How much extra information is gained using imputed genotype data?

Title
How much extra information is gained using imputed genotype data?
Publication Date
2018
Author(s)
Clark, S
( author )
OrcID: https://orcid.org/0000-0001-8605-1738
Email: sclark37@une.edu.au
UNE Id une-id:sclark37
Duijvesteijn, N
van der Werf, J H J
( author )
OrcID: https://orcid.org/0000-0003-2512-1696
Email: jvanderw@une.edu.au
UNE Id une-id:jvanderw
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Massey University
Place of publication
Palmerston North, New Zealand
UNE publication id
une:1959.11/28984
Abstract
Genotype imputation has been discussed widely as a tool to increase the statistical power associated with genome wide association studies (GWAS) and genomic prediction. Previous studies have examined the performance of imputation by evaluating how well a validation set has been predicted. The aim of this study was to examine the amount of extra information added by utilising genotype imputation. The number of new haplotype combinations, between adjacent loci, were estimated for multiple genotype densities from an Australian Sheep dataset. In our example, using genotypes from OAR6, imputation increased the number of haplotypes for 81% of the regions when imputing from 12k to 50k. Large distances between adjacent low density markers resulted in higher numbers of new haplotypes. This also corresponded to a greater proportion of low frequency haplotypes. When imputing from HD to WGS no information was added for 50% of the regions and there was a greater proportion of haplotypes with only 1 observation. Estimating the number of new haplotypes from imputation provides an understanding about the value of imputation and can be utilised to help design of reference genotype datasets.
Link
Citation
Proceedings of the World Congress on Genetics Applied to Livestock Production, v.11, p. 1-5
Start page
1
End page
5
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International

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