Genetics of intra-species variation in avoidance behavior induced by a thermal stimulus in C. elegans

Genetics of intra-species variation in avoidance behavior induced by a thermal stimulus in C. elegans

Individuals within a species vary in their responses to a wide range of stimuli, partly as a result of differences in their genetic makeup. Relatively little is known about the genetic and neuronal mechanisms contributing to diversity of behavior in natural populations. By studying animal-to-animal variation in innate avoidance behavior to thermal stimuli in the nematode Caenorhabditis elegans, we uncovered genetic principles of how different components of a behavioral response can be altered in nature to generate behavioral diversity. Using a thermal pulse assay, we uncovered heritable variation in responses to a transient temperature increase. Quantitative trait locus mapping revealed that separate components of this response were controlled by distinct genomic loci. The loci we identified contributed to variation in components of thermal pulse avoidance behavior in an additive fashion. Our results show that the escape behavior induced by thermal stimuli is composed of simpler behavioral components that are influenced by at least six distinct genetic loci. The loci that decouple components of the escape behavior reveal a genetic system that allows independent modification of behavioral parameters. Our work sets the foundation for future studies of evolution of innate behaviors at the molecular and neuronal level.

Partitioning heritability by functional category using GWAS summary statistics

Partitioning heritability by functional category using GWAS summary statistics
Hilary Kiyo Finucane, Brendan Bulik-Sullivan, Alexander Gusev, Gosia Trynka, Yakir Reshef, Po-Ru Loh, Verneri Anttilla, Han Xu, Chongzhi Zang, Kyle Farh, Stephan Ripke, Felix Day, ReproGen Consortium, Schizophrenia Working Group of the Psychiatric Genetics Consortium, RACI Consortium, Shaun Purcell, Eli Stahl, Sara Lindstrom, John R.B. Perry, Yukinori Okada, Soumya Raychaudhuri, Mark Daly, Nick Patterson, Benjamin M. Neale, Alkes L. Price

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits spanning a total of 1.3 million phenotype measurements. To enable this analysis, we introduce a new method for partitioning heritability from GWAS summary statistics while controlling for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior. These results demonstrate that GWAS can aid in understanding the biological basis of disease and provide direction for functional follow-up.

Ancestry specific association mapping in admixed populations

Ancestry specific association mapping in admixed populations

Line Skotte, Thorfinn Sand S Korneliussen, Ida Moltke, Anders Albrechtsen

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

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

Frank Technow, Carlos D. Messina, L. Radu Totir, Mark Cooper

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

Mutation detection in candidate genes for parauberculosis resistance in sheep

Bianca Moioli, Luigi De Grossi, Roberto Steri, Silvia D’Andrea, Fabio Pilla

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

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

Yan Huang, Deniz Erezyilmaz

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.

MultiMeta: an R package for meta-analysing multi-phenotype genome-wide association studies

MultiMeta: an R package for meta-analysing multi-phenotype genome-wide association studies
Dragana Vuckovic, Paolo Gasparini, Nicole Soranzo, Valentina Iotchkova

Summary: As new methods for multivariate analysis of Genome Wide Association Studies (GWAS) become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance based method for meta-analysis, generalized to an n-dimensional setting. Availability: The R package MultiMeta can be downloaded from CRAN Contact: