Genetic Architecture Of Traits Research Articles

Assessing the opportunity for selection to impact morphological traits in crosses between two Solanum species.

Within biology, there have been long-standing goals to understand how traits impact fitness, determine the degree of adaptation, and predict responses to selection. One key step in answering these questions is to study the mode of gene action or genetic architecture of traits. The genetic architecture underlying a trait will ultimately determine whether selection can lead to a change in the phenotype. Theoretical and empirical research have shown that additive architectures are most responsive to selection. The genus Solanum offers a unique system to quantify the genetic architecture of traits. Crosses between Solanum pennellii and S. lycopersicum, which have evolved unique adaptive traits for very different environments, offer an opportunity to investigate the genetic architecture of a variety of morphological traits that often are not variable within species. We generated cohorts between strains of these two Solanum species and collected phenotypic data for eight morphological traits. The genetic architectures underlying these traits were estimated using an information-theoretic approach to line cross analysis. By estimating the genetic architectures of these traits, we were able to show a key role for maternal and epistatic effects and infer the accessibility of these traits to selection.

Development and optimization of expected cross value for mate selection problems

In this study, we address the mate selection problem in the hybridization stage of a breeding pipeline, which constitutes the multi-objective breeding goal key to the performance of a variety development program. The solution framework we formulate seeks to ensure that individuals with the most desirable genomic characteristics are selected to cross in order to maximize the likelihood of the inheritance of desirable genetic materials to the progeny. Unlike approaches that use phenotypic values for parental selection and evaluate individuals separately, we use a criterion that relies on the genetic architecture of traits and evaluates combinations of genomic information of the pairs of individuals. We introduce the expected cross value (ECV) criterion that measures the expected number of desirable alleles for gametes produced by pairs of individuals sampled from a population of potential parents. We use the ECV criterion to develop an integer linear programming formulation for the parental selection problem. The formulation is capable of controlling the inbreeding level between selected mates. We evaluate the approach or two applications: (i) improving multiple target traits simultaneously, and (ii) finding a multi-parental solution to design crossing blocks. We evaluate the performance of the ECV criterion using a simulation study. Finally, we discuss how the ECV criterion and the proposed integer linear programming techniques can be applied to improve breeding efficiency while maintaining genetic diversity in a breeding program.

Toward telomere-to-telomere cat genomes for precision medicine and conservation biology.

Genomic data from species of the cat family Felidae promise to stimulate veterinary and human medical advances, and clarify the coherence of genome organization. We describe how interspecies hybrids have been instrumental in the genetic analysis of cats, from the first genetic maps to propelling cat genomes toward the T2T standard set by the human genome project. Genotype-to-phenotype mapping in cat models has revealed dozens of health-related genetic variants, the molecular basis for mammalian pigmentation and patterning, and species-specific adaptations. Improved genomic surveillance of natural and captive populations across the cat family tree will increase our understanding of the genetic architecture of traits, population dynamics, and guide a future of genome-enabled biodiversity conservation.

Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating.

The aim of this study was to evaluate the accuracy of the ridge regression best linear unbiased prediction model across different traits, parent population sizes, and breeding strategies when estimating breeding values in common bean (Phaseolus vulgaris). Genomic selection was implemented to make selections within a breeding cycle and compared across five different breeding strategies (single seed descent, mass selection, pedigree method, modified pedigree method, and bulk breeding) following 10 breeding cycles. The model was trained on a simulated population of recombinant inbreds genotyped for 1010 single nucleotide polymorphism markers including 38 known quantitative trait loci identified in the literature. These QTL included 11 for seed yield, eight for white mold disease incidence, and 19 for days to flowering. Simulation results revealed that realized accuracies fluctuate depending on the factors investigated: trait genetic architecture, breeding strategy, and the number of initial parents used to begin the first breeding cycle. Trait architecture and breeding strategy appeared to have a larger impact on accuracy than the initial number of parents. Generally, maximum accuracies (in terms of the correlation between true and estimated breeding value) were consistently achieved under a mass selection strategy, pedigree method, and single seed descent method depending on the simulation parameters being tested. This study also investigated model updating, which involves retraining the prediction model with a new set of genotypes and phenotypes that have a closer relation to the population being tested. While it has been repeatedly shown that model updating generally improves prediction accuracy, it benefited some breeding strategies more than others. For low heritability traits (e.g., yield), conventional phenotype-based selection methods showed consistent rates of genetic gain, but genetic gain under genomic selection reached a plateau after fewer cycles. This plateauing is likely a cause of faster fixation of alleles and a diminishing of genetic variance when selections are made based on estimated breeding value as opposed to phenotype.

