Mapping Of Complex Traits Research Articles

A global resource for exploring and exploiting genetic variation in sorghum crop wild relatives

AbstractOne response to mitigate the impact of climate change on agricultural systems is to develop new varieties that are tolerant to the new range of biotic and abiotic challenges this change causes. This requires access to novel variants of genes for complex adaptive traits. Crop wild relatives (CWRs) are a potentially valuable source of these genes; however, these materials are often difficult to work with and identifying valuable alleles is difficult without substantial investment in prebreeding. In this study, we describe the development of a nested association mapping population for sorghum [Sorghum bicolor (L.) Moench] using two cultivated grain sorghum reference parents and nine wild and exotic sorghum accessions as donors. The donor parents come from the S. bicolor (L.) Moench subsp. verticilliflorum (Steud.) de Wet ex Wiersema & J. Dahlb., S. bicolor (L.) Moench subsp. drummondii (Steud.) de Wet ex Davidse, and S. bicolor (L.) Moench subsp. bicolor margaritiferum taxa and were sampled from a range of environments across Africa. In total, the resource consists of 13 populations and a total of 1,224 lines. The population has been genotyped with diversity array technology (DArT) markers that produced 42,372 unique single nucleotide polymorphism (SNP) markers covering the genome. We determine the utility of the resource for high resolution mapping of complex traits by demonstrating that the exotics contain unique alleles for some example adaptive trait loci and by using the population for genome‐wide association study (GWAS). The resource should provide useful material for plant breeders attempting to deal with the challenges generated by climate change.

Whole genome variants across 57 pig breeds enable comprehensive identification of genetic signatures that underlie breed features

BackgroundA large number of pig breeds are distributed around the world, their features and characteristics vary among breeds, and they are valuable resources. Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved pig breeds.ResultsIn this study, we performed GWAS using a standard mixed linear model with three types of genome variants (SNP, InDel, and CNV) that were identified from public, whole-genome, sequencing data sets. We used 469 pigs of 57 breeds, and we identified and analyzed approximately 19 million SNPs, 1.8 million InDels, and 18,016 CNVs. We defined six biological phenotypes by the characteristics of breed features to identify the associated genome variants and candidate genes, which included coat color, ear shape, gradient zone, body weight, body length, and body height. A total of 37 candidate genes was identified, which included 27 that were reported previously (e.g., PLAG1 for body weight), but the other 10 were newly detected candidate genes (e.g., ADAMTS9 for coat color).ConclusionOur study indicated that using GWAS across a modest number of breeds with high density genome variants provided efficient mapping of complex traits.

High-fat diet negatively impacts both metabolic and behavioral health in outbred heterogeneous stock rats.

Obesity is influenced by genetics and diet and has wide ranging comorbidities, including anxiety and depressive disorders. Outbred heterogeneous stock (HS) rats are used for fine-genetic mapping of complex traits and may be useful for understanding gene by diet interactions. In this study, HS rats were fed diets containing 60% kcal from fat (high-fat diet, HFD) or 10% kcal from fat (low-fat diet, LFD) and tested for metabolic (study 1) and behavioral (study 2) outcomes. In study 1, we measured glucose tolerance, fasting glucose and insulin, fat pad weights and despair-like behavior in the forced swim test (FST). In study 2, we assessed anxiety-like (elevated plus maze, EPM; open field test, OFT) and despair-like/coping (splash test, SpT; and FST) behaviors. Body weight and food intake were measured weekly in both studies. We found negative effects of HFD on metabolic outcomes, including increased body weight and fat pad weights, decreased glucose tolerance, and increased fasting insulin. We also found negative effects of HFD on despair-like/coping and anxiety-like behaviors. These include increased immobility in the FST, decreased open arm time in the EPM, and increased movement and rest episodes and decreased rearing in the OFT. The diet-induced changes in EPM and OFT were independent of overall locomotion. Additionally, diet-induced changes in OFT behaviors were independent of adiposity, while adiposity was a confounding factor for EPM and FST behavior. This work establishes the HS as a model to study gene by diet interactions affecting metabolic and behavioral health.

