DOI: 10.5061/dryad.gn44q
title: Data from: Deciphering the genomic architecture of the stickleback brain with a novel multi-locus gene-mapping approach

description: Quantitative traits important to organismal function and fitness, such as
 brain size, are presumably controlled by many small-effect loci.
 Deciphering the genetic architecture of such traits with traditional
 quantitative trait locus (QTL) mapping methods is challenging. Here, we
 investigated the genetic architecture of brain size (and the size of five
 different brain parts) in nine-spined sticklebacks (Pungitius pungitius)
 with the aid of novel multi-locus QTL mapping approaches based on a
 de-biased LASSO method. Apart from having more statistical power to detect
 QTL and reduced rate of false positives than conventional QTL mapping
 approaches, the developed methods can handle large marker panels and
 provide estimates of genomic heritability. Single-locus analyses of an
 F2-interpopulation cross with 239 individuals and 15 198 fully informative
 single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with
 variation in stickleback brain size traits. Many of these loci were in
 strong linkage disequilibrium (LD) with each other, and consequently, a
 multi-locus mapping of individual SNPs, accounting for LD structure in the
 data, recovered only four significant QTL. However, a multi-locus mapping
 of SNPs grouped by linkage group (LG) identified 14 LGs (1-6 depending on
 the trait) that influence variation in brain traits. For instance, 17.6%
 of the variation in relative brain size was explainable by cumulative
 effects of SNPs distributed over six LGs, whereas 42% of the variation was
 accounted for by all 21 LGs. Hence, the results suggest that variation in
 stickleback brain traits is influenced by many small-effect loci. Apart
 from suggesting moderately heritable (h2 ≈ 0.15-0.42) multifactorial
 genetic architecture of brain traits, the results highlight the challenges
 in identifying the loci contributing to variation in quantitative traits.
 Nevertheless, the results demonstrate that the novel QTL mapping approach
 developed here has distinctive advantages over the traditional QTL mapping
 methods in analyses of dense marker panels.
description: Data for QTL mapping on
 brain size in sticklebacksThe data set is used in
 a quantitative trait locus mapping study on six brain volume traits
 including bulbus olfactorious, telecephalon, optic tectum, hypothalamus,
 cerebellum and total brain size of a F2 nine stickleback population. The
 data consists of 239 individuals, and 15198 non-identical SNPs. A linkage
 map has been constructed, and divide the SNPs into 21 linkage groups. The
 data are distributed as following: File1: brain_phenotype.txt -The
 phenotype data of six brain traits, which have been corrected by the sex
 and bodysize effects. File2: genotype.txt -The SNP data, each row
 represents the individuals which is one-to-one match to the phenotype data
 file, and each column represents the SNPs which is one-to-one match to the
 linkage map file. The missing genotype data have been imputed, and coded
 as 1,0,-1 for the genotypes AA, AB and BB, respectively. File3:
 linkage_map.csv -The linkage map information, column1 is the marker ID,
 column2 is the linkage group info, and column3 is the linkage position of
 each SNPQTL_brain.zip