Boby Mathew

Multi-parent advanced generation inter-cross in barley: high-resolution quantitative trait locus mapping for flowering time as a proof of concept

The choice of mapping population is one of the key factors in understanding the genetic effects of complex traits and determines the power and precision of quantitative trait locus (QTL) mapping. We present the results of the first eight-way multi-parent advanced generation inter-cross (MAGIC) doubled haploid (DH) population in barley (Hordeum vulgare ssp. vulgare) applied to mapping complex traits. The results of the genetic architecture within the barley MAGIC population allowed QTL mapping in 533 DH lines with 4,550 single nucleotide polymorphisms (SNPs) with a newly developed mixed linear model in SAS v9.2, incorporating multi-locus analysis and cross validation for flowering time. Two QTL mapping approaches, the binary approach (BA), which is widely used in QTL and association mapping, and a novel haplotype approach (HA) were compared based on their efficiency, precision for QTL detection and estimation of genetic effects. The analysis detected 17 QTLs, five of which were shared between the two approaches; five and two were specifically found with the BA and HA approaches, respectively. The combination of the two mapping approaches enabled high-precision QTL mapping for flowering time. The QTLs corresponded to the genomic regions of major flowering-time genes Vrn-H1, Vrn-H3, HvGI, Ppd-H1, HvFT2, HvFT4, Co1 and linked genes for plant height (sdw1). These results confirm the proof of concept of QTL mapping in a multi-parent population, highlight the advantages and demonstrate that the barley MAGIC DH lines in combination with an advanced QTL mapping approach are valuable resources for mapping complex traits.

A genetic map of cassava (Manihot esculenta Crantz) with integrated physical mapping of immunity-related genes

BackgroundCassava, Manihot esculenta Crantz, is one of the most important crops world-wide representing the staple security for more than one billion of people. The development of dense genetic and physical maps, as the basis for implementing genetic and molecular approaches to accelerate the rate of genetic gains in breeding program represents a significant challenge. A reference genome sequence for cassava has been made recently available and community efforts are underway for improving its quality. Cassava is threatened by several pathogens, but the mechanisms of defense are far from being understood. Besides, there has been a lack of information about the number of genes related to immunity as well as their distribution and genomic organization in the cassava genome.ResultsA high dense genetic map of cassava containing 2,141 SNPs has been constructed. Eighteen linkage groups were resolved with an overall size of 2,571 cM and an average distance of 1.26 cM between markers. More than half of mapped SNPs (57.4%) are located in coding sequences. Physical mapping of scaffolds of cassava whole genome sequence draft using the mapped markers as anchors resulted in the orientation of 687 scaffolds covering 45.6% of the genome. One hundred eighty nine new scaffolds are anchored to the genetic cassava map leading to an extension of the present cassava physical map with 30.7 Mb. Comparative analysis using anchor markers showed strong co-linearity to previously reported cassava genetic and physical maps. In silico based searching for conserved domains allowed the annotation of a repertory of 1,061 cassava genes coding for immunity-related proteins (IRPs). Based on physical map of the corresponding sequencing scaffolds, unambiguous genetic localization was possible for 569 IRPs.ConclusionsThis is the first study reported so far of an integrated high density genetic map using SNPs with integrated genetic and physical localization of newly annotated immunity related genes in cassava. These data build a solid basis for future studies to map and associate markers with single loci or quantitative trait loci for agronomical important traits. The enrichment of the physical map with novel scaffolds is in line with the efforts of the cassava genome sequencing consortium.

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.

Development of new SNP derived cleaved amplified polymorphic sequence marker set and its successful utilization in the genetic analysis of seed color variation in barley

The aim of the present study was to develop a new cost effective PCR based CAPS marker set using advantages of high-throughput SNP genotyping. Initially, SNP survey was made using 20 diverse barley genotypes via 9k iSelect array genotyping that resulted in 6334 polymorphic SNP markers. Principle component analysis using this marker data showed fine differentiation of barley diverse gene pool. Till this end, we developed 200 SNP derived CAPS markers distributed across the genome covering around 991cM with an average marker density of 5.09cM. Further, we genotyped 68 CAPS markers in an F2 population (Cheri×ICB181160) segregating for seed color variation in barley. Genetic mapping of seed color revealed putative linkage of single nuclear gene on chromosome 1H. These findings showed the proof of concept for the development and utility of a newer cost effective genomic tool kit to analyze broader genetic resources of barley worldwide.

