Scans for signatures of selection in Russian cattle breed genomes reveal new candidate genes for environmental adaptation and acclimation

Domestication and selective breeding has resulted in over 1000 extant cattle breeds. Many of these breeds do not excel in important traits but are adapted to local environments. These adaptations are a valuable source of genetic material for efforts to improve commercial breeds. As a step toward this goal we identified candidate regions to be under selection in genomes of nine Russian native cattle breeds adapted to survive in harsh climates. After comparing our data to other breeds of European and Asian origins we found known and novel candidate genes that could potentially be related to domestication, economically important traits and environmental adaptations in cattle. The Russian cattle breed genomes contained regions under putative selection with genes that may be related to adaptations to harsh environments (e.g., AQP5, RAD50, and RETREG1). We found genomic signatures of selective sweeps near key genes related to economically important traits, such as the milk production (e.g., DGAT1, ABCG2), growth (e.g., XKR4), and reproduction (e.g., CSF2). Our data point to candidate genes which should be included in future studies attempting to identify genes to improve the extant breeds and facilitate generation of commercial breeds that fit better into the environments of Russia and other countries with similar climates

Integrative Genomics Reveals the Genetics and Evolution of the Honey Bee’s Social Immune System

Social organisms combat pathogens through individual innate immune responses or through social immunity—behaviors among individuals that limit pathogen transmission within groups. Although we have a relatively detailed understanding of the genetics and evolution of the innate immune system of animals, we know little about social immunity. Addressing this knowledge gap is crucial for understanding how life-history traits influence immunity, and identifying if trade-offs exist between innate and social immunity. Hygienic behavior in the Western honey bee, Apis mellifera, provides an excellent model for investigating the genetics and evolution of social immunity in animals. This heritable, colony-level behavior is performed by nurse bees when they detect and remove infected or dead brood from the colony. We sequenced 125 haploid genomes from two artificially selected highly hygienic populations and a baseline unselected population. Genomic contrasts allowed us to identify a minimum of 73 genes tentatively associated with hygienic behavior. Many genes were within previously discovered QTLs associated with hygienic behavior and were predictive of hygienic behavior within the unselected population. These genes were often involved in neuronal development and sensory perception in solitary insects. We found that genes associated with hygienic behavior have evidence of positive selection within honey bees (Apis), supporting the hypothesis that social immunity contributes to fitness. Our results indicate that genes influencing developmental neurobiology and behavior in solitary insects may have been co-opted to give rise to a novel and adaptive social immune phenotype in honey bees.York University Librarie

Using haplotype differentiation among hierarchically structured populations for the detection of selection signatures

The detection of molecular signatures of selection is one of the major concerns of modern population genetics. A widely used strategy in this context is to compare samples from several populations, and to look for genomic regions with outstanding genetic differentiation between these populations. Genetic differentiation is generally based on allele frequency differences between populations, which are measured by Fst or related statistics. Here we introduce a new statistic, denoted hapFLK, which focuses instead on the differences of haplotype frequencies between populations. In contrast to most existing statistics, hapFLK accounts for the hierarchical structure of the sampled populations. Using computer simulations, we show that each of these two features - the use of haplotype information and of the hierarchical structure of populations - significantly improves the detection power of selected loci, and that combining them in the hapFLK statistic provides even greater power. We also show that hapFLK is robust with respect to bottlenecks and migration and improves over existing approaches in many situations. Finally, we apply hapFLK to a set of six sheep breeds from Northern Europe, and identify seven regions under selection, which include already reported regions but also several new ones. We propose a method to help identifying the population(s) under selection in a detected region, which reveals that in many of these regions selection most likely occurred in more than one population. Furthermore, several of the detected regions correspond to incomplete sweeps, where the favourable haplotype is only at intermediate frequency in the population(s) under selection

Detection of selective sweeps in structured populations : a comparison of recent methods

