Multi-trait Mixed Model Research Articles

Genetic parameters for cloacal gland, sexual maturity, reproductive organs weight, and body weight in meat-type quail

Selection to increase body weight in poultry can hamper reproduction traits and compromise production efficiency. Thus, attention to reproduction traits is essential to improving the sustainability of breeding programs. Data from a domestic quail breeding program for meat production were used to estimate genetic parameters. We analyzed five traits: 4-week body weight, age at sexual maturity for males and females, cloacal gland area, female, and male reproductive organs weights. A multi-trait mixed model analysis with fixed effects of generation/hatch was performed, assuming environmental covariance equals zero for sex-limited traits. Heritability estimates range from low to moderate for male sexual maturity and cloacal gland area, and high for other traits. Intersexual genetic correlation for age at sexual maturity is positive, which can lead to correlated responses in the other sex. Reproductive organs weights are genetically correlated with body weight, but not significantly between sexes and nor with sexual maturity. Genetic correlations for the cloacal gland area were positive with body weight and negative with age at sexual maturity of males and females, demonstrating a potential use of this trait for selection with favorable outcomes in reproduction. The use of the cloacal gland area can be used in the same way as the scrotal circumference in mammals, improving female reproduction traits by selecting a trait recorded in males.

The influence of foreign lineages in the genetic component of reproductive traits in Criollo Argentino mares: A 30-year study

The Criollo horse is the most important equine breed in Argentina. These horses are characterized by their strength, resistance, and increased fertility even at old ages, and they are also associated with Argentine tradition and livestock heritage. However, over the last 70 years, the Criollo Argentino breed has been influenced by other South American Criollo horses from Brazil, Uruguay, or Chile. In this study, we aimed to determine the genetic influence of foreign individuals on the reproductive performance of Criollo Argentino mares. To achieve this, we analyzed a large dataset including pedigree records of nearly 212,000 individuals. For the analysis, the individuals were organised into clusters based on their genetic origin (foreign or native Criollos). We then performed a comprehensive genetic characterization of each group, as well as an estimation of the estimated breeding values (EBVs), heritabilities, and genetic correlations for the three traits, using a multi-trait mixed model. Our results demonstrate a positive trend toward the introgression of foreign breeding lineages in the Criollo Argentino population. However, we also quantified a low-to-moderate genetic influence on all the fertility traits analysed, and the absence of any negative effect associated with inbreeding. Despite the fact that the genetic origin produced certain differences between foreign and native individuals in terms of fertility at the beginning of the study, the use of foreign lineages did not significantly change the fertility of the Criollo Argentino mares. To our knowledge, this is the first study analysing the fertility of Criollo horses from a genetic point of view.

Investigating functional relationships among health and fertility traits in dairy cows

The association between health and pregnancy per artificial insemination, maintenance of pregnancy, and interval to pregnancy in dairy cattle is well documented. However, the understanding of the causal relationships among health and fertility traits is limited. Although information on heritabilities and genetic correlations between traits is key for the development of multiple-trait selection strategies, only knowledge about functional and causal relationships among them allow the prediction of the consequences of external interventions such as management decisions. Thus, this research was carried out to investigate potential causal relationships among multiple health events (retained fetal membranes [RFM], metritis [MET], clinical endometritis [CE], lameness [LS]) and fertility indicators including resumption of ovarian cyclicity (CY) by 50 days in milk and pregnancy on day 60 (P60) after the first artificial insemination in Holstein cows. Mixed-effects structural equation models (SEM) were fitted conditionally on a causal structure among traits, represented by a directed acyclic graph, inferred from the data using the inductive causation (IC) algorithm. The posterior mean and standard deviation of heritability for RFM, MET, CE, LS, CY and P60 were 0.20 ± 0.07, 0.09 ± 0.02, 0.10 ± 0.04, 0.35 ± 0.04, 0.15 ± 0.03, and 0.10 ± 0.03, respectively. The moderate estimates of heritability found for CY and P60 suggest that selective breeding could be effective for the genetic improvement of these traits. The estimates of genetic correlation among traits ranged from -0.82 ± 0.07 (MET and CY) to 0.70 ± 0.13 (RFM and MET). Biological considerations and results from the inductive causation algorithm analysis retrieved six potential causal connections between traits: RFM→MET; RFM→CE; LS→CE; CE→P60; CY→P60; and CY→CE. In addition to the data-driven network recovered, some additional connections or variations on their directions were also considered on the SEM analyses based on biological and chronological knowledge. Overall, the SEM results were very similar to those obtained from the traditional multi-trait mixed model analysis. The advantage of the SEM analysis is that it sheds some light onto the potential causal relationships between traits, which can aid better decisions to manage events occurring during the postpartum of dairy cattle.

