The genetic basis of rapidly evolving male genital morphology in Drosophila.

The external genitalia are some of the most rapidly evolving morphological structures in insects. The posterior lobe of the male genital arch shows striking differences in both size and shape among closely related species of the Drosophila melanogaster species subgroup. Here, we dissect the genetic basis of posterior lobe morphology between D. mauritiana and D. sechellia, two island endemic species that last shared a common ancestor ∼300,000 years ago. We test a large collection of genome-wide homozygous D. mauritiana genetic introgressions, which collectively cover ∼50% of the genome, for their morphological effects when placed in a D. sechellia genetic background. We find several introgressions that have large effects on posterior lobe morphology and that posterior lobe size and posterior lobe shape can be separated genetically for some of the loci that specify morphology. Using next generation sequencing technology, we perform whole transcriptome gene expression analyses of the larval genital imaginal disc of D. mauritiana, D. sechellia, and two D. mauritiana-D. sechellia hybrid introgression genotypes that each have large effects on either posterior lobe size or posterior lobe shape. Many of the genes we identify as differentially expressed are expressed at levels similar to D. mauritiana in one introgression hybrid, but are expressed at levels similar to D. sechellia in the other introgression hybrid. However, we also find that both introgression hybrids express some of the same genes at levels similar to D. mauritiana, and notably, that both introgression hybrids possess genes in the insulin receptor signaling pathway, which are expressed at D. mauritiana expression levels. These results suggest the possibility that the insulin signaling pathway might integrate size and shape genetic inputs to establish differences in overall posterior lobe morphology between D. mauritiana and D. sechellia.

A two-parameter generalized Poisson model to improve the analysis of RNA-seq data.

Deep sequencing of RNAs (RNA-seq) has been a useful tool to characterize and quantify transcriptomes. However, there are significant challenges in the analysis of RNA-seq data, such as how to separate signals from sequencing bias and how to perform reasonable normalization. Here, we focus on a fundamental question in RNA-seq analysis: the distribution of the position-level read counts. Specifically, we propose a two-parameter generalized Poisson (GP) model to the position-level read counts. We show that the GP model fits the data much better than the traditional Poisson model. Based on the GP model, we can better estimate gene or exon expression, perform a more reasonable normalization across different samples, and improve the identification of differentially expressed genes and the identification of differentially spliced exons. The usefulness of the GP model is demonstrated by applications to multiple RNA-seq data sets.

Comparison between the stochastic search variable selection and the least absolute shrinkage and selection operator for genome-wide association studies of rheumatoid arthritis.

BACKGROUND: Because multiple loci control complex diseases, there is great interest in testing markers simultaneously instead of one by one. In this paper, we applied two model selection algorithms: the stochastic search variable selection (SSVS) and the least absolute shrinkage and selection operator (LASSO) to two quantitative phenotypes related to rheumatoid arthritis (RA). RESULTS: The Genetic Analysis Workshop 16 data includes 2,062 unrelated individuals and 545,080 single-nucleotide polymorphism markers from the Illumina 550 k chip. We performed our analyses on the cases as the quantitative phenotype data was not provided for the controls. The performance of the two algorithms was compared. Using sure independence screening as the prescreening procedure, both SSVS and LASSO give small models. No markers are identified in the human leukocyte antigen region of chromosome 6 that was shown to be associated with RA. SSVS and LASSO identify seven common loci, and some of them are on genes LRRC8D, LRP1B, and COLEC12. These genes have not been reported to be associated with RA. LASSO also identified a common locus on gene KTCD21 for the two phenotypes (marker rs230662 and rs483731, respectively). CONCLUSION: SSVS outperforms LASSO in simulation studies. Both SSVS and LASSO give small models on the RA data, however this depends on model parameters. We also demonstrate the ability of both LASSO and SSVS to handle more markers than the number of samples.