Genotype calling of triploid offspring from diploid parents.

BACKGROUND: Polyploidy is widespread in animals and especially in plants. Different kinds of ploidies exist, for example, hexaploidy in wheat, octaploidy in strawberries, and diploidy, triploidy, tetraploidy, and pseudo-tetraploidy (partly tetraploid) in fish. Triploid offspring from diploid parents occur frequently in the wild in Atlantic salmon (Salmo salar) and, as with triploidy in general, the triploid individuals are sterile. Induced triploidy in Atlantic salmon is common practice to produce sterile fish. In Norwegian aquaculture, production of sterile triploid fish is an attempt by government and industry to limit genetic introgression between wild and farmed fish. However, triploid fish may have traits and properties that differ from those of diploids. Investigating the genetics behind traits in triploids has proved challenging because genotype calling of genetic markers in triploids is not supported by standard software. Our aim was to develop a method that can be used for genotype calling of genetic markers in triploid individuals. RESULTS: Allele signals were produced for 381 triploid Atlantic salmon offspring using a 56 K Thermo Fisher GeneTitan genotyping platform. Genotypes were successfully called by applying finite normal mixture models to the (transformed) allele signals. Subsets of markers were filtered by quality control statistics for use with downstream analyses. The quality of the called genotypes was sufficient to allow for assignment of diploid parents to the triploid offspring and to discriminate between maternal and paternal parents from autosomal inheritance patterns. In addition, as the maternal inheritance in triploid offspring is identical to gynogenetic inheritance, the maternal recombination pattern for each chromosome could be mapped by using a similar approach as that used in gene-centromere mapping. CONCLUSIONS: We show that calling of dense marker genotypes for triploid individuals is feasible. The resulting genotypes can be used in parentage assignment of triploid offspring to diploid parents, to discriminate between maternal and paternal parents using autosomal inheritance patterns, and to map the maternal recombination pattern using an approach similar to gene-centromere mapping. Genotyping of triploid individuals is important both for selective breeding programs and unravelling the underlying genetics of phenotypes recorded in triploids. In principle, the developed method can be used for genotype calling of other polyploid organisms.

Association between SNPs within candidate genes and compounds related to boar taint and reproduction.

BACKGROUND: Boar taint is an unpleasant odour and flavour of the meat from some uncastrated male pigs primarily caused by elevated levels of androstenone and skatole in adipose tissue. Androstenone is produced in the same biochemical pathway as testosterone and estrogens, which represents a particular challenge when selecting against high levels of androstenone in the breeding programme, without simultaneously decreasing levels of other steroids. Detection of single nucleotide polymorphisms (SNPs) associated with compounds affecting boar taint is important both for gaining a better understanding of the complex regulation of the trait and for the purpose of identifying markers that can be used to improve the gain of breeding. The beneficial SNPs to be used in breeding would have the combinational effects of reducing levels of boar taint without affecting fertility of the animals. The aim of this study was to detect SNPs in boar taint candidate genes and to perform association studies for both single SNPs and haplotypes with levels of boar taint compounds and phenotypes related to reproduction. RESULTS: An association study involving 275 SNPs in 121 genes and compounds related to boar taint and reproduction were carried out in Duroc and Norwegian Landrace boars. Phenotypes investigated were levels of androstenone, skatole and indole in adipose tissue, levels of androstenone, testosterone, estrone sulphate and 17beta-estradiol in plasma, and length of bulbo urethralis gland. The SNPs were genotyped in more than 2800 individuals and several SNPs were found to be significantly (LRT > 5.4) associated with the different phenotypes. Genes with significant SNPs in either of the traits investigated include cytochrome P450 members CYP2E1, CYP21, CYP2D6 and CYP2C49, steroid 5alpha-reductase SRD5A2, nuclear receptor NGFIB, catenin CTNND1, BRCA1 associated protein BAP1 and hyaluronoglucosaminidase HYAL2. Haplotype analysis provided additional evidence for an effect of CYP2E1 on levels of skatole and indole, and for BAP1, HYAL2 and SRD5A2 on levels of androstenone. CONCLUSION: The findings in this study indicate that polymorphisms in CYP2E1, CYP21, CYP2D6, CYP2C49, NGFIB and CTNND1 might be used to reduce levels of boar taint without affecting levels of testosterone, estrone sulphate, 17beta-estradiol or length of bulbo urethralis gland.

Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes.

The ordinary-, penalized-, and bootstrap t-test, least squares and best linear unbiased prediction were compared for their false discovery rates (FDR), i.e. the fraction of falsely discovered genes, which was empirically estimated in a duplicate of the data set. The bootstrap-t-test yielded up to 80% lower FDRs than the alternative statistics, and its FDR was always as good as or better than any of the alternatives. Generally, the predicted FDR from the bootstrapped P-values agreed well with their empirical estimates, except when the number of mRNA samples is smaller than 16. In a cancer data set, the bootstrap-t-test discovered 200 differentially regulated genes at a FDR of 2.6%, and in a knock-out gene expression experiment 10 genes were discovered at a FDR of 3.2%. It is argued that, in the case of microarray data, control of the FDR takes sufficient account of the multiple testing, whilst being less stringent than Bonferoni-type multiple testing corrections. Extensions of the bootstrap simulations to more complicated test-statistics are discussed.