MODELOS MULTIVARIADOS EN LA BÚSQUEDA DE REGIONES GENÓMICAS PARA RESISTENCIA A MAL DE RÍO CUARTO Y BACTERIOSIS EN MAÍZ

Maize (Zea Mays L.) production has been greatly benefited from the improvement of inbred lines in regard to the resistance to diseases. However, the absence of resistant genotypes to bacteriosis is remarkable. The aim of the study was to identify genomic regions for resistance to Mal de Río Cuarto (MRC) and to bacterial disease (BD) in a diverse maize germplasm evaluated in the Argentinian region where MRC virus is endemic. A maize diverse population was assessed for both diseases during the 2019-2020 crop season. Incidence and severity of MRC and BD were estimated for each line and a genome wide association study (GWAS) was conducted with 78,376 SNP markers. A multi-trait mixed linear model was used for simultaneous evaluation of resistance to MRC and BD in the scored lines. The germplasm showed high genetic variability for both MRC and BD resistance. No significant genetic correlation was observed between the response to both diseases. Promising genomic regions for resistance to MRC and BD were identified and will be confirmed in further trials. Key words: maize disease; genome wide association study; SNP; multi-trait model

MODELOS MULTIVARIADOS EN LA BÚSQUEDA DE REGIONES GENÓMICAS PARA RESISTENCIA A MAL DE RÍO CUARTO Y BACTERIOSIS EN MAÍZ

Maize (Zea Mays L.) production has been greatly benefited from the improvement of inbred lines in regard to the resistance to diseases. However, the absence of resistant genotypes to bacteriosis is remarkable. The aim of the study was to identify genomic regions for resistance to Mal de Río Cuarto (MRC) and to bacterial disease (BD) in a diverse maize germplasm evaluated in the Argentinian region where MRC virus is endemic. A maize diverse population was assessed for both diseases during the 2019-2020 crop season. Incidence and severity of MRC and BD were estimated for each line and a genome wide association study (GWAS) was conducted with 78,376 SNP markers. A multi-trait mixed linear model was used for simultaneous evaluation of resistance to MRC and BD in the scored lines. The germplasm showed high genetic variability for both MRC and BD resistance. No significant genetic correlation was observed between the response to both diseases. Promising genomic regions for resistance to MRC and BD were identified and will be confirmed in further trials. Key words: maize disease; genome wide association study; SNP; multi-trait model

Identifying inbred lines with resistance to endemic diseases in exotic maize germplasm

AbstractMal de Rio Cuarto (MRC) and common rust (CR), caused by Mal de Rio Cuarto virus (MRCV) and Puccinia sorghi, respectively, are endemic diseases affecting maize (Zea mays L.) production in Argentina. Exotic maize germplasm is an important source of resistance to these diseases. The aim of this work was to identify maize lines that exhibit MRC and CR resistance. A multienvironment trial was performed to phenotypically assess a diverse panel of inbred lines from the International Maize and Wheat Improvement Center (CIMMYT). The maize lines were evaluated using a disease severity index (DSI) for MRC and CR in the central area of Argentina. A multitrait mixed linear model (MLM) was used to identify the lines with the best performance for both diseases and estimate genetic parameters. No correlation of resistance between MRC and CR was found among the tested lines. Additionally, best linear unbiased predictions (BLUPs) of genotypic effects were used as response variable to perform a genome‐wide association study (GWAS). The GWAS revealed promising alleles for maize breeding, two associated with MRC and three with CR. Lines with lower DSI for MRC and CR were identified as novel materials for incorporating resistance to the local germplasm.

Reconstruction of Networks with Direct and Indirect Genetic Effects.

Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e., through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example, when trying to improve crop yield and simultaneously control plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most current methods require all genetic variance to be explained by a small number of quantitative trait loci (QTL) with fixed effects. Only a few authors have considered the "missing heritability" case, where contributions of many undetectable QTL are modeled with random effects. Usually, these are treated as nuisance terms that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such an MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here, we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits; and (2) we can test the existence of direct genetic effects, and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.

Single and Multi-trait GWAS Identify Genetic Factors Associated with Production Traits in Common Bean Under Abiotic Stress Environments.

The genetic improvement of economically important production traits of dry bean (Phaseolus vulgaris L.), for geographic regions where production is threatened by drought and high temperature stress, is challenging because of the complex genetic nature of these traits. Large scale SNP data sets for the two major gene pools of bean, Andean and Middle American, were developed by mapping multiple pools of genotype-by-sequencing reads and identifying over 200k SNPs for each gene pool against the most recent assembly of the P. vulgaris genome sequence. Moderately sized Bean Abiotic Stress Evaluation (BASE) panels, consisting of genotypes appropriate for production in Central America and Africa, were assembled. Phylogenetic analyses demonstrated the BASE populations represented broad genetic diversity for the appropriate races within the two gene pools. Joint mixed linear model genome-wide association studies with data from multiple locations discovered genetic factors associated with four production traits in both heat and drought stress environments using the BASE panels. Pleiotropic genetic factors were discovered using a multi-trait mixed model analysis. SNPs within or near candidate genes associated with hormone signaling, epigenetic regulation, and ROS detoxification under stress conditions were identified and can be used as genetic markers in dry bean breeding programs.

Genome-wide association study of seed protein, oil and amino acid contents in soybean from maturity groups I to IV

Key messageGenomic regions associated with seed protein, oil and amino acid contents were identified by genome-wide association analyses. Geographic distributions of haplotypes indicate scope of improvement of these traits.Soybean [Glycine max (L.) Merr.] protein and oil are used worldwide in feed, food and industrial materials. Increasing seed protein and oil contents is important; however, protein content is generally negatively correlated with oil content. We conducted a genome-wide association study using phenotypic data collected from five environments for 621 accessions in maturity groups I–IV and 34,014 markers to identify quantitative trait loci (QTL) for seed content of protein, oil and several essential amino acids. Three and five genomic regions were associated with seed protein and oil contents, respectively. One, three, one and four genomic regions were associated with cysteine, methionine, lysine and threonine content (g kg−1 crude protein), respectively. As previously shown, QTL on chromosomes 15 and 20 were associated with seed protein and oil contents, with both exhibiting opposite effects on the two traits, and the chromosome 20 QTL having the most significant effect. A multi-trait mixed model identified trait-specific QTL. A QTL on chromosome 5 increased oil with no effect on protein content, and a QTL on chromosome 10 increased protein content with little effect on oil content. The chromosome 10 QTL co-localized with maturity gene E2/GmGIa. Identification of trait-specific QTL indicates feasibility to reduce the negative correlation between protein and oil contents. Haplotype blocks were defined at the QTL identified on chromosomes 5, 10, 15 and 20. Frequencies of positive effect haplotypes varied across maturity groups and geographic regions, providing guidance on which alleles have potential to contribute to soybean improvement for specific regions.

Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS

The present study was conducted to study the genetic architecture of grain micronutrients (Zn, Fe and β-carotene contents), grain protein content and four yield traits in a spring wheat reference set comprising 246 genotypes. Phenotypic data on these traits recorded at two locations and the genotyping data for 17,937 SNP markers (obtained through outsourcing) were used for genome wide association study, which gave following results after Bonferroni correction using four methods: (1) single locus single trait analysis gave 136 marker-trait associations; (2) multi-locus mixed model gave 587 MTAs; (3) multi-trait mixed model gave 28 MTAs and (4) matrix-variate linear mixed model gave 33 MTAs. As many as 73 epistatic interactions were also detected. Keeping all the results in mind, nine most important MTAs were selected for biofortification. These markers were associated with three traits (GPC, GFeC and GYPP). These MTAs can be used in wheat improvement programs either using marker-assisted recurrent selection or pseudo-backcrossing method.

Genome wide association mapping of agro-morphological traits among a diverse collection of finger millet (Eleusine coracana L.) genotypes using SNP markers.

