Genetic Architecture Of Quantitative Traits Research Articles

Orthogonal contrast based models for quantitative genetic analysis in autotetraploid species.

Dissecting the genetic architecture of quantitative traits is a crucial goal for efficient breeding of polyploid plants, including autotetraploid crop species, such as potato and coffee, and ornamentals such as rose. To meet this goal, a quantitative genetic model is needed to link the genetic effects of genes or genotypes at quantitative trait loci (QTL) to the phenotype of quantitative traits. We present a statistically tractable quantitative genetic model for autotetraploids based on orthogonal contrast comparisons in the general linear model. The new methods are suitable for autotetraploid species with any population genetic structure and take full account of the essential features of autotetrasomic inheritance. The statistical properties of the new methods are explored and compared to an alternative method in the literature by simulation studies. We have shown how these methods can be applied for quantitative genetic analysis in autotetraploids by analysing trait phenotype data from an autotetraploid potato segregating population. Using trait segregation analysis, we showed that both highly heritable traits of flowering time and plant height were under the control of major QTL. The orthogonal model directly dissects genetic variance into independent components and gives consistent estimates of genetic effects provided that tetrasomic gene segregation is considered.

Additive genetic variation in the craniofacial skeleton of baboons (genus Papio) and its relationship to body and cranial size.

Determining the genetic architecture of quantitative traits and genetic correlations among them is important for understanding morphological evolution patterns. We address two questions regarding papionin evolution: (1) what effect do body and cranial size, age, and sex have on phenotypic (VP ) and additive genetic (VA ) variation in baboon crania, and (2) how might additive genetic correlations between craniofacial traits and body mass affect morphological evolution? We use a large captive pedigreed baboon sample to estimate quantitative genetic parameters for craniofacial dimensions (EIDs). Our models include nested combinations of the covariates listed above. We also simulate the correlated response of a given EID due to selection on body mass alone. Covariates account for 1.2-91% of craniofacial VP . EID VA decreases across models as more covariates are included. The median genetic correlation estimate between each EID and body mass is 0.33. Analysis of the multivariate response to selection reveals that observed patterns of craniofacial variation in extant baboons cannot be attributed solely to correlated response to selection on body mass, particularly in males. Because a relatively large proportion of EID VA is shared with body mass variation, different methods of correcting for allometry by statistically controlling for size can alter residual VP patterns. This may conflate direct selection effects on craniofacial variation with those resulting from a correlated response to body mass selection. This shared genetic variation may partially explain how selection for increased body mass in two different papionin lineages produced remarkably similar craniofacial phenotypes.

Charting the genotype-phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel.

Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome-wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P-values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex-specific and genetic background-specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies.

Inference on the Genetic Basis of Eye and Skin Color in an Admixed Population via Bayesian Linear Mixed Models.

Genetic association studies in admixed populations are underrepresented in the genomics literature, with a key concern for researchers being the adequate control of spurious associations due to population structure. Linear mixed models (LMMs) are well suited for genome-wide association studies (GWAS) because they account for both population stratification and cryptic relatedness and achieve increased statistical power by jointly modeling all genotyped markers. Additionally, Bayesian LMMs allow for more flexible assumptions about the underlying distribution of genetic effects, and can concurrently estimate the proportion of phenotypic variance explained by genetic markers. Using three recently published Bayesian LMMs, Bayes R, BSLMM, and BOLT-LMM, we investigate an existing data set on eye (n = 625) and skin (n = 684) color from Cape Verde, an island nation off West Africa that is home to individuals with a broad range of phenotypic values for eye and skin color due to the mix of West African and European ancestry. We use simulations to demonstrate the utility of Bayesian LMMs for mapping loci and studying the genetic architecture of quantitative traits in admixed populations. The Bayesian LMMs provide evidence for two new pigmentation loci: one for eye color (AHRR) and one for skin color (DDB1).