Barrier analysis detected genetic discontinuity among Ethiopian barley (Hordeum vulgare L.) landraces due to landscape and human mobility on gene flow

The rate of gene flow is greatly affected by the ability of a species to move from one location to another. In Ethiopia the presence of diverse agro-ecological zones, climatic features, rugged mountains and isolated valleys affected the seed-mediated gene flow among regions. Hence, this study was aimed at investigating and detecting presence of any gene barriers and genetic differentiation among regions. Thus, the study was proposed to test whether the high genetic diversity of barley in Ethiopia was due to a reduced gene flow resulting from geographic barriers and/or effects of human activities. A total of 199 barley landraces collected from 10 different geographic regions and altitudes of Ethiopia were analyzed for 15 molecular markers. A barrier analysis was conducted to identify any geographic areas with pronounced genetic discontinuity between the regions that can be interpreted as barriers to gene flow. The result obtained from analysis of molecular variances indicated high genetic variation within regions rather than between regions. Despite high gene flow among regions, we were able to detect genetic discontinuity due to landscape and human mobility for certain barley growing areas. Hence, it was postulated that these barriers have to be considered in genetic resource sampling strategies.

A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction

Single nucleotide polymorphism (SNP)-heritability estimation is an important topic in several research fields, including animal, plant and human genetics, as well as in ecology. Linear mixed model estimation of SNP-heritability uses the structures of genomic relationships between individuals, which is constructed from genome-wide sets of SNP-markers that are generally weighted equally in their contributions. Proposed methods to handle dependence between SNPs include, “thinning” the marker set by linkage disequilibrium (LD)-pruning, the use of haplotype-tagging of SNPs, and LD-weighting of the SNP-contributions. For improved estimation, we propose a new conceptual framework for genomic relationship matrix, in which Mahalanobis distance-based LD-correction is used in a linear mixed model estimation of SNP-heritability. The superiority of the presented method is illustrated and compared to mixed-model analyses using a VanRaden genomic relationship matrix, a matrix used by GCTA and a matrix employing LD-weighting (as implemented in the LDAK software) in simulated (using real human, rice and cattle genotypes) and real (maize, rice and mice) datasets. Despite of the computational difficulties, our results suggest that by using the proposed method one can improve the accuracy of SNP-heritability estimates in datasets with high LD.

Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation

Key messageA novel reparametrization-based INLA approach as a fast alternative to MCMC for the Bayesian estimation of genetic parameters in multivariate animal model is presented.Abstract Multi-trait genetic parameter estimation is a relevant topic in animal and plant breeding programs because multi-trait analysis can take into account the genetic correlation between different traits and that significantly improves the accuracy of the genetic parameter estimates. Generally, multi-trait analysis is computationally demanding and requires initial estimates of genetic and residual correlations among the traits, while those are difficult to obtain. In this study, we illustrate how to reparametrize covariance matrices of a multivariate animal model/animal models using modified Cholesky decompositions. This reparametrization-based approach is used in the Integrated Nested Laplace Approximation (INLA) methodology to estimate genetic parameters of multivariate animal model. Immediate benefits are: (1) to avoid difficulties of finding good starting values for analysis which can be a problem, for example in Restricted Maximum Likelihood (REML); (2) Bayesian estimation of (co)variance components using INLA is faster to execute than using Markov Chain Monte Carlo (MCMC) especially when realized relationship matrices are dense. The slight drawback is that priors for covariance matrices are assigned for elements of the Cholesky factor but not directly to the covariance matrix elements as in MCMC. Additionally, we illustrate the concordance of the INLA results with the traditional methods like MCMC and REML approaches. We also present results obtained from simulated data sets with replicates and field data in rice.

