Prospects of whole-genome sequence data in animal and plant breeding

Research output: Thesisinternal PhD, WUAcademic

Abstract

The rapid decrease in costs of DNA sequencing implies that whole-genome sequence data will be widely available in the coming few years. Whole-genome sequence data includes all base-pairs on the genome that show variation in the sequenced population. Consequently, it is assumed that the causal mutations (e.g. quantitative trait loci; QTL) are included, which allows testing a given trait directly for association with a QTL, and might lead to discovery of new QTL or higher accuracies in genomic predictions compared to currently available marker panels. The main aim of this thesis was to investigate the benefits of using whole-genome sequence data in breeding of animals and plants compared to currently available marker panels. First the potential and benefits of using whole-genome sequence data were studied in (dairy) cattle. Accuracy of genotype imputation to whole-genome sequence data was generally high, depending on the used marker panel. In contrast to the expectations, genomic prediction showed no advantage of using whole-genome sequence data compared to a high density marker panel. Thereafter, the use of whole-genome sequence data for QTL detection in tomato (S. Lycopersicum) was studied. In a recombinant inbred line (RIL) population, more QTL were found when using sequence data compared to a marker panel, while increasing marker density was not expected to provide additional power to detect QTL. Next to the RIL population, also in an association panel it was shown that, even with limited imputation accuracy, the power of a genome-wide association study can be improved by using whole-genome sequence data. For successful application of whole-genome sequence data in animals or plants, genotype imputation will remain important to obtain accurate sequence data for all individuals in a cost effective way. Sequence data will increase the power of QTL detection in RIL populations, association panels or outbred populations. Added value of whole-genome sequence data in genomic prediction will be limited, unless more information is known about the biological background of traits and functional annotations of DNA. Also statistical models that incorporate this information and that can efficiently handle large datasets have to be developed.

LanguageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Wageningen University
Supervisors/Advisors
  • Veerkamp, Roel, Promotor
  • van Eeuwijk, Fred, Promotor
  • Calus, Mario, Co-promotor
Award date5 Jul 2017
Place of PublicationWageningen
Publisher
Print ISBNs9789463431903
DOIs
Publication statusPublished - 2017

Fingerprint

animal breeding
plant breeding
quantitative trait loci
genome
inbred lines
genomics
prediction
genotype
value added
statistical models
dairy cattle
sequence analysis
tomatoes
mutation

Keywords

  • next generation sequencing
  • dna sequencing
  • quantitative trait loci
  • cattle
  • genomics
  • solanum lycopersicum
  • animal breeding
  • plant breeding

Cite this

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title = "Prospects of whole-genome sequence data in animal and plant breeding",
abstract = "The rapid decrease in costs of DNA sequencing implies that whole-genome sequence data will be widely available in the coming few years. Whole-genome sequence data includes all base-pairs on the genome that show variation in the sequenced population. Consequently, it is assumed that the causal mutations (e.g. quantitative trait loci; QTL) are included, which allows testing a given trait directly for association with a QTL, and might lead to discovery of new QTL or higher accuracies in genomic predictions compared to currently available marker panels. The main aim of this thesis was to investigate the benefits of using whole-genome sequence data in breeding of animals and plants compared to currently available marker panels. First the potential and benefits of using whole-genome sequence data were studied in (dairy) cattle. Accuracy of genotype imputation to whole-genome sequence data was generally high, depending on the used marker panel. In contrast to the expectations, genomic prediction showed no advantage of using whole-genome sequence data compared to a high density marker panel. Thereafter, the use of whole-genome sequence data for QTL detection in tomato (S. Lycopersicum) was studied. In a recombinant inbred line (RIL) population, more QTL were found when using sequence data compared to a marker panel, while increasing marker density was not expected to provide additional power to detect QTL. Next to the RIL population, also in an association panel it was shown that, even with limited imputation accuracy, the power of a genome-wide association study can be improved by using whole-genome sequence data. For successful application of whole-genome sequence data in animals or plants, genotype imputation will remain important to obtain accurate sequence data for all individuals in a cost effective way. Sequence data will increase the power of QTL detection in RIL populations, association panels or outbred populations. Added value of whole-genome sequence data in genomic prediction will be limited, unless more information is known about the biological background of traits and functional annotations of DNA. Also statistical models that incorporate this information and that can efficiently handle large datasets have to be developed.",
keywords = "next generation sequencing, dna sequencing, quantitative trait loci, cattle, genomics, solanum lycopersicum, animal breeding, plant breeding, next generation sequencing, dna-sequencing, loci voor kwantitatief kenmerk, rundvee, genomica, solanum lycopersicum, dierveredeling, plantenveredeling",
author = "{van Binsbergen}, Rianne",
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year = "2017",
doi = "10.18174/413524",
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Prospects of whole-genome sequence data in animal and plant breeding. / van Binsbergen, Rianne.