Finger millet (Eleusine coracana L.) is an important dry-land cereal in Asia and Africa because of its ability to provide assured harvest under extreme dry conditions and excellent nutritional properties. However, the genetic improvement of the crop is lacking in the absence of suitable genomic resources for reliable genotype-phenotype associations. Keeping this in view, a diverse global finger millet germplasm collection of 113 accessions was evaluated for 14 agro-morphological characters in two environments viz. ICAR-Vivekananda Institute of Hill Agriculture, Almora (E1) and Crop Research Centre (CRC), GBPUA&T, Pantnagar (E2), India. Principal component analysis and cluster analysis of phenotypic data separated the Indian and exotic accessions into two separate groups. Previously generated SNPs through genotyping by sequencing (GBS) were used for association mapping to identify reliable marker(s) linked to grain yield and its component traits. The marker trait associations were determined using single locus single trait (SLST), multi-locus mixed model (MLMM) and multi-trait mixed model (MTMM) approaches. SLST led to the identification of 20 marker-trait associations (MTAs) (p value<0.01 and <0.001) for 5 traits. While advanced models, MLMM and MTMM resulted in additional 36 and 53 MTAs, respectively. Nine MTAs were common out of total 109 associations in all the three mapping approaches (SLST, MLMM and MTMM). Among these nine SNPs, five SNP sequences showed homology to candidate genes of Oryza sativa (Rice) and Setaria italica (Foxtail millet), which play an important role in flowering, maturity and grain yield. In addition, 67 and 14 epistatic interactions were identified for 10 and 7 traits at E1 and E2 locations, respectively. Hence, the 109 novel SNPs associated with important agro-morphological traits, reported for the first time in this study could be precisely utilized in finger millet genetic improvement after validation.

Estimation of genetic parameters for temperament in Jeju crossbred horses.

ObjectiveTemperament can be defined as a type of behavioral tendency that appears in a relatively stable manner in responses to various external stimuli over time. The aim of this study was to estimate genetic parameters for the records of temperament testing that are used to improve the temperament of Jeju crossbred (Jeju×Thoroughbred) horses.MethodsThis study was conducted using 205 horses (101 females and 104 males) produced between 2010 and 2015. The experimental animals were imprinted and tamed according to the Manual for Horse Taming and Evaluation for Therapeutic Riding Horses and evaluated according to the categories for temperament testing (gentleness, patience, aggressiveness, sensitivity, and friendliness) between 15 months and 18 months of age. Each category was scored on a five-point linear scale. Genetic parameters for the test categories were analyzed using a multi-trait mixed model with repeated records. The ASReml program was used to analyze the data.ResultsThe heritability of gentleness, patience, aggressiveness, sensitivity and friendliness ranged from 0.08 to 0.53. The standard errors of estimated heritability ranged from 0.13 to 0.17. The test categories showed high genetic correlations with each other, ranging from 0.96 to 0.99 and high repeatability, ranging from 0.70 to 0.73.ConclusionThe results of this study showed that the test categories had moderate heritability and high genetic correlations, but additional studies may be necessary to use the results for the improvement programs of the temperament of Jeju crossbred horses.

Accuracies of univariate and multivariate genomic prediction models in African cassava

BackgroundGenomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a single-environment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies.ResultsIn Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model.ConclusionsWe recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

Use of multi-trait and random regression models to identify genetic variation in tolerance to porcine reproductive and respiratory syndrome virus

BackgroundA host can adopt two response strategies to infection: resistance (reduce pathogen load) and tolerance (minimize impact of infection on performance). Both strategies may be under genetic control and could thus be targeted for genetic improvement. Although there is evidence that supports a genetic basis for resistance to porcine reproductive and respiratory syndrome (PRRS), it is not known whether pigs also differ genetically in tolerance. We determined to what extent pigs that have been shown to vary genetically in resistance to PRRS also exhibit genetic variation in tolerance. Multi-trait linear mixed models and random regression sire models were fitted to PRRS Host Genetics Consortium data from 1320 weaned pigs (offspring of 54 sires) that were experimentally infected with a virulent strain of PRRS virus to obtain genetic parameter estimates for resistance and tolerance. Resistance was defined as the inverse of within-host viral load (VL) from 0 to 21 (VL21) or 0 to 42 (VL42) days post-infection and tolerance as the slope of the reaction-norm of average daily gain (ADG21, ADG42) on VL21 or VL42.ResultsMulti-trait analysis of ADG associated with either low or high VL was not indicative of genetic variation in tolerance. Similarly, random regression models for ADG21 and ADG42 with a tolerance slope fitted for each sire did not result in a better fit to the data than a model without genetic variation in tolerance. However, the distribution of data around average VL suggested possible confounding between level and slope estimates of the regression lines. Augmenting the data with simulated growth rates of non-infected half-sibs (ADG0) helped resolve this statistical confounding and indicated that genetic variation in tolerance to PRRS may exist if genetic correlations between ADG0 and ADG21 or ADG42 are low to moderate.ConclusionsEvidence for genetic variation in tolerance of pigs to PRRS was weak when based on data from infected piglets only. However, simulations indicated that genetic variance in tolerance may exist and could be detected if comparable data on uninfected relatives were available. In conclusion, of the two defense strategies, genetics of tolerance is more difficult to elucidate than genetics of resistance.