Integrated nested Laplace approximation inference and cross-validation to tune variance components in estimation of breeding value

The main aim of this study was to compare a number of recently proposed Bayesian and frequentist statistical methods for the estimation of genetic parameters and to apply the cross-validation (CV) approach in order to tune the variance components in simulated and field plant breeding datasets. We were especially interested in whether the CV approach was capable of improving the prediction accuracy of breeding values which have been obtained using the residual (or restricted/reduced) maximum likelihood and Markov chain Monte Carlo estimation tools. We showed that the nonsampling-based Bayesian inference method of integrated nested Laplace approximation (INLA) can be used for rapid and accurate estimation of genetic parameters in linear mixed models with multiple random effects such as additive, dominance, and genotype-by-environment interaction effects. Moreover, we also compared the INLA estimates with results obtained using Markov chain Monte Carlo and restricted maximum likelihood methods. In other studies, K-fold CV is primarily used for comparing method performance; however, here we showed that the K-fold CV method can be used to tune genetic parameters and minimize the prediction error in the estimation of breeding value . We also compared the K-fold CV results with different generalized cross-validation methods which are much faster to compute. Analysis results obtained from field and simulated datasets are presented.

Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population

Flowering time is a well-known complex trait in crops and is influenced by many interacting genes. In this study, Mathew et al. identify two-way and.... Gene-by-gene interactions, also known as epistasis, regulate many complex traits in different species. With the availability of low-cost genotyping it is now possible to study epistasis on a genome-wide scale. However, identifying genome-wide epistasis is a high-dimensional multiple regression problem and needs the application of dimensionality reduction techniques. Flowering Time (FT) in crops is a complex trait that is known to be influenced by many interacting genes and pathways in various crops. In this study, we successfully apply Sure Independence Screening (SIS) for dimensionality reduction to identify two-way and three-way epistasis for the FT trait in a Multiparent Advanced Generation Inter-Cross (MAGIC) barley population using the Bayesian multilocus model. The MAGIC barley population was generated from intercrossing among eight parental lines and thus, offered greater genetic diversity to detect higher-order epistatic interactions. Our results suggest that SIS is an efficient dimensionality reduction approach to detect high-order interactions in a Bayesian multilocus model. We also observe that many of our findings (genomic regions with main or higher-order epistatic effects) overlap with known candidate genes that have been already reported in barley and closely related species for the FT trait.

Major Novel QTL for Resistance to Cassava Bacterial Blight Identified through a Multi-Environmental Analysis

Cassava, Manihot esculenta Crantz, has been positioned as one of the most promising crops world-wide representing the staple security for more than one billion people mainly in poor countries. Cassava production is constantly threatened by several diseases, including cassava bacterial blight (CBB) caused by Xanthomonas axonopodis pv. manihotis (Xam), it is the most destructive disease causing heavy yield losses. Here, we report the detection and localization on the genetic map of cassava QTL (Quantitative Trait Loci) conferring resistance to CBB. An F1 mapping population of 117 full sibs was tested for resistance to two Xam strains (Xam318 and Xam681) at two locations in Colombia: La Vega, Cundinamarca and Arauca. The evaluation was conducted in rainy and dry seasons and additional tests were carried out under controlled greenhouse conditions. The phenotypic evaluation of the response to Xam revealed continuous variation. Based on composite interval mapping analysis, 5 strain-specific QTL for resistance to Xam explaining between 15.8 and 22.1% of phenotypic variance, were detected and localized on a high resolution SNP-based genetic map of cassava. Four of them show stability among the two evaluated seasons. Genotype by environment analysis detected three QTL by environment interactions and the broad sense heritability for Xam318 and Xam681 were 20 and 53%, respectively. DNA sequence analysis of the QTL intervals revealed 29 candidate defense-related genes (CDRGs), and two of them contain domains related to plant immunity proteins, such as NB-ARC-LRR and WRKY.