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Linking variation in quantitative traits to variation in the genome is an important, but challenging task in the study of life-history evolution. Linkage maps provide a valuable tool for the unravelling of such trait−gene associations. Moreover, they give insight into recombination landscapes and between-species karyotype evolution. Here we used genotype data, generated from a 10k single-nucleotide polymorphism (SNP) chip, of over 2000 individuals to produce high-density linkage maps of the great tit (Parus major), a passerine bird that serves as a model species for ecological and evolutionary questions. We created independent maps from two distinct populations: a captive F2-cross from The Netherlands (NL) and a wild population from the United Kingdom (UK). The two maps contained 6554 SNPs in 32 linkage groups, spanning 2010 cM and 1917 cM for the NL and UK populations, respectively, and were similar in size and marker order. Subtle levels of heterochiasmy within and between chromosomes were remarkably consistent between the populations, suggesting that the local departures from sex-equal recombination rates have evolved. This key and surprising result would have been impossible to detect if only one population was mapped. A comparison with zebra finch Taeniopygia guttata, chicken Gallus gallus and the green anole lizard Anolis carolinensis genomes provided further insight into the evolution of avian karyotypes. [ABSTRACT FROM AUTHOR]

Individuals of all vertebrate species differ consistently in their reactions to mildly stressful challenges. These typical reactions, described as personalities or coping strategies, have a clear genetic basis, but the structure of their inheritance in natural populations is almost unknown. We carried out a quantitative genetic analysis of two personality traits (exploration and boldness) and the combination of these two traits (early exploratory behaviour). This study was carried out on the lines resulting from a two-directional artificial selection experiment on early exploratory behaviour (EEB) of great tits (Parus major) originating from a wild population. In analyses using the original lines, reciprocal F1 and reciprocal first backcross generations, additive, dominance, maternal effects ands sex-dependent expression of exploration, boldness and EEB were estimated. Both additive and dominant genetic effects were important determinants of phenotypic variation in exploratory behaviour and boldness. However, no sex-dependent expression was observed in either of these personality traits. These results are discussed with respect to the maintenance of genetic variation in personality traits, and the expected genetic structure of other behavioural and life history traits in general.Heredity (2004) 93, 496-503. doi:10.1038/sj.hdy.6800530 Published online 28 July 2004 [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]