This work was supported by the Marie-Curie Initial Training Network INTERCROSSING (European Commission FP7). OEG was further supported by the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland). Date of Acceptance: 25/08/2015Identifying genomic regions targeted by positive selection has been a longstanding interest of evolutionary biologists. This objective was difficult to achieve until the recent emergence of Next Generation Sequencing, which is fostering the development of large-scale catalogs of genetic variation for increasing number of species. Several statistical methods have been recently developed to analyze these rich datasets but there is still a poor understanding of the conditions under which these methods produce reliable results. This study aims at filling this gap by assessing the performance of genome-scan methods that consider explicitly the physical linkage among SNPs surrounding a selected variant. Our study compares the performance of seven recent methods for the detection of selective sweeps (iHS, nSL, EHHST, xp-EHH, XP-EHHST, XPCLR and hapFLK). We use an individual-based simulation approach to investigate the power and accuracy of these methods under a wide range of population models under both hard and soft sweeps. Our results indicate that XPCLR and hapFLK perform best and can detect soft sweeps under simple population structure scenarios if migration rate is low. All methods perform poorly with moderate to high migration rates, or with weak selection and very poorly under a hierarchical population structure. Finally, no single method is able to detect both starting and nearly completed selective sweeps. However, combining several methods (XPCLR or hapFLK with iHS or nSL) can greatly increase the power to pinpoint the selected region.PostprintPeer reviewe

Selection Signatures in Worldwide Sheep Populations

The diversity of populations in domestic species offers great opportunities to study genome response to selection. The recently published Sheep HapMap dataset is a great example of characterization of the world wide genetic diversity in sheep. In this study, we re-analyzed the Sheep HapMap dataset to identify selection signatures in worldwide sheep populations. Compared to previous analyses, we made use of statistical methods that (i) take account of the hierarchical structure of sheep populations, (ii) make use of linkage disequilibrium information and (iii) focus specifically on either recent or older selection signatures. We show that this allows pinpointing several new selection signatures in the sheep genome and distinguishing those related to modern breeding objectives and to earlier post-domestication constraints. The newly identified regions, together with the ones previously identified, reveal the extensive genome response to selection on morphology, color and adaptation to new environments

Mapping genomic regions and genes associated with the fat-tail, an adaptation trait in indigenous sheep

Poster prepared for a share fair, Addis Ababa, May 201

Population structure of eleven Spanish ovine breeds and detection of selective sweeps with BayeScan and hapFLK

The goals of the current work were to analyse the population structure of 11 Spanish ovine breeds and to detect genomic regions that may have been targeted by selection. A total of 141 individuals were genotyped with the Infinium 50 K Ovine SNP BeadChip (Illumina). We combined this dataset with Spanish ovine data previously reported by the International Sheep Genomics Consortium (N = 229). Multidimensional scaling and Admixture analyses revealed that Canaria de Pelo and, to a lesser extent, Roja Mallorquina, Latxa and Churra are clearly differentiated populations, while the remaining seven breeds (Ojalada, Castellana, Gallega, Xisqueta, Ripollesa, Rasa Aragonesa and Segureña) share a similar genetic background. Performance of a genome scan with BayeScan and hapFLK allowed us identifying three genomic regions that are consistently detected with both methods i.e. Oar3 (150–154 Mb), Oar6 (4–49 Mb) and Oar13 (68–74 Mb). Neighbor-joining trees based on polymorphisms mapping to these three selective sweeps did not show a clustering of breeds according to their predominant productive specialization (except the local tree based on Oar13 SNPs). Such cryptic signatures of selection have been also found in the bovine genome, posing a considerable challenge to understand the biological consequences of artificial selection.Publishe

The genetic basis of feed efficiency in swine divergently selected for residual feed intake

