Genome-wide association study and scan for signatures of selection point to candidate genes for body temperature maintenance under the cold stress in Siberian cattle populations

Design of new highly productive livestock breeds, well-adapted to local climatic conditions is one of the aims of modern agriculture and breeding. The genetics underlying economically important traits in cattle are widely studied, whereas our knowledge of the genetic mechanisms of adaptation to local environments is still scarce. To address this issue for cold climates we used an integrated approach for detecting genomic intervals related to body temperature maintenance under acute cold stress. Our approach combined genome-wide association studies (GWAS) and scans for signatures of selection applied to a cattle population (Hereford and Kazakh Whiteheaded beef breeds) bred in Siberia. We utilized the GGP HD150K DNA chip containing 139,376 single nucleotide polymorphism markers

Weighted functional linear regression models for gene-based association analysis

<div><p>Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with <i>P</i> < 0.1 in at least one analysis had lower <i>P</i> values with weighted models. Moreover, we found an association between diastolic blood pressure and the <i>VMP1</i> gene (<i>P</i> = 8.18×10⁻⁶), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had <i>P</i> = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at <a href="https://cran.r-project.org/web/packages/FREGAT/index.html" target="_blank">https://cran.r-project.org/web/packages/FREGAT/index.html</a>.</p></div

Chromosome Synapsis and Recombination in Male Hybrids between Two Chromosome Races of the Common Shrew (Sorex araneus L., Soricidae, Eulipotyphla)

Hybrid zones between chromosome races of the common shrew (Sorex araneus) provide exceptional models to study the potential role of chromosome rearrangements in the initial steps of speciation. The Novosibirsk and Tomsk races differ by a series of Robertsonian fusions with monobrachial homology. They form a narrow hybrid zone and generate hybrids with both simple (chain of three chromosomes) and complex (chain of eight or nine) synaptic configurations. Using immunolocalisation of the meiotic proteins, we examined chromosome pairing and recombination in males from the hybrid zone. Homozygotes and simple heterozygotes for Robertsonian fusions showed a low frequency of synaptic aberrations (&lt;10%). The carriers of complex synaptic configurations showed multiple pairing abnormalities, which might lead to reduced fertility. The recombination frequency in the proximal regions of most chromosomes of all karyotypes was much lower than in the other regions. The strong suppression of recombination in the pericentromeric regions and co-segregation of race specific chromosomes involved in the long chains would be expected to lead to linkage disequilibrium between genes located there. Genic differentiation, together with the high frequency of pairing aberrations in male carriers of the long chains, might contribute to maintenance of the narrow hybrid zone

FFBSKAT: fast family-based sequence kernel association test.

The kernel machine-based regression is an efficient approach to region-based association analysis aimed at identification of rare genetic variants. However, this method is computationally complex. The running time of kernel-based association analysis becomes especially long for samples with genetic (sub) structures, thus increasing the need to develop new and effective methods, algorithms, and software packages. We have developed a new R-package called fast family-based sequence kernel association test (FFBSKAT) for analysis of quantitative traits in samples of related individuals. This software implements a score-based variance component test to assess the association of a given set of single nucleotide polymorphisms with a continuous phenotype. We compared the performance of our software with that of two existing software for family-based sequence kernel association testing, namely, ASKAT and famSKAT, using the Genetic Analysis Workshop 17 family sample. Results demonstrate that FFBSKAT is several times faster than other available programs. In addition, the calculations of the three-compared software were similarly accurate. With respect to the available analysis modes, we combined the advantages of both ASKAT and famSKAT and added new options to empower FFBSKAT users. The FFBSKAT package is fast, user-friendly, and provides an easy-to-use method to perform whole-exome kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The FFBSKAT package, along with its manual, is available for free download at http://mga.bionet.nsc.ru/soft/FFBSKAT/

174 genotyped individuals from Siberian Herefords and Kazakh Whiteheaded breeds

174 cattle individuals from Siberian Hereford (150) and Kazakh Whiteheaded (24) breeds. These samples were genotyped on the GGP HD150K bovine SNP array, filtered for unmapped SNPs, SNPs on sex chromosomes, MAF<0.05, low genotyping success and HW equilibrium. This resulted in 107 550 SNPs present in this file