Genetic parameters for milk yield and reproduction traits in the Chilean Dairy Overo Colorado cattle breed

The objective of this study was to estimate genetic parameters for milk production and reproduction traits, with phenotypic and pedigree information from the Dairy Overo Colorado breed from southern Chile. Single and multi-trait mixed models were used to analyze the data. Analyses were performed using ASReml software. Heritability estimates for milk yield, fat yield, protein yield, fat content, protein content, age at first calving and calving interval, were 0.29, 0.34, 0.30, 0.56, 0.56, 0.01 and 0.002, respectively. Genetic correlations between yield traits were high and positive (≥ 0.81). Genetic correlations of milk yield with fat content (-0.32) and protein content (-0.30) were unfavorable. Genetic correlation estimates between age at first calving and yield traits were negative between -0.71 and -0.29. Phenotypic correlations between calving interval and milk yield traits were slightly positive (0.06-0.09), suggesting a weak relationship between reproduction and yield in this population. Genetic variation is considerable for milk production traits. Genetic improvement for age at first calving and calving interval may be slow, because small differences between animals and low reliabilities are expected for estimated breeding values for these traits due to low heritabilities. The design of an efficient breeding program for yield traits based on artificial insemination for this population is feasible using either progeny testing or genomic breeding value prediction for selecting sires.

Alternative approaches to evaluation of cow efficiency

The purpose of this study was to evaluate alternative expressions of genetic merit for cow efficiency. Weights of Pinzgauer cattle taken at birth, weaning, and maturity were extracted from the South African National Database. Average daily gain from birth to weaning (ADG) and cow weight (CWT) were analyzed with a multi-trait mixed model. The model included direct and maternal genetic effects, a permanent environmental effect attributable to dams on ADG, a direct genetic effect and a permanent environmental effect attributable to there being multiple observations from the same cow on CWT as random effects. Heritability estimates for direct and maternal additive effects on ADG were 0.27 ± 0.04 and 0.06 ± 0.02, respectively. The estimated heritability for CWT was 0.45 ± 0.06. Estimates of repeatability for ADG and CWT were 0.42 and 0.67, respectively. Estimated breeding values based on the preceding results and using the maternal genetic effect on ADG as a proxy for the direct genetic effect on milk production were combined in six indexes of cow efficiency. These indexes sought to increase output and decrease input simultaneously, to increase output holding input constantly, and to hold input constant while decreasing input. The diversity of emphasis applied across these indexes suggests the need for due diligence in developing breeding objectives for improvement of cow efficiency. Indexes that are consistent with the econometric definition of efficiency and seek to simultaneously increase output and reduce input are recommended. ______________________________________________________________________________________ Keywords : genetic parameters, multiple trait selection, Pinzgauer, selection index

Genetic architecture of plant stress resistance: multi‐trait genome‐wide association mapping

Summary Plants are exposed to combinations of various biotic and abiotic stresses, but stress responses are usually investigated for single stresses only.Here, we investigated the genetic architecture underlying plant responses to 11 single stresses and several of their combinations by phenotyping 350 Arabidopsis thaliana accessions. A set of 214 000 single nucleotide polymorphisms (SNPs) was screened for marker‐trait associations in genome‐wide association (GWA) analyses using tailored multi‐trait mixed models.Stress responses that share phytohormonal signaling pathways also share genetic architecture underlying these responses. After removing the effects of general robustness, for the 30 most significant SNPs, average quantitative trait locus (QTL) effect sizes were larger for dual stresses than for single stresses.Plants appear to deploy broad‐spectrum defensive mechanisms influencing multiple traits in response to combined stresses. Association analyses identified QTLs with contrasting and with similar responses to biotic vs abiotic stresses, and below‐ground vs above‐ground stresses. Our approach allowed for an unprecedented comprehensive genetic analysis of how plants deal with a wide spectrum of stress conditions.