Uncovering novel genes for drought stress in rice at germination stage using genome wide association study.

Breeding rice with drought tolerance for harsh environments is crucial for agricultural sustainability. Understanding the genetic underpinnings of drought tolerance is vital for developing resilient rice varieties. Genome-wide association studies (GWAS) have emerged as pivotal tools in unravelling the complex genetic architecture of traits like drought tolerance, capitalizing on the natural genetic diversity within rice germplasm collections. In this study, a comprehensive panel of 210 rice varieties was phenotyped over ten days in controlled conditions, subjected to simulated drought stress using 20% PEG 6000 in petri dishes. Throughout the stress period, crucial traits such as germination percentage (GP), germination rate index (GRI), mean germination time (MGT), and seedling percentage (SP) were meticulously monitored. The GWAS analysis uncovered a total of 38 QTLs associated with drought tolerance traits, including novel loci like qMGT-5.2, qSP-3, qSP7.2, and qGP-5.2. Additionally, RNA-seq analysis identified ten genes with significant expression differences under drought stress conditions. Notably, haplotype analysis pinpointed elite haplotypes in specific genes linked to heightened drought tolerance. Overall, this study underscores the importance of GWAS in validating known genes while unearthing novel loci to enrich the genetic resources for enhancing drought tolerance in rice breeding programs.

PSXIV-6 Comparison of Changes in Snp Effects and Gebv Using Different Training Populations in Simulated Data

Abstract Genome-wide association studies using SNPs aim to capture QTL effects across the genome. However, the results of GWAS vary across studies, even when traits and populations are similar. Fluctuation in estimating SNP effects and variances can be caused by several biological factors such as selection, genetic architecture of trait, and population stratification, and by nonbiological factors, such as method and inflation of the predictor error for SNP effects. The current study aims to compare changes in SNP effects and GEBVs using different training populations. A simulation study was conducted using data from 20,000 individuals generated in the AlphaSimR package in R. Data for a trait with 10 chromosomes were simulated, and linkage disequilibrium was created by random crossing across 1,000 generations. The trait genetic architecture was controlled by 100 loci on each chromosome, accounting for 100% of the genetic variation. Pedigree, phenotypes, and genotypes for all individuals in the population were created, but phenotypes for individuals in the last generation were masked for validation purposes. The training population was then divided into three types comprising all, only odd, and only even animals. Genomic breeding values (GEBV) were estimated using the genomic information by single-step GBLUP. SNP effects and variance were calculated by back-solving the GEBVs. The correlation between all and odd populations was 0.83, all and even populations was 0.81, and odd and even populations were 0.40. Correlations for all 50k SNP effects between all and odd population was 0.75, the all and even population was 0.75, and the odd and even populations was 0.20. Changing the training population data by half led to small changes in accuracy. However, SNP effects fluctuated across populations more than GEBVs. Visual inspection of Manhattan plots showed that the peak regions varied with the training population used. The top regions were more stable and experienced fewer fluctuations, while the intermediate regions varied more often. The largest differences were between the odd and even scenarios. The regions explaining more variance were not the same, and the variations were greater when less data were used for training (odd and even). Overall, the fluctuations in SNP effects in GWAS highlight the complexity of genetic influences on traits, and how sensitive those studies are to small changes in the data. Understanding the factors that contribute to GWAS fluctuations may help improve the design and interpretation of such studies. We expect to derive a novel tool that uses the fluctuations of SNP effects to leverage QTL discovery.

Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population

BackgroundAlthough the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data.ResultsWe used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN.ConclusionsThe SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.

Genome-wide association mapping in a sweet cherry germplasm collection (Prunus avium L.) reveals candidate genes for fruit quality traits.