Data from: Genome-wide association study for body weight in cattle populations from Siberia

Body weight is a complex trait in cattle associated with commonly used commercial breeding measurements related to growth. Although many quantitative trait loci (QTL) for body weight have been identified in cattle so far, searching for genetic determinants in different breeds or environments is promising. Therefore, we carried out a genome‐wide association study (GWAS) in two cattle populations from the Russian Federation (Siberian region) using the GGP HD150K array containing 139 376 single nucleotide polymorphism (SNP) markers. Association tests for 107 550 SNPs left after filtering revealed five statistically significant SNPs on BTA5, considering a false discovery rate of less than 0.05. The chromosomal region containing these five SNPs contains the CCND2 gene, which was previously associated with average daily weight gain and body mass index in US beef cattle populations and in humans respectively. Our study is the first GWAS for body weight in beef cattle populations from the Russian Federation. The results provided here suggest that, despite the existence of breed‐ and species‐specific QTL, the genetic architecture of body weight could be evolutionarily conserved in mammals

Data from: Genome-wide association study for body weight in cattle populations from Siberia

Body weight is a complex trait in cattle associated with commonly used commercial breeding measurements related to growth. Although many quantitative trait loci (QTL) for body weight have been identified in cattle so far, searching for genetic determinants in different breeds or environments is promising. Therefore, we carried out a genome‐wide association study (GWAS) in two cattle populations from the Russian Federation (Siberian region) using the GGP HD150K array containing 139 376 single nucleotide polymorphism (SNP) markers. Association tests for 107 550 SNPs left after filtering revealed five statistically significant SNPs on BTA5, considering a false discovery rate of less than 0.05. The chromosomal region containing these five SNPs contains the CCND2 gene, which was previously associated with average daily weight gain and body mass index in US beef cattle populations and in humans respectively. Our study is the first GWAS for body weight in beef cattle populations from the Russian Federation. The results provided here suggest that, despite the existence of breed‐ and species‐specific QTL, the genetic architecture of body weight could be evolutionarily conserved in mammals

Region-Based Association Test for Familial Data under Functional Linear Models

<div><p>Region-based association analysis is a more powerful tool for gene mapping than testing of individual genetic variants, particularly for rare genetic variants. The most powerful methods for regional mapping are based on the functional data analysis approach, which assumes that the regional genome of an individual may be considered as a continuous stochastic function that contains information about both linkage and linkage disequilibrium. Here, we extend this powerful approach, earlier applied only to independent samples, to the samples of related individuals. To this end, we additionally include a random polygene effects in functional linear model used for testing association between quantitative traits and multiple genetic variants in the region. We compare the statistical power of different methods using Genetic Analysis Workshop 17 mini-exome family data and a wide range of simulation scenarios. Our method increases the power of regional association analysis of quantitative traits compared with burden-based and kernel-based methods for the majority of the scenarios. In addition, we estimate the statistical power of our method using regions with small number of genetic variants, and show that our method retains its advantage over burden-based and kernel-based methods in this case as well. The new method is implemented as the R-function ‘famFLM’ using two types of basis functions: the B-spline and Fourier bases. We compare the properties of the new method using models that differ from each other in the type of their function basis. The models based on the Fourier basis functions have an advantage in terms of speed and power over the models that use the B-spline basis functions and those that combine B-spline and Fourier basis functions. The ‘famFLM’ function is distributed under GPLv3 license and is freely available at <a href="http://mga.bionet.nsc.ru/soft/famFLM/" target="_blank">http://mga.bionet.nsc.ru/soft/famFLM/</a>.</p></div

The statistical power of regional association analysis on the familial data when only rare variants were used in simulations for random selection of causal variants and 80% of non-causal variants were excluded from the analysis.

<p>The notations of the methods are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128999#pone.0128999.g001" target="_blank">Fig 1</a>.</p

The statistical power of regional association analysis on the familial data when only rare variants were used in simulations for random selection of causal variants and 50% of non-causal variants were excluded from the analysis.

<p>The notations of the methods are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128999#pone.0128999.g001" target="_blank">Fig 1</a>.</p