Genomic accuracy in different genetic architecture and genomic structure

Genomic selection has been widely implemented in national and international genetic evaluation in the animal industry, because of its potential advantages over traditional selection methods and the availability of commercial high density single nucleotide polymorphism (SNP) panels.Considerable uncertainty currently exists in determining which genome-wide evaluation method is the most appropriate. We hypothesize that genome-wide methods deal differently with the genetic architecture of quantitative traits and genomes. A genomic linear unbiased prediction method (GBLUP) and a genomic nonlinear Bayesian variable selection methods (BayesA and BayesB) were compared using stochastic simulation across three effective population sizes (Ne). Thereby, a genome with three chromosomes, 100 cM each was simulated. For each animal, a trait was simulated with heritability of 0.50, three different marker densities (1000, 2000 and 3000 markers) and number of QTL was assumed to be either 100, 200 or 300. Data were simulated with two different distributions for the QTL effect which were uniform and gamma (a= 1.66, b=0.4). Marker density, number of QTL and QTL effect distributions significantly affected the genomic accuracy with different Ne. BayesB produced estimates with higher accuracies in traits influenced by a low number of QTL, high marker density, gamma QTL effect distribution and with high Ne.

Genetic polymorphisms and their association with brain and behavioural measures in heterogeneous stock mice

Although the search for quantitative trait loci for behaviour remains a considerable challenge, the complicated genetic architecture of quantitative traits is beginning to be understood. The current project utilised heterogeneous stock (HS) male mice (n = 580) to investigate the genetic basis for brain weights, activity, anxiety and cognitive phenotypes. We identified 126 single nucleotide polymorphisms (SNPs) in genes involved in regulation of neurotransmitter systems, nerve growth/death and gene expression, and subsequently investigated their associations with changes in behaviour and/or brain weights in our sample. We found significant associations between four SNP-phenotype pairs, after controlling for multiple testing. Specificity protein 2 (Sp2, rs3708840), tryptophan hydroxylase 1 (Tph1, rs262731280) and serotonin receptor 3A (Htr3a, rs50670893) were associated with activity/anxiety behaviours, and microtubule-associated protein 2 (Map2, rs13475902) was associated with cognitive performance. All these genes except for Tph1 were expressed in the brain above the array median, and remained significantly associated with relevant behaviours after controlling for the family structure. Additionally, we found evidence for a correlation between Htr3a expression and activity. We discuss our findings in the light of the advantages and limitations of currently available mouse genetic tools, suggesting further directions for association studies in rodents.

Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis

BackgroundMulti-marker methods, which fit all markers simultaneously, were originally tailored for genomic selection purposes, but have proven to be useful also in association analyses, especially the so-called BayesC Bayesian methods. In a recent study, BayesD extended BayesC towards accounting for dominance effects and improved prediction accuracy and persistence in genomic selection. The current study investigated the power and precision of BayesC and BayesD in genome-wide association studies by means of stochastic simulations and applied these methods to a dairy cattle dataset.MethodsThe simulation protocol was designed to mimic the genetic architecture of quantitative traits as realistically as possible. Special emphasis was put on the joint distribution of the additive and dominance effects of causative mutations. Additive marker effects were estimated by BayesC and additive and dominance effects by BayesD. The dependencies between additive and dominance effects were modelled in BayesD by choosing appropriate priors. A sliding-window approach was used. For each window, the R. Fernando window posterior probability of association was calculated and this was used for inference purpose. The power to map segregating causal effects and the mapping precision were assessed for various marker densities up to full sequence information and various window sizes.ResultsPower to map a QTL increased with higher marker densities and larger window sizes. This held true for both methods. Method BayesD had improved power compared to BayesC. The increase in power was between −2 and 8% for causative genes that explained more than 2.5% of the genetic variance. In addition, inspection of the estimates of genomic window dominance variance allowed for inference about the magnitude of dominance at significant associations, which remains hidden in BayesC analysis. Mapping precision was not substantially improved by BayesD.ConclusionsBayesD improved power, but precision only slightly. Application of BayesD needs large datasets with genotypes and own performance records as phenotypes. Given the current efforts to establish cow reference populations in dairy cattle genomic selection schemes, such datasets are expected to be soon available, which will enable the application of BayesD for association mapping and genomic prediction purposes.

Differential regulation of cryptic genetic variation shapes the genetic interactome underlying complex traits

Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets.

