Dataset Information

Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

ABSTRACT: Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

SUBMITTER: Spindel J

PROVIDER: S-EPMC4334555 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

Spindel Jennifer J Begum Hasina H Akdemir Deniz D Virk Parminder P Collard Bertrand B Redoña Edilberto E Atlin Gary G Jannink Jean-Luc JL McCouch Susan R SR

PLoS genetics 20150217 2

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the Int ...[more]

PMID: 25689273

Similar Datasets

Project description:BackgroundArsenic (As) is an unwanted toxic mineral that threatens the major rice-growing regions in the world, especially in South Asia. Rice production in Bangladesh and India depends on As-contaminated groundwater sources for irrigating paddy fields, resulting in elevated amounts of As in the topsoil. Arsenic accumulating in rice plants has a significant negative effect on human and animal health. Here, we present a quantitative trait locus (QTL) mapping study to identify candidate genes conferring As toxicity tolerance and accumulation in rice (Oryza sativa L.) seedlings. An early backcross breeding population consisting of 194 lines derived from a cross between WTR1 (indica) and Hao-an-nong (japonica) was grown in hydroponics for 25 days, from the seventh day exposed to an environmentally relevant concentration of 10 ppm As.ResultsArsenic toxicity leads to significantly negative plant responses, including reduced biomass, stunted plant growth, reduced leaf chlorophyll content, and increased shoot As concentration ranging from 9 to 20 mg kg- 1. Marker-trait association was determined for seven As-related traits using 704 single nucleotide polymorphism (SNP) markers generated from a 6 K SNP-array. One QTL was mapped on chromosome 1 for relative chlorophyll content, two QTLs for As content in roots were mapped on chromosome 8, and six QTLs for As content in shoots were mapped on chromosomes 2, 5, 6, and 9. Using the whole-genome sequence of the parents, we narrowed down the number of candidate genes associated with the QTL intervals based on the existence of a non-synonymous mutation in genes between the parental lines. Also, by using publicly available gene expression profiles for As stress, we further narrowed down the number of candidate genes in the QTL intervals by comparing the expression profiles of genes under As stress and control conditions. Twenty-five genes showing transcription regulation were considered as candidate gene nominees for As toxicity-related traits.ConclusionsOur study provides insight into the genetic basis of As tolerance and uptake in the early seedling stage of rice. Comparing our findings with the previously reported QTLs for As toxicity stress in rice, we identified some novel and co-localized QTLs associated with As stress. Also, the mapped QTLs harbor gene models of known function associated with stress responses, metal homeostasis, and transporter activity in rice. Overall, our findings will assist breeders with initial marker information to develop suitable varieties for As-contaminated ecosystems.

Project description:BackgroundBreeding of conventional and hybrid rice (Oryza sativa L.) have solved hunger problems and increased farmers' income in the world. Molecular markers have been widely used in marker-assisted breeding and identification of larger numbers of different bred varieties in the past decades. The recently developed SNP markers are applied for more stable and detectable compared with other markers. But the cost of genotyping lots SNPs is high. So, it is essential to select less representative SNPs and inexpensive detecting methods to lower the cost and accelerate variety identification and breeding process. KASP (Kompetitive Allele-Specific PCR) is a flexible method to detect the SNPs, and large number of KASP markers have been widely used in variety identification and breeding. However, the ability of less KASP markers on massive variety identification and breeding remains unknown.ResultsHere, 48 KASP markers were selected from 378 markers to classify and analyze 518 varieties including conventional and hybrid rice. Through analyzing the population structure, the 48 markers could almost represent the 378 markers. In terms of variety identification, the 48 KASP markers had a 100% discrimination rate in 53 conventional indica varieties and 193 hybrid varieties, while they could distinguish 89.1% conventional japonica rice from different breeding institutes. Two more markers added would increase the ratio from 68.38 to 77.94%. Additionally, the 48 markers could be used for classification of subpopulations in the bred variety. Also, 8 markers had almost completely different genotypes between japonica and indica, and 3 markers were found to be very important for japonica hybrid rice. In hybrid varieties, the heterozygosity of chromosomes 3, 6 and 11 was relatively higher than others.ConclusionsOur results showed that 48 KASP markers could be used to identify rice varieties, and the panel we tested could provide a database for breeders to identify new breeding lines. Also, the specific markers we found were useful for marker-assisted breeding in rice, including conventional and hybrid.

Dataset Information

Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

Publications

Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets