC) in Bayesian multiple QTL mapping

Mapping multiple quantitative trait loci (QTL) is commonly viewed as a problem of model selection. Various model selection criteria have been proposed, primarily in the non-Bayesian framework. The deviance information criterion (DIC) is the most popular criterion for Bayesian model selection and model comparison but has not been applied to Bayesian multiple QTL mapping. A derivation of the DIC is presented for multiple interacting QTL models and calculation of the DIC is demonstrated using posterior samples generated by Markov chain Monte Carlo (MCMC) algorithms. The DIC measures posterior predictive error by penalizing the fit of a model (deviance) by its complexity, determined by the effective number of parameters. The effective number of parameters simultaneously accounts for the sample size, the cross design, the number and lengths of chromosomes, covariates, the number of QTL, the type of QTL effects, and QTL effect sizes. The DIC provides a computationally efficient way to perform sensitivity analysis and can be used to quantitatively evaluate if including environmental effects, gene–gene interactions, and/or gene–environment interactions in the prior specification is worth the extra parameterization. The DIC has been implemented in the freely available package R/qtlbim, which greatly facilitates the general usage of Bayesian methodology for genome-wide interacting QTL analysis.

Genome‐wide quantitative trait locus association scan of general cognitive ability using pooled DNA and 500K single nucleotide polymorphism microarrays

General cognitive ability (g), which refers to what cognitive abilities have in common, is an important target for molecular genetic research because multivariate quantitative genetic analyses have shown that the same set of genes affects diverse cognitive abilities as well as learning disabilities. In this first autosomal genome-wide association scan of g, we used a two-stage quantitative trait locus (QTL) design with pooled DNA to screen more than 500 000 single nucleotide polymorphisms (SNPs) on microarrays, selecting from a sample of 7000 7-year-old children. In stage 1, we screened for allele frequency differences between groups pooled for low and high g. In stage 2, 47 SNPs nominated in stage 1 were tested by individually genotyping an independent sample of 3195 individuals, representative of the entire distribution of g scores in the full 7000 7-year-old children. Six SNPs yielded significant associations across the normal distribution of g, although only one SNP remained significant after a false discovery rate of 0.05 was imposed. However, none of these SNPs accounted for more than 0.4% of the variance of g, despite 95% power to detect associations of that size. It is likely that QTL effect sizes, even for highly heritable traits such as cognitive abilities and disabilities, are much smaller than previously assumed. Nonetheless, an aggregated ‘SNP set’ of the six SNPs correlated 0.11 (P < 0.00000003) with g. This shows that future SNP sets that will incorporate many more SNPs could be useful for predicting genetic risk and for investigating functional systems of effects from genes to brain to behavior.

Quantitative trait locus association scan of early reading disability and ability using pooled DNA and 100K SNP microarrays in a sample of 5760 children

Quantitative genetic research suggests that reading disability is the quantitative extreme of the same genetic and environmental factors responsible for normal variation in reading ability. This finding warrants a quantitative trait locus (QTL) strategy that compares low versus high extremes of the normal distribution of reading in the search for QTLs associated with variation throughout the distribution. A low reading ability group (N=755) and a high reading group (N=747) were selected from a representative UK sample of 7-year-olds assessed on two measures of reading that we have shown to be highly heritable and highly genetically correlated. The low and high reading ability groups were each divided into 10 independent DNA pools and the 20 pools were assayed on 100 K single nucleotide polymorphism (SNP) microarrays to screen for the largest allele frequency differences between the low and high reading ability groups. Seventy five of these nominated SNPs were individually genotyped in an independent sample of low (N=452) and high (N=452) reading ability children selected from a second sample of 4258 7-year-olds. Nine of the seventy-five SNPs were nominally significant (P<0.05) in the predicted direction. These 9 SNPs and 14 other SNPs showing low versus high allele frequency differences in the predicted direction were genotyped in the rest of the second sample to test the QTL hypothesis. Ten SNPs yielded nominally significant linear associations in the expected direction across the distribution of reading ability. However, none of these SNP associations accounted for more than 0.5% of the variance of reading ability, despite 99% power to detect them. We conclude that QTL effect sizes, even for highly heritable common disorders and quantitative traits such as early reading disability and ability, might be much smaller than previously considered.

