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

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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

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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:Background:Grain weight is a grain yield component, which is an integrated index of grain length, width and thickness. They are controlled by a large number of quantitative trait loci (QTLs). Besides major QTLs, minor QTLs play an essential role. In our previous studies, QTL analysis for grain length and width was performed using a recombinant inbred line population derived from rice cross TQ/IRBB lines. Two major QTLs were detected, which were located in proximity to GS3 and GW5 that have been cloned. In the present study, QTLs for grain weight and shape were identified using rice populations that were homozygous at GS3 and GW5. Method:Nine populations derived from the indica rice cross TQ/IRBB52 were used. An F10:11population named W1, consisting of 250 families and covering 16 segregating regions, was developed from one residual heterozygote (RH) in the F7generation of Teqing/IRBB52. Three near isogenic line (NIL)-F2 populations, ZH1, ZH2 and ZH3 that comprised 205, 239 and 234 plants, respectively, were derived from three RHs in F10:11. They segregated the target QTL region in an isogenic background. Two NIL populations, HY2 and HY3, were respectively produced from homozygous progeny of the ZH2 and ZH3 populations. Three other NIL-F2 populations, Z1, Z2 and Z3, were established using three RHs having smaller heterozygous segments. QTL analysis for 1000-grain weight (TGW), grain length (GL), grain width (GW), and length/width ratio (LWR) was conducted using QTL IciMapping and SAS procedure with GLM model. Result:A total of 27 QTLs distributed on 12 chromosomes were identified. One QTL cluster, qTGW2/qGL2/qGW2 located in the terminal region of chromosome 2, were selected for further analysis. Two linked QTLs were separated in region Tw31911-RM266. qGL2 was located in Tw31911-Tw32437 and mainly controlled GL and GW. The effects were larger on GL than on GW and the allelic directions were opposite. qTGW2 was located in Tw35293-RM266 and affected TGW, GL and GW with the same allelic direction. Finally, qTGW2 was delimited within a 103-kb region flanked by Tw35293 and Tw35395. Conclusion:qTGW2 with significant effects on TGW, GL and GW was validated and fine-mapped using NIL and NIL-F2 populations. These results provide a basis for map-based cloning of qTGW2 and utilization of qTGW2 in the breeding of high-yielding rice varieties.

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

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