In sweet cherry (Prunus avium L.), large variability exists for various traits related to fruit quality. There is a need to discover the genetic architecture of these traits in order to enhance the efficiency of breeding strategies for consumer and producer demands. With this objective, a germplasm collection consisting of 116 sweet cherry accessions was evaluated for 23 agronomic fruit quality traits over 2-6 years, and characterized using a genotyping-by-sequencing approach. The SNP coverage collected was used to conduct a genome-wide association study using two multilocus models and three reference genomes. We identified numerous SNP-trait associations for global fruit size (weight, width, and thickness), fruit cracking, fruit firmness, and stone size, and we pinpointed several candidate genes involved in phytohormone, calcium, and cell wall metabolisms. Finally, we conducted a precise literature review focusing on the genetic architecture of fruit quality traits in sweet cherry to compare our results with potential colocalizations of marker-trait associations. This study brings new knowledge of the genetic control of important agronomic traits related to fruit quality, and to the development of marker-assisted selection strategies targeted towards the facilitation of breeding efforts.

Genetic Architecture of Flowering Time Differs Between Populations With Contrasting Demographic and Selective Histories.

Understanding the evolutionary factors that impact the genetic architecture of traits is a central goal of evolutionary genetics. Here, we investigate how quantitative trait variation accumulated over time in populations that colonized a novel environment. We compare the genetic architecture of flowering time in Arabidopsis populations from the drought-prone Cape Verde Islands and their closest outgroup population from North Africa. We find that trait polygenicity is severely reduced in the island populations compared to the continental North African population. Further, trait architectures and reconstructed allelic histories best fit a model of strong directional selection in the islands in accord with a Fisher-Orr adaptive walk. Consistent with this, we find that large-effect variants that disrupt major flowering time genes (FRI and FLC) arose first, followed by smaller effect variants, including ATX2 L125F, which is associated with a 4-day reduction in flowering time. The most recently arising flowering time-associated loci are not known to be directly involved in flowering time, consistent with an omnigenic signature developing as the population approaches its trait optimum. Surprisingly, we find no effect in the natural population of EDI-Cvi-0 (CRY2 V367M), an allele for which an effect was previously validated by introgression into a Eurasian line. Instead, our results suggest the previously observed effect of the EDI-Cvi-0 allele on flowering time likely depends on genetic background, due to an epistatic interaction. Altogether, our results provide an empirical example of the effects demographic history and selection has on trait architecture.

The long-term effects of genomic selection: 2. Changes in allele frequencies of causal loci and new mutations.

Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (∼15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles.

Design, execution, and interpretation of plant RNA-seq analyses.

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

Genetic architecture of a pollinator shift and its fate in secondary hybrid zones of two Petunia species

BackgroundTheory suggests that the genetic architecture of traits under divergent natural selection influences how easily reproductive barriers evolve and are maintained between species. Divergently selected traits with a simple genetic architecture (few loci with major phenotypic effects) should facilitate the establishment and maintenance of reproductive isolation between species that are still connected by some gene flow. While empirical support for this idea appears to be mixed, most studies test the influence of trait architectures on reproductive isolation only indirectly. Petunia plant species are, in part, reproductively isolated by their different pollinators. To investigate the genetic causes and consequences of this ecological isolation, we deciphered the genetic architecture of three floral pollination syndrome traits in naturally occurring hybrids between the widespread Petunia axillaris and the highly endemic and endangered P. exserta.ResultsUsing population genetics, Bayesian linear mixed modelling and genome-wide association studies, we found that the three pollination syndrome traits vary in genetic architecture. Few genome regions explain a majority of the variation in flavonol content (defining UV floral colour) and strongly predict the trait value in hybrids irrespective of interspecific admixture in the rest of their genomes. In contrast, variation in pistil exsertion and anthocyanin content (defining visible floral colour) is controlled by many genome-wide loci. Opposite to flavonol content, the genome-wide proportion of admixture between the two species predicts trait values in their hybrids. Finally, the genome regions strongly associated with the traits do not show extreme divergence between individuals representing the two species, suggesting that divergent selection on these genome regions is relatively weak within their contact zones.ConclusionsAmong the traits analysed, those with a more complex genetic architecture are best maintained in association with the species upon their secondary contact. We propose that this maintained genotype–phenotype association is a coincidental consequence of the complex genetic architectures of these traits: some of their many underlying small-effect loci are likely to be coincidentally linked with the actual barrier loci keeping these species partially isolated upon secondary contact. Hence, the genetic architecture of a trait seems to matter for the outcome of hybridization not only then when the trait itself is under selection.