Feed efficiency is an economically important trait in the swine industry since feed accounts more than 50% of total production costs. A measure of feed efficiency, residual feed intake (RFI), is defined as the difference between observed and expected feed intake based on production and maintenance requirements. Since 2001 at Iowa State University (ISU), divergent selection for improved (Low RFI) and reduced feed efficiency (High RFI) has been conducted in Yorkshire pigs for ten generations. Using these selection lines, the over-arching objective of this dissertation was to further our knowledge of the biological and genetic basis of RFI in pigs. The main objectives were to investigate genotype-by diet interactions, identify genomic regions associated with RFI and component traits, validate insulin like growth factor I (IGF-I) as an early genetic indicator of grow-finish RFI, and to evaluate correlated responses to selection for grow-finish RFI on feed efficiency and performance of nursery pigs. To quantify genotype by diet interactions for RFI, in generation (G) 8 through G10 of the high and low RFI lines, a lower-energy, higher-fiber (LEHF) diet was fed to a subset of pigs and compared to the performance of pigs fed a standard corn and soybean-meal based diet, similar to the diet fed during selection, which was higher in energy and lower in fiber (HELF). These diets differed in metabolizable energy (3.32 vs. 2.87 Mcal/kg for the HELF vs. LEHF diet) and neutral detergent fiber (9.4 vs. 25.9% NDF). When pigs were fed the HELF, the Low RFI pigs had lower average daily feed intake (-12%), energy intake (-12%), average daily gain (-6%), and backfat depth (-12%) than High RFI pigs (P \u3c 0.05). Regardless of RFI line, performance was reduced when pigs were fed the LEHF compared to the HELF diet. For the LEHF diet compared to the HELF diet, differences between the RFI lines were smaller for average daily feed intake (-11%), energy intake (-10%), gain to feed ratio (+2%), and RFI (-6%) (P \u3c 0.05). Feed digestibility was reduced when pigs were fed the LEHF diet, with the Low RFI pigs digesting significantly (P ≤ 0.04) more dry matter (+7%), gross energy (+7%), nitrogen (+10%) and NDF (+32%) than High RFI pigs when fed the LEHF diet. However, no line differences in digestibility were observed when the HELF diet was fed. Estimates of genetic correlations of performance traits across diets were high and positive for RFI and component traits, with a 0.87 ñ 0.28 genetic correlation for RFI across diets. The observed correlated response to selection in RFI when feeding the LEHF diet was 55% less than predicted based on the genetic correlation for RFI between diets. Genotype-by-diet interactions were further investigated by estimating single nucleotide polymorphism (SNP) by diet interactions, but these were found to account for less than 0.7% of the genetic variance in any given non-overlapping 1-Megabase window for RFI and component traits. By comparing the top genomic regions associated with RFI for the HELF and LEHF diets separately, we observed that the top associations were located on Sus scrofa chromosome (SSC) 2 near IGF2 (insulin like growth factor II) when pigs were fed the HELF but on SSC 6 for the LEHF diet, which demonstrates that at least some genomic regions associated with these traits were different between diets. This agrees with the estimated genetic correlation between diets for RFI (0.87 ñ 0.28) and provides more evidence of genotype-by-diet interactions for RFI. Genomic regions associated with RFI and component traits given the HELF, LEHF and both diets combined were identified using a genome-wide association study (GWAS). Two genomic regions were associated with multiple traits, indicating pleiotropic effects, on SSC1 near MC4R (melanocortin-4 receptor) and on SSC2 near IGF2. Results showed that the genetic architecture of RFI was highly polygenic. Genomic regions were also identified by evaluating genomic regions under selection in the ISU and in an independent Large White population that was also divergently selected for RFI (INRA). Regions were identified on SSC 2 near CAST (calpastatin) and on SSC 13 near GAPBA (GA binding protein transcription factor alpha subunit), which were different than associations found using GWAS. However, findings also suggested that the differences in RFI between the ISU and INRA Low and High RFI lines may be the result of selection affecting different genes and biological pathways, as few common regions were identified to be under selective pressure in the two populations. Using IGF-I data collected in G2 through G5, and in G10 and G11, IGF-I was found to have a positive genetic correlation with grow-finish RFI (0.54 ñ 0.19 for G2-G5 and 0.51 ñ 0.48 for G10-G11), indicating IGF-I is a good early biological marker for grow-finish RFI. In nursery-aged pigs in G10, the Low RFI line was found to eat less (-20%), grow slower (-10%) and have greater feed efficiency (+12% measured as gain to feed ratio) compared to the High RFI line, showing that selection for grow-finish RFI also improved nursery feed efficiency in the Low RFI line. In conclusion, RFI is a biologically and genetically complex trait with no genes with major effects and many genes contributing small effects. Nutritional value of the diet fed during selection impacts feed efficiency and its genetic architecture. Therefore, genotype-by-diet interactions must be taken into consideration in selection programs, particularly those that desire to improve feed efficiency. Genomic selection would be a good strategy to improve feed efficiency because of the highly polygenic genetic architecture of RFI. In addition, juvenile IGF-I can be used as a genetic indicator for grow-finish RFI. Finally, correlated responses to selection for grow-finish RFI also led to improved feed efficiency of Low RFI pigs in the nursery