Hybrid Rat Diversity Program (HRDP): A rat resource for mapping complex traits

Rats are a major model for studying complex disease mechanisms, behavioral phenotypes, environmental factors, and for drug development and discovery. Inbred rat strains control for genetic background and allow for repeated, reproducible molecular, cellular, and whole animal phenotyping. Genetic susceptibility to disease, sensitivity to environmental elements, and pharmacogenomics are critical components of the concepts of precision medicine. A resource that combines the power of genetic stability within strains, power for genomic mapping strategies, and strength in an animal model that mirrors many human disease traits is the goal of this project. Through the development of the Hybrid Rat Diversity Program (HRDP), a powerful panel of inbred rats with genomic, physiologic, and behavioral phenotype data is beginning to serve as a catalyst for research in many fields. When complete, the HRDP will consist of 96 inbred rat strains to maximize power to detect specific genetic loci associated with complex traits while maximizing the genetic diversity among strains. The 96 strain panel consists of 33 genetically diverse inbred strains and two panels of recombinant inbred panels: FEXL/LEXF (33 strains, Japan) and HXB/BXH (30 strains, Czech Republic). Embryo resuscitation and breeding is well underway at the Medical College of Wisconsin (MCW). Whole genome sequencing for 32 of the 96 strains is complete, having been generated on am Illumina NovaSeq S4 to achieve 30X coverage. Liver and brain transcriptome analysis is underway through PhenoGen ( http://phenogen.ucdenver.edu) at the University of Colorado. Genomic, phenotype, and strain information will be made available through the Hybrid Rat Diversity Panel portal at the Rat Genome Database ( http://rgd.mcw.edu). The HRDP will provide a genetically stable population of rat strains with fully sequenced genomes, transcriptomes for brain and liver, and general phenotypic characterization to be used for systems genetic studies and fine mapping of complex traits.Support or Funding InformationNational Institutes of Health, Office of the Director, R24OD024617

High-density genetic linkage map and QTL fine mapping of growth and sex in snakehead (Channa argus)

High-density genetic linkage maps are essential for quantitative trait locus (QTL) mapping, candidate gene identification, marker-assisted selection, genome assembly and comparative genomic analysis in aquaculture species. Snakehead (Channa argus) is an important aquaculture species in China, but the genetic improvement of this species is still in its infancy. In this study, a high-density genetic linkage map was constructed for snakehead using 2b-RAD sequencing technology. The consensus map contains 3151 single nucleotide polymorphism (SNP) markers assigned to 24 linkage groups (LGs) and spanning 2728.9 cM with an average marker interval of 0.87 cM. Comparative genomics revealed a 1:1 syntenic relationship between snakehead LGs and chromosomes of medaka and European seabass. Fourteen chromosome-wide QTLs associated with body weight and body length were identified in five LGs, explaining 9.6–12.8% of the phenotypic variation. Potential candidate growth-related genes, such as SATB homeobox 2 (satb2), were identified within the QTL intervals. A single major locus for sex determination was identified in LG5 that explained 97.4–100% of the phenotypic variation. Six markers with a sex-determining locus were found to be heterogametic in males and homogametic in females. A sex-specific SNP marker located in the sex QTL region was validated for practical verification of sex in snakehead. All of the results suggest an XX/XY sex determination system in this species. This high-density genetic map and QTL analysis supply a basis for genome evolutionary studies, genome assembly and QTL fine mapping of complex traits in snakehead.

Development of a Multiparent Population for Genetic Mapping and Allele Discovery in Six-Row Barley.