The genetic architecture of freezing tolerance varies across the range of Arabidopsis thaliana.

The capacity to tolerate freezing temperatures limits the geographical distribution of many plants, including several species of agricultural importance. However, the genes involved in freezing tolerance remain largely unknown. Here, we describe the variation in constitutive freezing tolerance that occurs among worldwide accessions of Arabidopsis thaliana. We found that although plants from high latitudes tend to be more freezing tolerant than plants from low latitudes, the environmental factors that shape cold adaptation differ across the species range. Consistent with this, we found that the genetic architecture of freezing tolerance also differs across its range. Conventional genome-wide association studies helped identify a priori and other promising candidate genes. However, simultaneously modelling climate variables and freezing tolerance together pinpointed other excellent a priori candidate genes. This suggests that if the selective factor underlying phenotypic variation is known, multi-trait mixed models may aid in identifying the genes that underlie adaptation.

Genome Wide Single Locus Single Trait, Multi-Locus and Multi-Trait Association Mapping for Some Important Agronomic Traits in Common Wheat (T. aestivum L.).

Genome wide association study (GWAS) was conducted for 14 agronomic traits in wheat following widely used single locus single trait (SLST) approach, and two recent approaches viz. multi locus mixed model (MLMM), and multi-trait mixed model (MTMM). Association panel consisted of 230 diverse Indian bread wheat cultivars (released during 1910–2006 for commercial cultivation in different agro-climatic regions in India). Three years phenotypic data for 14 traits and genotyping data for 250 SSR markers (distributed across all the 21 wheat chromosomes) was utilized for GWAS. Using SLST, as many as 213 MTAs (p ≤ 0.05, 129 SSRs) were identified for 14 traits, however, only 10 MTAs (~9%; 10 out of 123 MTAs) qualified FDR criteria; these MTAs did not show any linkage drag. Interestingly, these genomic regions were coincident with the genomic regions that were already known to harbor QTLs for same or related agronomic traits. Using MLMM and MTMM, many more QTLs and markers were identified; 22 MTAs (19 QTLs, 21 markers) using MLMM, and 58 MTAs (29 QTLs, 40 markers) using MTMM were identified. In addition, 63 epistatic QTLs were also identified for 13 of the 14 traits, flag leaf length (FLL) being the only exception. Clearly, the power of association mapping improved due to MLMM and MTMM analyses. The epistatic interactions detected during the present study also provided better insight into genetic architecture of the 14 traits that were examined during the present study. Following eight wheat genotypes carried desirable alleles of QTLs for one or more traits, WH542, NI345, NI170, Sharbati Sonora, A90, HW1085, HYB11, and DWR39 (Pragati). These genotypes and the markers associated with important QTLs for major traits can be used in wheat improvement programs either using marker-assisted recurrent selection (MARS) or pseudo-backcrossing method.

Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model.

Genome-wide association studies (GWAS) have led to the identification of many genetic variants associated with complex diseases in the past 10 years. Penalization methods, with significant numerical and statistical advantages, have been extensively adopted in analyzing GWAS. This study has been partly motivated by the analysis of Genetic Analysis Workshop (GAW) 18 data, which have two notable characteristics. First, the subjects are from a small number of pedigrees and hence related. Second, for each subject, multiple correlated traits have been measured. Most of the existing penalization methods assume independence between subjects and traits and can be suboptimal. There are a few methods in the literature based on mixed modeling that can accommodate correlations. However, they cannot fully accommodate the two types of correlations while conducting effective marker selection. In this study, we develop a penalized multitrait mixed modeling approach. It accommodates the two different types of correlations and includes several existing methods as special cases. Effective penalization is adopted for marker selection. Simulation demonstrates its satisfactory performance. The GAW 18 data are analyzed using the proposed method.