An Ethical Framework for Research Using Genetic Ancestry.

A wide range of research uses patterns of genetic variation to infer genetic similarity between individuals, typically referred to as genetic ancestry. This research includes inference of human demographic history, understanding the genetic architecture of traits, and predicting disease risk. Researchers are not just structuring an intellectual inquiry when using genetic ancestry, they are also creating analytical frameworks with broader societal ramifications. This essay presents an ethics framework in the spirit of virtue ethics for these researchers: rather than focus on rule following, the framework is designed to build researchers' capacities to react to the ethical dimensions of their work. The authors identify one overarching principle of intellectual freedom and responsibility, noting that freedom in all its guises comes with responsibility, and they identify and define four principles that collectively uphold researchers' intellectual responsibility: truthfulness, justice and fairness, anti-racism, and public beneficence. Researchers should bring their practices into alignment with these principles, and to aid this, the authors name three common ways research practices infringe these principles, suggest a step-by-step process for aligning research choices with the principles, provide rules of thumb for achieving alignment, and give a worked case. The essay concludes by identifying support needed by researchers to act in accord with the proposed framework.

White Lupin Drought Tolerance: Genetic Variation, Trait Genetic Architecture, and Genome-Enabled Prediction.

White lupin is a high-protein crop requiring drought tolerance improvement. This study focused on a genetically-broad population of 138 lines to investigate the phenotypic variation and genotype × environment interaction (GEI) for grain yield and other traits across drought-prone and moisture-favourable managed environments, the trait genetic architecture and relevant genomic regions by a GWAS using 9828 mapped SNP markers, and the predictive ability of genomic selection (GS) models. Water treatments across two late cropping months implied max. available soil water content of 60-80% for favourable conditions and from wilting point to 15% for severe drought. Line yield responses across environments featured a genetic correlation of 0.84. Relatively better line yield under drought was associated with an increased harvest index. Two significant QTLs emerged for yield in each condition that differed across conditions. Line yield under stress displayed an inverse linear relationship with the onset of flowering, confirmed genomically by a common major QTL. An adjusted grain yield computed as deviation from phenology-predicted yield acted as an indicator of intrinsic drought tolerance. On the whole, the yield in both conditions and the adjusted yield were polygenic, heritable, and exploitable by GS with a high predictive ability (0.62-0.78). Our results can support selection for climatically different drought-prone regions.

Decomposing heritability and genetic covariance by direct and indirect effect paths.

Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.

Quantitative genetic-by-soil microbiome interactions in a perennial grass affect functional traits.

Plants interact with diverse microbiomes that can impact plant growth and performance. Recent studies highlight the potential beneficial aspects of plant microbiomes, including the possibility that microbes facilitate the process of local adaptation in their host plants. Microbially mediated local adaptation in plants occurs when local host genotypes have higher fitness than foreign genotypes because of their affiliation with locally beneficial microbes. Here, plant adaptation results from genetic interactions of the host with locally beneficial microbes (e.g. host genotype-by-microbiome interactions). We used a recombinant inbred line (RIL) mapping population derived from upland and lowland ecotypes of the diploid C4 perennial bunch grass Panicum hallii to explore quantitative genetic responses to soil microbiomes focusing on functional root and shoot traits involved in ecotypic divergence. We show that the growth and development of ecotypes and their trait divergence depends on soil microbiomes. Moreover, we find that the genetic architecture is modified by soil microbiomes, revealing important plant genotype-by-microbiome interactions for quantitative traits. We detected a number of quantitative trait loci (QTL) that interact with the soil microbiome. Our results highlight the importance of microbial interactions in ecotypic divergence and trait genetic architecture in C4 perennial grasses.

