Ancestry specific association mapping in admixed populations

Line Skotte, Thorfinn Sand S Korneliussen, Ida Moltke, Anders Albrechtsen
doi: http://dx.doi.org/10.1101/014001

As recently demonstrated in several genetic association studies, historically small and isolated populations can offer increased statistical power due to extended link- age equilibrium and increased genetic drift over many generations. However, many such populations, like the Greenlandic Inuit population, have recently experienced substantial admixture with other populations, which can complicate the association studies. One important complication is that most current methods for performing association testing are based on the assumption that the effect of the tested ge- netic marker is the same regardless of ancestry. This is a reasonable assumption for a causal variant, but may not hold for the genetic markers that are tested in association studies, which are usually not causal. The effects of non-causal genetic markers depend on how strongly their presence correlate with the presence of the causal marker, and this may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies. Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect sizes are allowed to depend on the ancestry of the allele.Our method does not rely on accurate inference of local ancestry, yet using simulations we show that in some scenarios it gives a dramatic increase in statistical power to detect associations. In addition, the method allows for testing for difference in effect size between ancestral populations, which can be used to determine if a SNP is causal. We demonstrate the usefulness of the method on data from the Greenlandic population.

Integrating crop growth models with whole genome prediction through approximate Bayesian computation

Frank Technow, Carlos D. Messina, L. Radu Totir, Mark Cooper
doi: http://dx.doi.org/10.1101/014100

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (GxE), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of GxE and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with a simulated data set. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising novel approach to improving prediction accuracy for some of the most challenging scenarios of interest to applied geneticists.

Mutation detection in candidate genes for parauberculosis resistance in sheep

Bianca Moioli, Luigi De Grossi, Roberto Steri, Silvia D’Andrea, Fabio Pilla
doi: http://dx.doi.org/10.1101/014035

The marker-assisted selection exploits anonymous genetic markers that have been associated with measurable differences on complex traits; because it is based on the Linkage Disequilibrium between the polymorphic markers and the polymorphisms which code for the trait, its success is limited to the population in which the association has been assessed. The identification of the gene with effect on the target and the detection of the functional mutations will allow selection in independent populations, while encouraging studies on gene expression. The results of a genome-wide scan performed with the Illumina Ovine SNP50K Beadchip, on 100 sheep, 50 of which positive at paratuberculosis serological assessment, identified two candidate genes of immunity response, the PCP4 and the CD109, located in proximity of the markers with different allele frequency in positive and negative sheep. The coding region of the two genes was directly sequenced: three missense mutations were detected: two in the PCP4 gene and one in the second exon of the CD109 gene. The PCP4 mutations had a very low frequency (.12 and .07) so making hazardous to hypothesize their direct effect on immune response. On the contrary, the mutation detected in the CD109 gene showed a strong linkage disequilibrium with the anonymous marker. Direct sequencing of the DNA of sheep of different populations showed that disequilibrium was maintained. Allele frequency at the hypothesized marker associated to immune response, calculated for other breeds of sheep, showed that the marker allele potentially associated to disease resistance is more frequent in the local breeds and in breeds that have not been submitted to selection programs.

The genetics of resistance to Morinda fruit toxin during the postembryonic stages in Drosophila sechellia

Yan Huang, Deniz Erezyilmaz
doi: http://dx.doi.org/10.1101/014027

Many phytophagous insect species are ecologic specialists that have adapted to utilize a single host plant. Drosophila sechellia is a specialist that utilizes the ripe fruit of Morinda citrifolia, which is toxic to its sibling species, D. simulans. Here we apply multiplexed shotgun genotyping and QTL analysis to examine the genetic basis of resistance to M. citrifolia fruit toxin in interspecific hybrids. We find that at least four dominant and four recessive loci interact additively to confer resistance to the M. citrifolia fruit toxin. These QTL include a dominant locus of large effect on the third chromosome (QTL-IIIsima) that was not detected in previous analyses. The small-effect loci that we identify overlap with regions that were identified in selection experiments with D. simulans on octanoic acid and in QTL analyses of adult resistance to octanoic acid. Our high-resolution analysis sheds new light upon the complexity of M. citrifolia resistance, and suggests that partial resistance to lower levels of M. citrifolia toxin could be passed through introgression from D. sechellia to D. simulans in nature. The identification of a locus of major effect, QTL-IIIsima, is an important step towards identifying the molecular basis of host plant specialization by D. sechellia.