Quantitative genetics predicts, that traits subjected to strong selection should show low heritability and may yield biased estimates of genetic correlations (rg). Similar pattern may also appear if genetic sources of variation are confounded with non-genetic sources. Thus, a positive relationship between genetic correlations and heritabilities (h2) of underlying traits might be observed. Here we test this prediction using a large dataset of published estimates of genetic correlations and employing a powerful meta-analytical approach. We considered both between-traits and cross-sex genetic correlations. We failed to find support for the prediction about a positive rg – h2 relationship: our analysis based on nearly 1000 published estimates of genetic parameters indicates that the predicted relationship is weak and statistically non-significant. Thus, low heritability does not preclude the possibility of detecting substantial genetic correlations. Our meta-analysis indicates that published estimates of genetic parameters coming from various experimental designs and obtained using different statistical techniques are not significantly biased in case of weakly-heritable traits.

Adaptive radiation is thought to be responsible for the evolution of a great portion of the past and present diversity of life. Instances of adaptive radiation, characterized by the rapid emergence of an array of species as a consequence to their adaptation to distinct ecological niches, are important study systems in evolutionary biology. However, because of the rapid lineage formation in these groups, and the occurrence of hybridization between the participating species, it is often difficult to reconstruct the phylogenetic history of species that underwent an adaptive radiation. In this study, we present a novel approach for species-tree estimation in rapidly diversifying lineages, where introgression is known to occur, and apply it to a multimarker dataset containing up to 16 specimens per species for a set of 45 species of East African cichlid fishes (522 individuals in total), with a main focus on the cichlid species flock of Lake Tanganyika. We first identified, using age distributions of most recent common ancestors in individual gene trees, those lineages in our dataset that show strong signatures of past introgression. This led us to formulate three hypotheses of introgression between different lineages of Tanganyika cichlids: the ancestor of Boulengerochromini (or of Boulengerochromini and Bathybatini) received genomic material from the derived H-lineage; the common ancestor of Cyprichromini and Perissodini experienced, in turn, introgression from Boulengerochromini and/or Bathybatini; and the Lake Tanganyika Haplochromini and closely related riverine lineages received genetic material from Cyphotilapiini. We then applied the multispecies coalescent model to estimate the species tree of Lake Tanganyika cichlids, but excluded the lineages involved in these introgression events, as the multispecies coalescent model does not incorporate introgression. This resulted in a robust species tree, in which the Lamprologini were placed as sister lineage to the H-lineage (including the Eretmodini), and we identify a series of rapid splitting events at the base of the H-lineage. Divergence ages estimated with the multispecies coalescent model were substantially younger than age estimates based on concatenation, and agree with the geological history of the Great Lakes of East Africa. Finally, we formally tested the three hypotheses of introgression using a likelihood framework, and find strong support for introgression between some of the cichlid tribes of Lake Tanganyika.

Myosin motors are the fundamental force-generating element of muscle contraction. Variation in the human β-cardiac myosin gene (MYH7) can lead to hypertrophic cardiomyopathy (HCM), a heritable disease characterized by cardiac hypertrophy, heart failure, and sudden cardiac death. How specific myosin variants alter motor function or clinical expression of disease remains incompletely understood. Here, we combine structural models of myosin from multiple stages of its chemomechanical cycle, exome sequencing data from population cohorts of 60,706 and 42,930 individuals, and genetic and phenotypic data from 2,913 HCM patients to elucidate novel structure-function relationships within β-cardiac myosin. We first developed computational models of the human β-cardiac myosin protein before and after the myosin power stroke. Then, using a spatial scan statistic modified to analyze genetic variation in protein three-dimensional space, we found significant enrichment of disease-associated variants in the converter, a kinetic domain that transduces force from the catalytic domain to the lever arm to accomplish the power stroke. Focusing our analysis on surface-exposed residues, we identified another region enriched for disease-associated variants that contains both the converter domain and residues on a single flat surface on the myosin head described as a myosin mesa. This surface is prominent in the pre-stroke model, but substantially reduced in size following the power stroke. Notably, HCM patients with variants in the enriched regions have earlier presentation and worse outcome than those with variants in other regions. In summary, this study provides a model for the combination of protein structure, large-scale genetic sequencing and detailed phenotypic data to reveal insight into time-shifted protein structures and genetic disease.

Abstract The standard models for genomic prediction assume additive polygenic marker effects. For epistatic models including marker interaction effects, the number of effects to be fitted becomes large, which require computational tools tailored specifically for such models. Here, we extend the methods implemented in the R package bigRR so that marker interaction effects can be computed. Simulation results based on marker data from Arabidopsis thaliana show that the inclusion of interaction effects between markers can give a small but significant improvement in genomic predictions. The methods were implemented in the R package EPISbigRR available in the bigRR project on R-Forge. The package includes an introductory vignette to the functions available in EPISbigRR.