Germplasm collections hold valuable allelic diversity for crop improvement and genetic mapping of complex traits. To gain access to the genetic diversity within the USDA National Small Grain Collection (NSGC), we developed the Barley Recombinant Inbred Diverse Germplasm Population (BRIDG6), a six-row spring barley multiparent population (MPP) with 88 cultivated accessions crossed to a common parent (Rasmusson). The parents were randomly selected from a core subset of the NSGC that represents the genetic diversity of landrace and breeding accessions. In total, we generated 6160 F5 recombinant inbred lines (RILs), with an average of 69 and a range of 37-168 RILs per family, that were genotyped with 7773 SNPs, with an average of 3889 SNPs segregating per family. We detected 23 quantitative trait loci (QTL) associated with flowering time with five QTL found coincident with previously described flowering time genes. A major QTL was detected near the flowering time gene, HvPpd-H1 which affects photoperiod. Haplotype-based analysis of HvPpd-H1 identified private alleles to families of Asian origin conferring both positive and negative effects, providing the first observation of flowering time-related alleles private to Asian accessions. We evaluated several subsampling strategies to determine the effect of sample size on the power of QTL detection, and found that, for flowering time in barley, a sample size >50 families or 3000 individuals results in the highest power for QTL detection. This MPP will be useful for uncovering large and small effect QTL for traits of interest, and identifying and utilizing valuable alleles from the NSGC for barley improvement.

Imputation of canine genotype array data using 365 whole-genome sequences improves power of genome-wide association studies.

Genomic resources for the domestic dog have improved with the widespread adoption of a 173k SNP array platform and updated reference genome. SNP arrays of this density are sufficient for detecting genetic associations within breeds but are underpowered for finding associations across multiple breeds or in mixed-breed dogs, where linkage disequilibrium rapidly decays between markers, even though such studies would hold particular promise for mapping complex diseases and traits. Here we introduce an imputation reference panel, consisting of 365 diverse, whole-genome sequenced dogs and wolves, which increases the number of markers that can be queried in genome-wide association studies approximately 130-fold. Using previously genotyped dogs, we show the utility of this reference panel in identifying potentially novel associations, including a locus on CFA20 significantly associated with cranial cruciate ligament disease, and fine-mapping for canine body size and blood phenotypes, even when causal loci are not in strong linkage disequilibrium with any single array marker. This reference panel resource will improve future genome-wide association studies for canine complex diseases and other phenotypes.

Efficient protocols and methods for high-throughput utilization of the Collaborative Cross mouse model for dissecting the genetic basis of complex traits.

The Collaborative Cross (CC) mouse model is a next‐generation mouse genetic reference population (GRP) designated for a high‐resolution quantitative trait loci (QTL) mapping of complex traits during health and disease. The CC lines were generated from reciprocal crosses of eight divergent mouse founder strains composed of five classical and three wild‐derived strains. Complex traits are defined to be controlled by variations within multiple genes and the gene/environment interactions. In this article, we introduce and present variety of protocols and results of studying the host response to infectious and chronic diseases, including type 2 diabetes and metabolic diseases, body composition, immune response, colorectal cancer, susceptibility to Aspergillus fumigatus, Klebsiella pneumoniae, Pseudomonas aeruginosa, sepsis, and mixed infections of Porphyromonas gingivalis and Fusobacterium nucleatum, which were conducted at our laboratory using the CC mouse population. These traits are observed at multiple levels of the body systems, including metabolism, body weight, immune profile, susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present full protocols and step‐by‐step methods, implemented in our laboratory for the phenotypic and genotypic characterization of the different CC lines, mapping the gene underlying the host response to these infections and chronic diseases. The CC mouse model is a unique and powerful GRP for dissecting the host genetic architectures underlying complex traits, including chronic and infectious diseases.

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

The transcriptome-wide association studies (TWASs) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWASs facilitate gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, to improve on this, we employ a nonparametric Bayesian method that was originally proposed for genetic prediction of complex traits, which assumes a data-driven nonparametric prior for cis-eQTL effect sizes. The nonparametric Bayesian method is flexible and general because it includes both of the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan when ≥1% cis-SNPs co-regulate gene expression and gene expression heritability ≤0.2. In real applications, the nonparametric Bayesian method fitted transcriptomic imputation models for 57.8% more genes over PrediXcan, thus improving the power of follow-up TWASs. We implement both parametric PrediXcan and nonparametric Bayesian methods in a convenient software tool "TIGAR" (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWASs using individual-level or summary-level GWAS data.

