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Data from: A high-density SNP chip for genotyping great tit (Parus major) populations and its application to studying the genetic architecture of exploration behaviour

Authors :
Kim, J.M.
Santure, Anna W.
Barton, H.J.
Quinn, John L.
Cole, Ella F.
Visser, M.E.
Sheldon, B.C.
Groenen, M.
van Oers, K.
Slate, J.
Kim, J.M.
Santure, Anna W.
Barton, H.J.
Quinn, John L.
Cole, Ella F.
Visser, M.E.
Sheldon, B.C.
Groenen, M.
van Oers, K.
Slate, J.
Publication Year :
2018

Abstract

High density SNP microarrays (‘SNP chips’) are a rapid, accurate and efficient method for genotyping several hundred thousand polymorphisms in large numbers of individuals. While SNP chips are routinely used in human genetics and in animal and plant breeding, they are less widely used in evolutionary and ecological research. In this paper we describe the development and application of a high density Affymetrix Axiom chip with around 500 000 SNPs, designed to perform genomics studies of great tit (Parus major) populations. We demonstrate that the per-SNP genotype error rate is well below 1% and that the chip can also be used to identify structural or copy number variation (CNVs). The chip is used to explore the genetic architecture of exploration behaviour (EB), a personality trait that has been widely studied in great tits and other species. No SNPs reached genome-wide significance, including at DRD4, a candidate gene. However, EB is heritable and appears to have a polygenic architecture. Researchers developing similar SNP chips may note: (i) SNPs previously typed on alternative platforms are more likely to be converted to working assays, (ii) detecting SNPs by more than one pipeline, and in independent datasets, ensures a high proportion of working assays, (iii) allele frequency ascertainment bias is minimised by performing SNP discovery in individuals from multiple populations and (iv) samples with the lowest call rates tend to also have the greatest genotyping error rates.

Details

Database :
OAIster
Notes :
text/html
Publication Type :
Electronic Resource
Accession number :
edsoai.on1200322306
Document Type :
Electronic Resource