Expression analysis and variant calling workflows are employed to identify genes that either exhibit a differential behaviour or have a significant functional impact of mutations. This is always followed by pathway analysis which provides greater insights and simplifies explanation of observed phenotype. The current techniques used towards this purpose have some serious limitations. In this paper, we propose a theoretical framework to overcome many limitations of current techniques. Our framework takes into account the networked nature of the data and provides facility to weigh each gene differently and in the process we do away with the need of arbitrary cut-offs. This framework is designed to be modular and provides the researchers with flexibility to plug analytical tools of their choice for every component. We also demonstrate effectiveness of our approach for personalized and cohort analysis of cancer gene expression samples with PageRank as one of the modules in the framework.

With the rapid production of high dimensional genetic data, one major challenge in genome-wide association studies is to develop effective and efficient statistical tools to resolve the low power problem of detecting causal SNPs with low to moderate susceptibility, whose effects are often obscured by substantial background noises. Here we present a novel method that serves as an optimal technique for reducing background noises and improving detection power in genome-wide association studies. The approach uses hidden Markov model and its derivate Markov hidden Markov model to estimate the posterior probabilities of a markers being in an associated state. We conducted extensive simulations based on the human whole genome genotype data from the GlaxoSmithKline-POPRES project to calibrate the sensitivity and specificity of our method and compared with many popular approaches for detecting positive signals including the χ^2 test for association and the Cochran-Armitage trend test. Our simulation results suggested that at very low false positive rates (<10^-6), our method reaches the power of 0.9, and is more powerful than any other approaches, when the allelic effect of the causal variant is non-additive or unknown. Application of our method to the data set generated by Welcome Trust Case Control Consortium using 14,000 cases and 3,000 controls confirmed its powerfulness and efficiency under the context of the large-scale genome-wide association studies.

Many breeders use selection as a first step for improvement of complex traits. The theory of selection is well studied in the genetics literature. In this article, we propose a new approach to breeding which focuses on mating instead of selection, we refer it to as genomic mating. We believe breeding by genomic mating uses the genomic information better than a recently proposed method of breeding methodologies like genomic selection. We used concepts of risk and diversification from the economics for formulating the optimum mating theory. The results from our simulations point to the efficiency of our method to selection based breeding.

Background The Mouse Genomes Project is an ongoing collaborative effort to sequence the genomes of the common laboratory mouse strains. In 2011, the initial analysis of sequence variation across 17 strains found 56.7M unique SNPs and 8.8M indels. We carried out deep sequencing of 13 additional inbred strains (BUB/BnJ, C57BL/10J, C57BR/cdJ, C58/J, DBA/1J, I/LnJ, KK/HiJ, MOLF/EiJ, NZB/B1NJ, NZW/LacJ, RF/J, SEA/GnJ and ST/bJ) cataloging molecular variation within and across the strains. These strains include important models for immune response, leukemia, age-related hearing loss and rheumatoid arthritis. We now have several examples of fully sequenced closely related strains that are divergent for several disease phenotypes. Results Approximately, 27.4M unique SNPs and 5M indels were identified across these strains compared to the C57BL/6J reference genome (GRCm38). The amount of variation found in the inbred laboratory mouse genome has increased to 71M SNPs and 12M indels. We investigate the genetic basis of highly penetrant cancer susceptibility in RF/J finding private novel missense mutations in DNA damage repair and highly cancer associated genes. We use two highly related strains (DBA/1J and DBA/2J) to investigate the genetic basis of collagen induced arthritis susceptibility. Conclusion This paper significantly expands the catalog of fully sequenced laboratory mouse strains and now contains several examples of highly genetically similar strains with divergent phenotypes. We show how studying private missense mutations can lead to insights into the genetic mechanism for a highly penetrant phenotype.

Genome duplication can provide material for evolutionary innovation, and much remains unknown about its functional effects. Assembly of large, outbred eukaryotic genomes is difficult, but structural rearrangements within such taxa can be investigated using linkage maps. RAD sequencing provides unprecedented ability to generate high-density linkage maps for non-model species. However, these methods often result in a low number of homologous markers between species due to phylogenetic distance or technical differences in library preparation. Family Salmonidae is ideal for studying the effects of whole genome duplication. The ancestral salmonid underwent whole genome duplication around 65 million years ago and the tetraploid genome has undergone rediploidization during the salmonid diversification. In the salmonids, synteny occurs between orthologous chromosomes, but each species exhibits conserved and unique chromosome arm fusions and fissions. In this study, we identify orthologous chromosome arms within the salmonids using available RADseq salmonid linkage maps along with a new high-density linkage map (3826 markers) constructed for the Salvelinus genera (Brook Charr S. fontinalis). We developed MapComp, a program that identifies identical and proximal markers between linkage maps using a reference genome of a related species as an intermediate (e.g. Rainbow Trout Oncorhynchus mykiss). We greatly increased the number of comparable markers between linkage maps relative to that obtained using only identical markers. This enabled a characterization of the most likely history of retained chromosomal rearrangements post-whole genome duplication in five species of Oncorhynchus, and one species of each of Salvelinus, Salmo, and Coregonus, representing all of the main salmonid genera. Additionally, integration with the genetic map of the pre-duplicated sister species Northern Pike Esox lucius permitted the identification of homeologous chromosomes in all species. Putative conserved inversions within chromosome arms were also identified among species. Analyses of RADseq-based linkage maps from other taxa are likely to benefit from MapComp, available at: https://github.com/enormandeau/mapcomp/.