Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato

Training set construction is an important prerequisite to Genomic Prediction (GP), and while this has been studied in diploids, polyploids have not received the same attention. Polyploidy is a common feature in many crop plants, like for example banana and blueberry, but also potato which is the thi...

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Autores principales:	Stefan Wilson, Marcos Malosetti, Chris Maliepaard, Han A. Mulder, Richard G. F. Visser, Fred van Eeuwijk
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	training set construction potato sampling technique(s) genomic prediction (GP) auto-tetraploid Plant culture SB1-1110
Acceso en línea:	https://doaj.org/article/6ed54ce3c91b4c1cbec52c3206b058aa
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spelling	oai:doaj.org-article:6ed54ce3c91b4c1cbec52c3206b058aa2021-11-30T17:31:04ZTraining Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato1664-462X10.3389/fpls.2021.771075https://doaj.org/article/6ed54ce3c91b4c1cbec52c3206b058aa2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpls.2021.771075/fullhttps://doaj.org/toc/1664-462XTraining set construction is an important prerequisite to Genomic Prediction (GP), and while this has been studied in diploids, polyploids have not received the same attention. Polyploidy is a common feature in many crop plants, like for example banana and blueberry, but also potato which is the third most important crop in the world in terms of food consumption, after rice and wheat. The aim of this study was to investigate the impact of different training set construction methods using a publicly available diversity panel of tetraploid potatoes. Four methods of training set construction were compared: simple random sampling, stratified random sampling, genetic distance sampling and sampling based on the coefficient of determination (CDmean). For stratified random sampling, population structure analyses were carried out in order to define sub-populations, but since sub-populations accounted for only 16.6% of genetic variation, there were negligible differences between stratified and simple random sampling. For genetic distance sampling, four genetic distance measures were compared and though they performed similarly, Euclidean distance was the most consistent. In the majority of cases the CDmean method was the best sampling method, and compared to simple random sampling gave improvements of 4–14% in cross-validation scenarios, and 2–8% in scenarios with an independent test set, while genetic distance sampling gave improvements of 5.5–10.5% and 0.4–4.5%. No interaction was found between sampling method and the statistical model for the traits analyzed.Stefan WilsonMarcos MalosettiChris MaliepaardHan A. MulderRichard G. F. VisserFred van EeuwijkFrontiers Media S.A.articletraining set constructionpotatosampling technique(s)genomic prediction (GP)auto-tetraploidPlant cultureSB1-1110ENFrontiers in Plant Science, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	training set construction potato sampling technique(s) genomic prediction (GP) auto-tetraploid Plant culture SB1-1110
spellingShingle	training set construction potato sampling technique(s) genomic prediction (GP) auto-tetraploid Plant culture SB1-1110 Stefan Wilson Marcos Malosetti Chris Maliepaard Han A. Mulder Richard G. F. Visser Fred van Eeuwijk Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato
description	Training set construction is an important prerequisite to Genomic Prediction (GP), and while this has been studied in diploids, polyploids have not received the same attention. Polyploidy is a common feature in many crop plants, like for example banana and blueberry, but also potato which is the third most important crop in the world in terms of food consumption, after rice and wheat. The aim of this study was to investigate the impact of different training set construction methods using a publicly available diversity panel of tetraploid potatoes. Four methods of training set construction were compared: simple random sampling, stratified random sampling, genetic distance sampling and sampling based on the coefficient of determination (CDmean). For stratified random sampling, population structure analyses were carried out in order to define sub-populations, but since sub-populations accounted for only 16.6% of genetic variation, there were negligible differences between stratified and simple random sampling. For genetic distance sampling, four genetic distance measures were compared and though they performed similarly, Euclidean distance was the most consistent. In the majority of cases the CDmean method was the best sampling method, and compared to simple random sampling gave improvements of 4–14% in cross-validation scenarios, and 2–8% in scenarios with an independent test set, while genetic distance sampling gave improvements of 5.5–10.5% and 0.4–4.5%. No interaction was found between sampling method and the statistical model for the traits analyzed.
format	article
author	Stefan Wilson Marcos Malosetti Chris Maliepaard Han A. Mulder Richard G. F. Visser Fred van Eeuwijk
author_facet	Stefan Wilson Marcos Malosetti Chris Maliepaard Han A. Mulder Richard G. F. Visser Fred van Eeuwijk
author_sort	Stefan Wilson
title	Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato
title_short	Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato
title_full	Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato
title_fullStr	Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato
title_full_unstemmed	Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato
title_sort	training set construction for genomic prediction in auto-tetraploids: an example in potato
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/6ed54ce3c91b4c1cbec52c3206b058aa
work_keys_str_mv	AT stefanwilson trainingsetconstructionforgenomicpredictioninautotetraploidsanexampleinpotato AT marcosmalosetti trainingsetconstructionforgenomicpredictioninautotetraploidsanexampleinpotato AT chrismaliepaard trainingsetconstructionforgenomicpredictioninautotetraploidsanexampleinpotato AT hanamulder trainingsetconstructionforgenomicpredictioninautotetraploidsanexampleinpotato AT richardgfvisser trainingsetconstructionforgenomicpredictioninautotetraploidsanexampleinpotato AT fredvaneeuwijk trainingsetconstructionforgenomicpredictioninautotetraploidsanexampleinpotato
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Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato

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