A polygenic basis for birth weight in a wild population of red deer (Cervus elaphus).

The genetic architecture of traits under selection has important consequences for the response to selection and potentially for population viability. Early QTL mapping studies in wild populations have reported loci with large effect on trait variation. However, these results are contradicted by more recent genome-wide association analyses, which strongly support the idea that most quantitative traits have a polygenic basis. This study aims to re-evaluate the genetic architecture of a key morphological trait, birth weight, in a wild population of red deer (Cervus elaphus), using genomic approaches. A previous study using 93 microsatellite and allozyme markers and linkage mapping on a kindred of 364 deer detected a pronounced QTL on chromosome 21 explaining 29% of the variance in birth weight, suggesting that this trait is partly controlled by genes with large effects. Here, we used data for more than 2,300 calves genotyped at >39,000 SNP markers and two approaches to characterise the genetic architecture of birth weight. First, we performed a genome-wide association (GWA) analysis, using a genomic relatedness matrix to account for population structure. We found no SNPs significantly associated with birth weight. Second, we used genomic prediction to estimate the proportion of variance explained by each SNP and chromosome. This analysis confirmed that most genetic variance in birth weight was explained by loci with very small effect sizes. Third, we found that the proportion of variance explained by each chromosome was slightly positively correlated with its size. These three findings highlight a highly polygenic architecture for birth weight, which contradicts the previous QTL study. These results are probably explained by the differences in how associations are modelled between QTL mapping and GWA. Our study suggests that models of polygenic adaptation are the most appropriate to study the evolutionary trajectory of this trait.

Simulations of rate of genetic gain in dry bean breeding programs

Key messageA reference study for breeders aiming at maximizing genetic gain in common bean. Depending on trait heritability and genetic architecture, conventional approaches may provide an advantage over other frameworks.Dry beans (Phaseolus vulgaris L.) are a nutrient dense legume that is consumed by developed and developing nations around the world. The progress to improve this crop has been quite steady. However, with the continued rise in global populations, there are demands to expedite genetic gains. Plant breeders have been at the forefront at increasing yields in the common bean. As breeding programs are both time-consuming and resource intensive, resource allocation must be carefully considered. To assist plant breeders, computer simulations can provide useful information that may then be applied to the real world. This study evaluated multiple breeding scenarios in the common bean and involved five selection strategies, three breeding frameworks, and four different parental population sizes. In addition, the breeding scenarios were implemented in three different traits: days to flowering, white mold tolerance, and seed yield. Results from the study reflect the complexity of breeding programs, with the optimal breeding scenario varying based on trait being selected. Relative genetic gains per cycle of up to 8.69% for seed yield could be obtained under the use of the optimal breeding scenario. Principal component analyses revealed similarity between strategies, where single seed descent and the modified pedigree method would often aggregate. As well, clusters in the direction of the Hamming distance eigenvector are a good indicator of poor performance in a strategy.

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat

AbstractCereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement.

Dissecting the loci underlying maturation timing in Atlantic salmon using haplotype and multi-SNP based association methods

Characterizing the role of different mutational effect sizes in the evolution of fitness-related traits has been a major goal in evolutionary biology for a century. Such characterization in a diversity of systems, both model and non-model, will help to understand the genetic processes underlying fitness variation. However, well-characterized genetic architectures of such traits in wild populations remain uncommon. In this study, we used haplotype-based and multi-SNP Bayesian association methods with sequencing data for 313 individuals from wild populations to test the mutational composition of known candidate regions for sea age at maturation in Atlantic salmon (Salmo salar). We detected an association at five loci out of 116 candidates previously identified in an aquaculture strain with maturation timing in wild Atlantic salmon. We found that at four of these five loci, variation explained by the locus was predominantly driven by a single SNP suggesting the genetic architecture of this trait includes multiple loci with simple, non-clustered alleles and a locus with potentially more complex alleles. This highlights the diversity of genetic architectures that can exist for fitness-related traits. Furthermore, this study provides a useful multi-SNP framework for future work using sequencing data to characterize genetic variation underlying phenotypes in wild populations.