MAPPING QUANTITATIVE TRAIT LOCI UNDERLYING FITNESS-RELATED TRAITS IN A FREE-LIVING SHEEP POPULATION

We searched for quantitative trait loci (QTL) underlying fitness-related traits in a free-living pedigree of 588 Soay sheep in which a genetic map using 251 markers with an average spacing of 15 cM had been established previously. Traits examined included birth date and weight, considered both as maternal and offspring traits, foreleg length, hindleg length, and body weight measured on animals in August and jaw length and metacarpal length measured on cleaned skeletal material. In some cases the data were split to consider different age classes separately, yielding a total of 15 traits studied. Genetic and environmental components of phenotypic variance were estimated for each trait and, for those traits showing nonzero heritability (N= 12), a QTL search was conducted by comparing a polygenic model with a model including a putative QTL. Support for a QTL at genome-wide significance was found on chromosome 11 for jaw length; suggestive QTL were found on chromosomes 2 and 5 (for birth date as a trait of the lamb), 8 (birth weight as a trait of the lamb), and 15 (adult hindleg length). We discuss the prospects for refining estimates of QTL position and effect size in the study population, and for QTL searches in free-living pedigrees in general.

The landscape of genetic complexity across 5,700 gene expression traits in yeast

Many studies have identified quantitative trait loci (QTLs) that contribute to continuous variation in heritable traits of interest. However, general principles regarding the distribution of QTL numbers, effect sizes, and combined effects of multiple QTLs remain to be elucidated. Here, we characterize complex genetics underlying inheritance of thousands of transcript levels in a cross between two strains of Saccharomyces cerevisiae. Most detected QTLs have weak effects, with a median variance explained of 27% for highly heritable transcripts. Despite the high statistical power of the study, no QTLs were detected for 40% of highly heritable transcripts, indicating extensive genetic complexity. Modeling of QTL detection showed that only 3% of highly heritable transcripts are consistent with single-locus inheritance, 17-18% are consistent with control by one or two loci, and half require more than five loci under additive models. Strikingly, analysis of parent and progeny trait distributions showed that a majority of transcripts exhibit transgressive segregation. Sixteen percent of highly heritable transcripts exhibit evidence of interacting loci. Our results will aid design of future QTL mapping studies and may shed light on the evolution of quantitative traits.

Quantitative genetic variation: a post-modern view

It has become commonplace to map individual quantitative trait loci (QTL) in experimental organisms; the means (line-crosses and dense maps of markers) and motivation (the close relationship between continuous physiological traits and common, complex diseases) are self-evident. Progress in mapping human QTL has been more gradual, an inevitable consequence of genetic mapping in a natural population setting. The common objective of these studies has been to understand the molecular mechanisms underlying individual QTL. Recent theoretical and practical advances shift this focus to a more comprehensive or genomic perspective on quantitative variation. Fisher's infinitesimal model of adaptive evolution, which satisfied quantitative geneticists for over 50 years, has been modified in the light of data from QTL mapping experiments in plants and animals. The resulting exponential model provides a pleasing empirical fit to the distribution of QTL effect sizes, predicts that a large amount of quantitative variation will be explained by a limited number of genes and suggests a new mathematical framework for linkage mapping. Molecular analysis of QTL suggests that coding variants (e.g. allozymes) underlie a fraction of quantitative variation and that variants that affect gene expression (expression QTL, eQTL) have a substantial role. This is supported by genomic experiments that combine expression profiling with classical genetic mapping approaches to reveal a remarkable wealth of quantitative heritable variation in the transcriptome and that cis-and trans-acting regulatory factors are organized in networks reflecting pleiotropy. It is hoped that these advances will enhance our understanding of the genetic basis of complex inherited diseases.

Consequences of recombination rate variation on quantitative trait locus mapping studies. Simulations based on the Drosophila melanogaster genome.

We examine the effect of variation in gene density per centimorgan on quantitative trait locus (QTL) mapping studies using data from the Drosophila melanogaster genome project and documented regional rates of recombination. There is tremendous variation in gene density per centimorgan across this genome, and we observe that this variation can cause systematic biases in QTL mapping studies. Specifically, in our simulated mapping experiments of 50 equal-effect QTL distributed randomly across the physical genome, very strong QTL are consistently detected near the centromeres of the two major autosomes, and few or no QTL are often detected on the X chromosome. This pattern persisted with varying heritability, marker density, QTL effect sizes, and transgressive segregation. Our results are consistent with empirical data collected from QTL mapping studies of this species and its close relatives, and they explain the "small X-effect" that has been documented in genetic studies of sexual isolation in the D. melanogaster group. Because of the biases resulting from recombination rate variation, results of QTL mapping studies should be taken as hypotheses to be tested by additional genetic methods, particularly in species for which detailed genetic and physical genome maps are not available.