Wageningen : Wageningen University, 2017. 220 p.

Research output: Thesisinternal PhD, WUAcademic

TY - THES

T1 - Prospects of whole-genome sequence data in animal and plant breeding

AU - van Binsbergen, Rianne

N1 - WU thesis 6710 Includes bibliographical references. - With summaries in English and Dutch

PY - 2017

Y1 - 2017

N2 - The rapid decrease in costs of DNA sequencing implies that whole-genome sequence data will be widely available in the coming few years. Whole-genome sequence data includes all base-pairs on the genome that show variation in the sequenced population. Consequently, it is assumed that the causal mutations (e.g. quantitative trait loci; QTL) are included, which allows testing a given trait directly for association with a QTL, and might lead to discovery of new QTL or higher accuracies in genomic predictions compared to currently available marker panels. The main aim of this thesis was to investigate the benefits of using whole-genome sequence data in breeding of animals and plants compared to currently available marker panels. First the potential and benefits of using whole-genome sequence data were studied in (dairy) cattle. Accuracy of genotype imputation to whole-genome sequence data was generally high, depending on the used marker panel. In contrast to the expectations, genomic prediction showed no advantage of using whole-genome sequence data compared to a high density marker panel. Thereafter, the use of whole-genome sequence data for QTL detection in tomato (S. Lycopersicum) was studied. In a recombinant inbred line (RIL) population, more QTL were found when using sequence data compared to a marker panel, while increasing marker density was not expected to provide additional power to detect QTL. Next to the RIL population, also in an association panel it was shown that, even with limited imputation accuracy, the power of a genome-wide association study can be improved by using whole-genome sequence data. For successful application of whole-genome sequence data in animals or plants, genotype imputation will remain important to obtain accurate sequence data for all individuals in a cost effective way. Sequence data will increase the power of QTL detection in RIL populations, association panels or outbred populations. Added value of whole-genome sequence data in genomic prediction will be limited, unless more information is known about the biological background of traits and functional annotations of DNA. Also statistical models that incorporate this information and that can efficiently handle large datasets have to be developed.

AB - The rapid decrease in costs of DNA sequencing implies that whole-genome sequence data will be widely available in the coming few years. Whole-genome sequence data includes all base-pairs on the genome that show variation in the sequenced population. Consequently, it is assumed that the causal mutations (e.g. quantitative trait loci; QTL) are included, which allows testing a given trait directly for association with a QTL, and might lead to discovery of new QTL or higher accuracies in genomic predictions compared to currently available marker panels. The main aim of this thesis was to investigate the benefits of using whole-genome sequence data in breeding of animals and plants compared to currently available marker panels. First the potential and benefits of using whole-genome sequence data were studied in (dairy) cattle. Accuracy of genotype imputation to whole-genome sequence data was generally high, depending on the used marker panel. In contrast to the expectations, genomic prediction showed no advantage of using whole-genome sequence data compared to a high density marker panel. Thereafter, the use of whole-genome sequence data for QTL detection in tomato (S. Lycopersicum) was studied. In a recombinant inbred line (RIL) population, more QTL were found when using sequence data compared to a marker panel, while increasing marker density was not expected to provide additional power to detect QTL. Next to the RIL population, also in an association panel it was shown that, even with limited imputation accuracy, the power of a genome-wide association study can be improved by using whole-genome sequence data. For successful application of whole-genome sequence data in animals or plants, genotype imputation will remain important to obtain accurate sequence data for all individuals in a cost effective way. Sequence data will increase the power of QTL detection in RIL populations, association panels or outbred populations. Added value of whole-genome sequence data in genomic prediction will be limited, unless more information is known about the biological background of traits and functional annotations of DNA. Also statistical models that incorporate this information and that can efficiently handle large datasets have to be developed.

KW - next generation sequencing

KW - dna sequencing

KW - quantitative trait loci

KW - cattle

KW - genomics

KW - solanum lycopersicum

KW - animal breeding

KW - plant breeding

KW - next generation sequencing

KW - dna-sequencing

KW - loci voor kwantitatief kenmerk

KW - rundvee

KW - genomica

KW - solanum lycopersicum

KW - dierveredeling

KW - plantenveredeling

U2 - 10.18174/413524

DO - 10.18174/413524

M3 - internal PhD, WU

SN - 9789463431903

PB - Wageningen University

CY - Wageningen

ER -