Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs

Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. S...

Full description

Bibliographic Details
Main Authors: M. Schmid, J. Bennewitz
Format: Article
Language:English
Published: Copernicus Publications 2017-09-01
Series:Archives Animal Breeding
Online Access:https://www.arch-anim-breed.net/60/335/2017/aab-60-335-2017.pdf
id doaj-db53496c87034a1bb65b463ca5ca6ed3
record_format Article
spelling doaj-db53496c87034a1bb65b463ca5ca6ed32020-11-25T00:11:59ZengCopernicus PublicationsArchives Animal Breeding0003-94382363-98222017-09-016033534610.5194/aab-60-335-2017Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designsM. Schmid0J. Bennewitz1University Hohenheim, Institute of Animal Science, Garbenstrasse 17, 70599 Stuttgart, GermanyUniversity Hohenheim, Institute of Animal Science, Garbenstrasse 17, 70599 Stuttgart, GermanyQuantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.https://www.arch-anim-breed.net/60/335/2017/aab-60-335-2017.pdf
collection DOAJ
language English
format Article
sources DOAJ
author M. Schmid
J. Bennewitz
spellingShingle M. Schmid
J. Bennewitz
Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs
Archives Animal Breeding
author_facet M. Schmid
J. Bennewitz
author_sort M. Schmid
title Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs
title_short Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs
title_full Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs
title_fullStr Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs
title_full_unstemmed Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs
title_sort invited review: genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs
publisher Copernicus Publications
series Archives Animal Breeding
issn 0003-9438
2363-9822
publishDate 2017-09-01
description Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.
url https://www.arch-anim-breed.net/60/335/2017/aab-60-335-2017.pdf
work_keys_str_mv AT mschmid invitedreviewgenomewideassociationanalysisforquantitativetraitsinlivestockaselectivereviewofstatisticalmodelsandexperimentaldesigns
AT jbennewitz invitedreviewgenomewideassociationanalysisforquantitativetraitsinlivestockaselectivereviewofstatisticalmodelsandexperimentaldesigns
_version_ 1725401939501383680