Quantitative trait loci for morphometric traits in multiple families of common carp (Cyprinus carpio)

Quantitative trait locus (QTL) mapping is frequently used to understand the genetic architecture of quantitative traits. Herein, we performed a genome scan for QTL affecting the morphometric characters in eight full-sib families containing 522 individuals using different statistical methods (Sib-pair and half-sib model). A total of 194 QTLs were detected in 25 different regions on 10 linkage groups (LGs). Among them, 37 QTLs on five LGs (eight, 13, 24, 40 and 45) were significant (5% genome-wide level), while the remaining 40 (1% chromosome-wide level) and 117 (5% chromosome-wide level) indicated suggestive effect on those traits. Heritabilities for most morphometric traits were moderate to high, ranging from 0.21 to 0.66, with generally strong phenotypic and genetic correlations between the traits. A large number of QTLs for morphometric traits were co-located, consistent with their high correlations, and may reflect pleiotropic effect on the same genes. Biological pathways were mapped for possible candidate genes on QTL regions. One significantly enriched pathway was identified on LG45, which had a P-value of 0.04 and corresponded to the “regulation of actin cytoskeleton pathway”. The results are expected to be useful in marker-assisted selection (MAS) and provide valuable information for the study of gene pathway for morphometric and growth traits of the common carp.

The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis.

Classical quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals without knowing the identities of quantitative trait loci (QTLs). Many studies have found a large proportion of quantitative trait variation can be attributed to the additive genetic variance (VA), providing the basis for claims that non-additive gene actions are unimportant. In this study, we show that arbitrarily defined parameterizations of genetic effects seemingly consistent with non-additive gene actions can also capture the majority of genetic variation. This reveals a logical flaw in using the relative magnitudes of variance components to indicate the relative importance of additive and non-additive gene actions. We discuss the implications and propose that variance component analyses should not be used to infer the genetic architecture of quantitative traits.

Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis.

Quantitative traits in plants are controlled by a large number of genes and their interaction with the environment. To disentangle the genetic architecture of such traits, natural variation within species can be explored by studying genotype-phenotype relationships. Genome-wide association studies that link phenotypes to thousands of single nucleotide polymorphism markers are nowadays common practice for such analyses. In many cases, however, the identified individual loci cannot fully explain the heritability estimates, suggesting missing heritability. We analyzed 349 Arabidopsis accessions and found extensive variation and high heritabilities for different morphological traits. The number of significant genome-wide associations was, however, very low. The application of genomic prediction models that take into account the effects of all individual loci may greatly enhance the elucidation of the genetic architecture of quantitative traits in plants. Here, genomic prediction models revealed different genetic architectures for the morphological traits. Integrating genomic prediction and association mapping enabled the assignment of many plausible candidate genes explaining the observed variation. These genes were analyzed for functional and sequence diversity, and good indications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained. For ACS11, an ethylene biosynthesis gene, haplotype differences explaining variation in the ratio of petiole and leaf length could be identified.

Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions.

BackgroundAccurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci (QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.MethodsBody weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect. A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP panel.ResultsUsing data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1 % with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using BayesB but had no predictive power with GBLUP.ConclusionsIn conclusion, In the presence of large effect QTL, BayesB did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.

Method to represent the distribution of QTL additive and dominance effects associated with quantitative traits in computer simulation.

BackgroundComputer simulation is a resource which can be employed to identify optimal breeding strategies to effectively and efficiently achieve specific goals in developing improved cultivars. In some instances, it is crucial to assess in silico the options as well as the impact of various crossing schemes and breeding approaches on performance for traits of interest such as grain yield. For this, a means by which gene effects can be represented in the genome model is critical.ResultsTo address this need, we devised a method to represent the genomic distribution of additive and dominance gene effects associated with quantitative traits. The method, based on meta-analysis of previously-estimated QTL effects following Bennewitz and Meuwissen (J Anim Breed Genet 127:171–9, 2010), utilizes a modified Dirichlet process Gaussian mixture model (DPGMM) to fit the number of mixture components and estimate parameters (i.e. mean and variance) of the genomic distribution. The method was demonstrated using several maize QTL data sets to provide estimates of additive and dominance effects for grain yield and other quantitative traits for application in maize genome simulations.ConclusionsThe DPGMM method offers an alternative to the over-simplified infinitesimal model in computer simulation as a means to better represent the genetic architecture of quantitative traits, which likely involve some large effects in addition to many small effects. Furthermore, it confers an advantage over other methods in that the number of mixture model components need not be known a priori. In addition, the method is robust with use of large-scale, multi-allelic data sets or with meta-analyses of smaller QTL data sets which may be derived from bi-parental populations in precisely estimating distribution parameters. Thus, the method has high utility in representing the genetic architecture of quantitative traits in computer simulation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0906-z) contains supplementary material, which is available to authorized users.

