The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection

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Abstract

Genomic selection is a promising research area due to its practical application in breeding. In this study, impact of realized genetic relationship and linkage disequilibrium (LD) on marker density and training population size required was investigated and their impact on practical application was further discussed. This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17). Two populations were genotyped with different marker sets at different density: IBM Syn4 and IBM Syn10. A high-density marker set in Syn10 was imputed into the Syn4 population with low marker density. Seven different prediction scenarios were carried out with a random regression best linear unbiased prediction (RR-BLUP) model. The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy. Taken the short-term cost for consideration, relationship information is more valuable than LD information. Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications.

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Most cited references 16

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High-throughput genotyping by whole-genome resequencing.

Xuehui Huang, Qi Feng, Qian Qian … (2009)

The next-generation sequencing technology coupled with the growing number of genome sequences opens the opportunity to redesign genotyping strategies for more effective genetic mapping and genome analysis. We have developed a high-throughput method for genotyping recombinant populations utilizing whole-genome resequencing data generated by the Illumina Genome Analyzer. A sliding window approach is designed to collectively examine genome-wide single nucleotide polymorphisms for genotype calling and recombination breakpoint determination. Using this method, we constructed a genetic map for 150 rice recombinant inbred lines with an expected genotype calling accuracy of 99.94% and a resolution of recombination breakpoints within an average of 40 kb. In comparison to the genetic map constructed with 287 PCR-based markers for the rice population, the sequencing-based method was approximately 20x faster in data collection and 35x more precise in recombination breakpoint determination. Using the sequencing-based genetic map, we located a quantitative trait locus of large effect on plant height in a 100-kb region containing the rice "green revolution" gene. Through computer simulation, we demonstrate that the method is robust for different types of mapping populations derived from organisms with variable quality of genome sequences and is feasible for organisms with large genome sizes and low polymorphisms. With continuous advances in sequencing technologies, this genome-based method may replace the conventional marker-based genotyping approach to provide a powerful tool for large-scale gene discovery and for addressing a wide range of biological questions.

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Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

Hans D Daetwyler, Beatriz Villanueva, John A. Woolliams (2008)

Background The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.

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Is Open Access

A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome

Martin W. Ganal, Gregor Durstewitz, Andreas Polley … (2011)

SNP genotyping arrays have been useful for many applications that require a large number of molecular markers such as high-density genetic mapping, genome-wide association studies (GWAS), and genomic selection. We report the establishment of a large maize SNP array and its use for diversity analysis and high density linkage mapping. The markers, taken from more than 800,000 SNPs, were selected to be preferentially located in genes and evenly distributed across the genome. The array was tested with a set of maize germplasm including North American and European inbred lines, parent/F1 combinations, and distantly related teosinte material. A total of 49,585 markers, including 33,417 within 17,520 different genes and 16,168 outside genes, were of good quality for genotyping, with an average failure rate of 4% and rates up to 8% in specific germplasm. To demonstrate this array's use in genetic mapping and for the independent validation of the B73 sequence assembly, two intermated maize recombinant inbred line populations – IBM (B73×Mo17) and LHRF (F2×F252) – were genotyped to establish two high density linkage maps with 20,913 and 14,524 markers respectively. 172 mapped markers were absent in the current B73 assembly and their placement can be used for future improvements of the B73 reference sequence. Colinearity of the genetic and physical maps was mostly conserved with some exceptions that suggest errors in the B73 assembly. Five major regions containing non-colinearities were identified on chromosomes 2, 3, 6, 7 and 9, and are supported by both independent genetic maps. Four additional non-colinear regions were found on the LHRF map only; they may be due to a lower density of IBM markers in those regions or to true structural rearrangements between lines. Given the array's high quality, it will be a valuable resource for maize genetics and many aspects of maize breeding.

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Author and article information

Contributors

Lewis Lukens: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 6 July 2015

Publication date Collection: 2015

Volume: 10

Issue: 7

Electronic Location Identifier: e0132379

Affiliations

[1 ]Maize Research Institute of Sichuan Agricultural University, Chengdu, China

[2 ]Guangzhou Genedenovo Biotechnology Co., Ltd, Guangzhou, China

[3 ]Guangxi Maize Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, China

[4 ]Institute of Plant Protection, Sichuan Academy of Agricultural Sciences, Chengdu, China

[5 ]Department of Agronomy, Iowa State University, Ames, IA, United States of America

[6 ]Interdepartmental genetics program, Iowa State University, Ames, IA, United States of America

University of Guelph, CANADA

Author notes

Competing Interests: HZ and XZ are employees of Guangzhou Genedenovo Biotechnology Co. There are no patents, products in development, or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Conceived and designed the experiments: H. Liu GP KH TL ZZ. Performed the experiments: H. Liu HZ YW JZ TZ XZ YS YZ SL. Analyzed the data: H. Liu HZ YW JZ TZ XZ YS YZ SL. Contributed reagents/materials/analysis tools: XL TL. Wrote the paper: H. Liu XL H. Lin TL GP.

* E-mail: pangt@ 123456sicau.edu.cn

Article

Publisher ID: PONE-D-14-47675

DOI: 10.1371/journal.pone.0132379

PMC ID: 4493124

PubMed ID: 26148055

SO-VID: 76ddb856-b06d-40ba-890d-cc31995b4767

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

History

Date received : 23 October 2014

Date accepted : 13 June 2015

Page count

Figures: 2, Tables: 3, Pages: 13

Funding

Funding for this research was provided by National Natural Science Foundation of China (31201221, 31271740, 31060191); the National Hi-Tech program of China (2012AA10307); Maize Research & Development Center, CARS-02-07; and the Key Laboratory Construction Program of Guangxi (12-071-09). Guangzhou Genedenovo Biotechnology Co., Ltd provided support in the form of salaries for authors HZ and XZ, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

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Data Availability All relevant data are within the paper and its Supporting Information files.

The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection

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