Fast genetic mapping of complex traits in C. elegans using millions of individuals in bulk

Genetic studies of complex traits in animals have been hindered by the need to generate, maintain, and phenotype large panels of recombinant lines. We developed a new method, C. elegans eXtreme Quantitative Trait Locus (ceX-QTL) mapping, that overcomes this obstacle via bulk selection on millions of unique recombinant individuals. We use ceX-QTL to map a drug resistance locus with high resolution. We also map differences in gene expression in live worms and discovered that mutations in the co-chaperone sti-1 upregulate the transcription of HSP-90. Lastly, we use ceX-QTL to map loci that influence fitness genome-wide confirming previously reported causal variants and uncovering new fitness loci. ceX-QTL is fast, powerful and cost-effective, and will accelerate the study of complex traits in animals.

Joint linkage and association mapping of complex traits in shrub willow (Salix purpurea L.).

Increasing energy demands and the necessity to reduce greenhouse gas emissions are key motivating factors driving the development of lignocellulosic crops as an alternative to non-renewable energy sources. The effects of global climate change will require a better understanding of the genetic basis of complex adaptive traits to breed more resilient bioenergy feedstocks, like willow (Salix spp.). Shrub willow is a sustainable and dedicated bioenergy crop, bred to be fast-growing and high-yielding on marginal land without competing with food crops. In a rapidly changing climate, genomic advances will be vital for the sustained improvement of willow and other non-model bioenergy crops. Here, joint genetic mapping was used to exploit genetic variation garnered from both recent and historical recombination events in S. purpurea. A panel of North American naturalized S. purpurea accessions and full-sib F2S. purpurea population were genotyped and phenotyped for a suite of morphological, physiological, pest and disease resistance, and wood chemical composition traits, collected from multi-environment and multi-year replicated field trials. Controlling for population stratification and kinship in the association panel and spatial variation in the F2, a comprehensive mixed model analysis was used to dissect the complex genetic architecture and plasticity of these important traits. Individually, genome-wide association (GWAS) models differed in terms of power, but the combined approach, which corrects for yearly and environmental co-factors across datasets, improved the overall detection and resolution of associated loci. Although there were few significant GWAS hits located within support intervals of QTL for corresponding traits in the F2, many large-effect QTL were identified, as well as QTL hotspots. This study provides the first comparison of linkage analysis and linkage disequilibrium mapping approaches in Salix, and highlights the complementarity and limits of these two methods for elucidating the genetic architecture of complex bioenergy-related traits of a woody perennial breeding programme.

A dissection model for mapping complex traits.

Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time-varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.

A mapping framework of competition\u2013cooperation QTLs that drive community dynamics

Genes have been thought to affect community ecology and evolution, but their identification at the whole-genome level is challenging. Here, we develop a conceptual framework for the genome-wide mapping of quantitative trait loci (QTLs) that govern interspecific competition and cooperation. This framework integrates the community ecology theory into systems mapping, a statistical model for mapping complex traits as a dynamic system. It can characterize not only how QTLs of one species affect its own phenotype directly, but also how QTLs from this species affect the phenotype of its interacting species indirectly and how QTLs from different species interact epistatically to shape community behavior. We validated the utility of the new mapping framework experimentally by culturing and comparing two bacterial species, Escherichia coli and Staphylococcus aureus, in socialized and socially isolated environments, identifying several QTLs from each species that may act as key drivers of microbial community structure and function.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes.

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

An Immortalized Genetic Mapping Population for Perennial Ryegrass: A Resource for Phenotyping and Complex Trait Mapping.

To address the lack of a truly portable, universal reference mapping population for perennial ryegrass, we have been developing a recombinant inbred line (RIL) mapping population of perennial ryegrass derived via single seed descent from a well-characterized F2 mapping population based on genetically distinct inbred parents in which the natural self-incompatibility (SI) system of perennial ryegrass has been overcome. We examined whether it is possible to create a genotyping by sequencing (GBS) based genetic linkage map in a small population of the F6 generation of this population. We used 41 F6 genotypes for GBS with PstI/MspI-based libraries. We successfully developed a genetic linkage map comprising 6074 SNP markers, placing a further 22080 presence and absence variation (PAV) markers on the map. We examined the resulting genetic map for general and RIL specific features. Overall segregation distortion levels were similar to those experienced in the F2 generation, but segregation distortion was reduced on linkage group 6 and increased on linkage group 7. Residual heterozygosity in the F6 generation was observed at a level of 5.4%. There was a high proportion of chromosomes (30%) exhibiting the intact haplotype of the original inbred parents of the F1 genotype from which the population is derived, pointing to a tendency for chromosomes to assort without recombining. This could affect the applicability of these lines and might make them more suitable for situations where repressed recombination is an advantage. Inter- and intra-chromosomal linkage disequilibrium (LD) analysis suggested that the map order was robust. We conclude that this RIL population, and subsequent F7 and F8 generations will be useful for genetic analysis and phenotyping of agronomic and biological important traits in perennial ryegrass.

Abstract 643: Atherosclerosis Susceptibility in the Collaborative Cross

Objective: The Collaborative Cross (CC) is a large recombinant inbred mouse population, generated from elaborate intercrosses of C57BL6/J, A/J, NOD/ShiLtJ, NZO/HiLtJ, WSB/EiJ, CAST/EiJ, PWK/PhJ, and 129S1/SvImJ mouse strains, useful for complex trait mapping and systems genetics. The CC population has tremendous genetic diversity, containing approximately 45 million segregating single-nucleotide polymorphisms (SNPs). The purpose of this study was to characterize atherosclerosis susceptibility in an initial cohort of Collaborative Cross strains. Methods and Results: We obtained 5 females from 20 CC lines available at the University of North Carolina and placed them on a low-fat, synthetic diet (AIN-76) for 2 weeks. Following two weeks of diet, all mice were assessed for plasma cardio-metabolic risk factors. The mice were then randomly assigned to cages and placed on a synthetic high-fat, cholic acid (HFCA) diet that contained 20% fat, 1.25% cholesterol, and 0.5% cholic acid. Aortic root atherosclerosis and cardio-metabolic risk factors were quantitated following 16 weeks of HFCA diet feeding. Atherosclerotic lesion size ranged from completely resistant (strains C004/TauUnc and CC037/TauUnc) to moderate lesion development in a majority of several strains. Notably all mice from strain CC063/Unc died prior to 20 weeks of age, while strain CC028/GeniUnc was highly susceptible to lesion development with an average lesion size of 105,781 ± 17,370 um 2 . As expected, strain is a significant contributor to atherosclerosis susceptibility (one way Anova analysis, p<8.23x10 -08 ). Similar effects were observed for circulating cholesterol with strains C004/TauUnc and CC037/TauUnc having relatively low circulating total cholesterol after HFCA feeding (187 and 198 mg/dl respectively) to > 500 mg/dl in strains CC006/UNC, CC011/Unc and CC036/Unc. Circulating cholesterol and atherosclerosis were significantly correlated (Spearman rho>0.30, p< 0.007). Conclusion: These results suggest that selective crosses between various CC strains should yield loci associated with atherosclerosis. High-resolution genome-wide association analysis for atherosclerosis may be feasible when more Collaborative Cross strains become available.

Genome-wide association for testis weight in the diversity outbred mouse population

Testis weight is a genetically mediated trait associated with reproductive efficiency across numerous species. We sought to evaluate the genetically diverse, highly recombinant Diversity Outbred (DO) mouse population as a tool to identify and map quantitative trait loci (QTLs) associated with testis weight. Testis weights were recorded for 502 male DO mice and the mice were genotyped on the GIGAMuga array at ~ 143,000 SNPs. We performed a genome-wide association analysis and identified one significant and two suggestive QTLs associated with testis weight. Using bioinformatic approaches, we developed a list of candidate genes and identified those with known roles in testicular size and development. Candidates of particular interest include the RNA demethylase gene Alkbh5, the cyclin-dependent kinase inhibitor gene Cdkn2c, the dynein axonemal heavy chain gene Dnah11, the phospholipase D gene Pld6, the trans-acting transcription factor gene Sp4, and the spermatogenesis-associated gene Spata6, each of which has a human ortholog. Our results demonstrate the utility of DO mice in high-resolution genetic mapping of complex traits, enabling us to identify developmentally important genes in adult mice. Understanding how genetic variation in these genes influence testis weight could aid in the understanding of mechanisms of mammalian reproductive function.