Studies on characters association for yield and its components in groundnut(Arachis hypogaeaL.)

In any crop improvement programme, the success in genetic restructuring of a population depends upon the information available on the genetic architecture of quantitative traits influencing yield. Hence, a study on inter-relationship among foliar disease resistance, yield and its component traits are important. Correlation analysis among yield and its component characters was carried out to identify the selection indices in BC1F1 generation of four crosses, viz. ICGV 00350 × GPBD 4, ICGV 03128 × GPBD 4, TMVGn 13 × GPBD 4 and VRI2 × GPBD 4. Kernel yield plant−1 and pod yield plant−1 expressed significant and positive association with number of primary branches, number of pods plant−1, 100-pod weight, 100-kernel weight, shell weight and shelling percentage in all the four crosses studied. Hence, these characters may be considered as selection indices. These characters may be given due emphasis in breeding for high kernel yield and pod yield in groundnut. The association among plant height, sound mature kernel per cent, late leaf spot and rust disease scores with yield and other yield component traits varies with the population. Hence, these characters may also be considered as selection indices with caution.

Replicated analysis of the genetic architecture of quantitative traits in two wild great tit populations.

Currently, there is much debate on the genetic architecture of quantitative traits in wild populations. Is trait variation influenced by many genes of small effect or by a few genes of major effect? Where is additive genetic variation located in the genome? Do the same loci cause similar phenotypic variation in different populations? Great tits (Parus major) have been studied extensively in long‐term studies across Europe and consequently are considered an ecological ‘model organism’. Recently, genomic resources have been developed for the great tit, including a custom SNP chip and genetic linkage map. In this study, we used a suite of approaches to investigate the genetic architecture of eight quantitative traits in two long‐term study populations of great tits—one in the Netherlands and the other in the United Kingdom. Overall, we found little evidence for the presence of genes of large effects in either population. Instead, traits appeared to be influenced by many genes of small effect, with conservative estimates of the number of contributing loci ranging from 31 to 310. Despite concordance between population‐specific heritabilities, we found no evidence for the presence of loci having similar effects in both populations. While population‐specific genetic architectures are possible, an undetected shared architecture cannot be rejected because of limited power to map loci of small and moderate effects. This study is one of few examples of genetic architecture analysis in replicated wild populations and highlights some of the challenges and limitations researchers will face when attempting similar molecular quantitative genetic studies in free‐living populations.

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast.

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies.

Multi-trait multi-environment quantitative trait loci mapping for a sugarcane commercial cross provides insights on the inheritance of important traits.

Breeding trials typically consist of phenotypic observations for various traits evaluated in multiple environments. For sugarcane in particular, repeated measures are obtained for plant crop and one or more ratoons, such that joint analysis through mixed models for modeling heterogeneous genetic (co)variances between traits, locations and harvests is appropriate. This modeling approach also enables us to include molecular marker information, aiding in understanding the genetic architecture of quantitative traits. Our work aims at detecting QTL and QTL by environment interactions by fitting mixed models with multiple QTLs, with appropriate modeling of multi-trait multi-environment data for outcrossing species. We evaluated 100 individuals from a biparental cross at two locations and three years for fiber content, sugar content (POL) and tonnes of cane per hectare (TCH). We detected 13 QTLs exhibiting QTL by location, QTL by harvest or the three-way interaction. Overall, 11 of the 13 effects presented some degree of pleiotropy, affecting at least two traits. Furthermore, these QTLs always affected fiber and TCH in the same direction, whereas POL was affected in the opposite way. There was no evidence in favor of the linked QTL over the pleiotropic QTL hypothesis for any detected genome position. These results provide valuable insights into the genetic basis of quantitative variation in sugarcane and the genetic relation between traits.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-015-0366-6) contains supplementary material, which is available to authorized users.

Dearth of polymorphism associated with a sustained response to selection for flowering time in maize.

