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

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 article, 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. 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 genomewide 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 data sets, ensures a high proportion of working assays; (iii) allele frequency ascertainment bias is minimized 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.

• Publication year

  2018

• Venue

  Molecular Ecology Resources

• Publication date

  2018-07-01

• Fields of study

  Biology, Medicine, Environmental Science

• Identifiers
  DOI 10.1111/1755-0998.12778PMID 29573186
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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