L-GATOR: Genetic Association Testing for a Longitudinally Measured Quantitative Trait in Samples with Related Individuals

In complex-trait mapping, when each subject has multiple measurements of a quantitative trait over time, power for detecting genetic association can be gained by the inclusion of all measurements and not just single time points or averages in the analysis. To increase power and control type 1 error, one should account for dependence among observations for a single individual as well as dependence between observations of related individuals if they are present in the sample. We propose L-GATOR, a retrospective, mixed-effects method for association mapping of longitudinally measured traits in samples with related individuals. L-GATOR allows arbitrary time points for different individuals, incorporates both time-varying and static covariates, and properly addresses various types of dependence. In simulations, we show that L-GATOR outperforms existing prospective methods in terms of both type 1 error and power when there is phenotype model misspecification or missing data. Compared with the previously proposed longGWAS method, L-GATOR was more than ten times faster for association testing in our simulations and almost 100 times faster for parameter estimation. L-GATOR is applicable to essentially arbitrary combinations of related and unrelated individuals, including small families as well as large, complex pedigrees. We apply the method to data from the Framingham Heart Study to identify association between longitudinal systolic blood pressure measurements and genome-wide SNPs. Of the smallest p values, one-third occur in or near genes that have been previously identified as associated with pulse pressure (such as PIK3CG) and systolic and diastolic blood pressure (such as C10orf107), showing that L-GATOR is able to prioritize relevant loci in a genome screen.

Joint Analysis of Strain and Parent-of-Origin Effects for Recombinant Inbred Intercrosses Generated from Multiparent Populations with the Collaborative Cross as an Example.

Multiparent populations (MPP) have become popular resources for complex trait mapping because of their wider allelic diversity and larger population size compared with traditional two-way recombinant inbred (RI) strains. In mice, the collaborative cross (CC) is one of the most popular MPP and is derived from eight genetically diverse inbred founder strains. The strategy of generating RI intercrosses (RIX) from MPP in general and from the CC in particular can produce a large number of completely reproducible heterozygote genomes that better represent the (outbred) human population. Since both maternal and paternal haplotypes of each RIX are readily available, RIX is a powerful resource for studying both standing genetic and epigenetic variations of complex traits, in particular, the parent-of-origin (PoO) effects, which are important contributors to many complex traits. Furthermore, most complex traits are affected by >1 genes, where multiple quantitative trait locus mapping could be more advantageous. In this paper, for MPP-RIX data but taking CC-RIX as a working example, we propose a general Bayesian variable selection procedure to simultaneously search for multiple genes with founder allelic effects and PoO effects. The proposed model respects the complex relationship among RIX samples, and the performance of the proposed method is examined by extensive simulations.

SNP marker-based genetic mapping of rust resistance gene in the vegetable cowpea landrace ZN016

Cowpea rust, caused by Uromyces vignae, is one of the most destructive foliar diseases in cowpea [V. unguiculata. (L.) Walp]. Deployment of rust-resistant cultivars is the most environment-friendly and effective way to curb this disease. For this purpose, a fundamental step is to map the rust resistance genes in elite germplasm lines. In the current study, one major and two minor QTLs conferring rust resistance were detected in a 119-line recombinant inbred line (RIL) population derived from a cross between the rust resistant line “ZN016” and a susceptible cultivar “Zhijiang 282”, using the state-of-the-art cowpea iSelect Consortium Array.The major QTL, designated as Ruv1, was mapped to a 12.48 cM interval between the SNP markers 2_01772 and 2_03292 on LG09, which explained 34.8% of the phenotypic variation. The minor QTLs, designated as Ruv2 and Ruv3, were mapped to a 7.01 cM interval on LG7 and a 6.19 cM interval on LG8, which accounted for 13.4% and 11.9% of the phenotypic variation, respectively. This study demonstrates the usefulness of the Consortium SNP Array on complex trait mapping and solidifies the basis for marker-assisted breeding of new cowpea varieties with improved rust resistance.