BackgroundLong term selection experiments bring unique insights on the genetic architecture of quantitative traits and their evolvability. Indeed, they are utilized to (i) monitor changes in allele frequencies and assess the effects of genomic regions involved traits determinism; (ii) evaluate the role of standing variation versus new mutations during adaptation; (iii) investigate the contribution of non allelic interactions. Here we describe genetic and phenotypic evolution of two independent Divergent Selection Experiments (DSEs) for flowering time conducted during 16 years from two early maize inbred lines.ResultsOur experimental design uses selfing as the mating system and small population sizes, so that two independent families evolved within each population, Late and Early. Observed patterns are strikingly similar between the two DSEs. We observed a significant response to selection in both directions during the first 7 generations of selection. Within Early families, the response is linear through 16 generations, consistent with the maintenance of genetic variance. Within Late families and despite maintenance of significant genetic variation across 17 generations, the response to selection reached a plateau after 7 generations. This plateau is likely caused by physiological limits. Residual heterozygosity in the initial inbreds can partly explain the observed responses as evidenced by 42 markers derived from both Methyl-Sensitive Amplification- and Amplified Fragment Length- Polymorphisms. Among the 42, a subset of 13 markers most of which are in high linkage disequilibrium, display a strong association with flowering time variation. Their fast fixation throughout DSEs’ pedigrees results in strong genetic differentiation between populations and families.ConclusionsOur results reveal a paradox between the sustainability of the response to selection and the associated dearth of polymorphisms. Among other hypotheses, we discuss the maintenance of heritable variation by few mutations with strong epistatic interactions whose effects are modified by continuous changes of the genetic background through time.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0382-5) contains supplementary material, which is available to authorized users.

Heterogeneity of genetic architecture of body size traits in a free-living population.

Knowledge of the underlying genetic architecture of quantitative traits could aid in understanding how they evolve. In wild populations, it is still largely unknown whether complex traits are polygenic or influenced by few loci with major effect, due to often small sample sizes and low resolution of marker panels. Here, we examine the genetic architecture of five adult body size traits in a free‐living population of Soay sheep on St Kilda using 37 037 polymorphic SNPs. Two traits (jaw and weight) show classical signs of a polygenic trait: the proportion of variance explained by a chromosome was proportional to its length, multiple chromosomes and genomic regions explained significant amounts of phenotypic variance, but no SNPs were associated with trait variance when using GWAS. In comparison, genetic variance for leg length traits (foreleg, hindleg and metacarpal) was disproportionately explained by two SNPs on chromosomes 16 (s23172.1) and 19 (s74894.1), which each explained >10% of the additive genetic variance. After controlling for environmental differences, females heterozygous for s74894.1 produced more lambs and recruits during their lifetime than females homozygous for the common allele conferring long legs. We also demonstrate that alleles conferring shorter legs have likely entered the population through a historic admixture event with the Dunface sheep. In summary, we show that different proxies for body size can have very different genetic architecture and that dense SNP helps in understanding both the mode of selection and the evolutionary history at loci underlying quantitative traits in natural populations.

Association mapping for root architectural traits in durum wheat seedlings as related to agronomic performance.

Association mapping provides useful insights on the genetic architecture of quantitative traits across a large number of unrelated genotypes, which in turn allows an informed choice of the lines to be crossed for a more accurate characterization of major QTLs in a biparental genetic background. In this study, seedlings of 183 durum wheat elite accessions were evaluated in order to identify QTLs for root system architecture (RSA). The QTLs identified were compared with QTLs detected for grain yield and its component traits, plant height and peduncle length measured in a previous study where the same accessions were evaluated in 15 field trials with a broad range of soil moisture availability and productivity (Maccaferri et al. in J Exp Bot 62:409–438, 2011). The following RSA features were investigated in seedlings at the four-leaf stage: seminal root angle, primary root length, total root length, average root length, root number and shoot length. Highly significant differences among accessions were detected for all traits. The highest repeatability (h 2 = 0.72) was observed for seminal root angle. Out of the 48 QTLs detected for RSA, 15 overlapped with QTLs for agronomic traits and/or grain yield in two or more environments. The congruency of the effects of RSA traits and agronomic traits was evaluated. Seminal root angle and root number appear the most promising traits for further studies on the adaptive role of RSA plasticity on field performance in environments differing for water availability. Our results provide novel insights on the genetic control of RSA and its implications on field performance of durum wheat.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-014-0177-1) contains supplementary material, which is available to authorized users.