Highest Proportion Of Genetic Variance Research Articles

Development of the Wheat Practical Haplotype Graph database as a resource for genotyping data storage and genotype imputation.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10−14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

Differential chromatin accessibility landscape reveals structural and functional features of the allopolyploid wheat chromosomes

BackgroundOur understanding of how the complexity of the wheat genome influences the distribution of chromatin states along the homoeologous chromosomes is limited. Using a differential nuclease sensitivity assay, we investigate the chromatin states of the coding and repetitive regions of the allopolyploid wheat genome.ResultsAlthough open chromatin is found to be significantly enriched around genes, the majority of MNase-sensitive regions are located within transposable elements (TEs). Chromatin of the smaller D genome is more accessible than that of the larger A and B genomes. Chromatin states of different TEs vary among families and are influenced by the TEs’ chromosomal position and proximity to genes. While the chromatin accessibility of genes is influenced by proximity to TEs, and not by their position on the chromosomes, we observe a negative chromatin accessibility gradient along the telomere-centromere axis in the intergenic regions, positively correlated with the distance between genes. Both gene expression levels and homoeologous gene expression bias are correlated with chromatin accessibility in promoter regions. The differential nuclease sensitivity assay accurately predicts previously detected centromere locations. SNPs located within more accessible chromatin explain a higher proportion of genetic variance for a number of agronomic traits than SNPs located within more closed chromatin.ConclusionsChromatin states in the wheat genome are shaped by the interplay of repetitive and gene-encoding regions that are predictive of the functional and structural organization of chromosomes, providing a powerful framework for detecting genomic features involved in gene regulation and prioritizing genomic variation to explain phenotypes.

GWAS and gene networks for milk-related traits from test-day multiple lactations in Portuguese Holstein cattle.

This study focused on the identification of QTL regions, candidate genes, and network related genes based on the first 3 lactations (LAC3) of milk, fat, and protein yields, and somatic cell score (SCS) in Portuguese Holstein cattle.Additionally, the results were compared with those from only first lactation (LAC1) data. The analyses were performed using the weighted single-step GWAS under an autoregressive test-day (TD) multiple lactations model. A total of 11,434,294 and 4,725,673 TD records from LAC3 and LAC1, respectively, including 38,323 autosomal SNPsand1338 genotyped animals were used in GWAS analyses. A total of 51 (milk), 5 (fat), 24 (protein), and 4 (SCS) genes were associated to previously annotated relevant QTL regionsfor LAC3. The CACNA2D1 at BTA4 explained the highest proportion of genetic variance respectively for milk, fat, and protein yields. For SCS, the TRNAG-CCC at BTA14, MAPK10, and PTPN3 genes, both at BTA6 were considered important candidate genes. The accessed network refined the importance of the reported genes. CACNA2D1 regulates calcium density and activation/inactivation kinetics of calcium transport in the mammary gland; whereas TRNAG-CCC, MAPK10, and PTPN3 are directly involved with inflammatory processes widely derived from mastitis. In conclusion, potential candidate genes (TRNAG-CCC, MAPK10, and PTPN3) associated with somatic cell were highlighted, which further validation studies are needed to clarify its mechanism action in response to mastitis. Moreover, most of the candidate genes identified were present in both (LAC3 and LAC1) for milk, fat and protein yields, except for SCS, in which no candidate genes were shared between LAC3 and LAC1. The larger phenotypic information provided by LAC3 dataset was more effective to identify relevant genes, providing a better understanding of the genetic architecture of these traits over all lactations simultaneously.

Genome-Wide Association Study Using Fix-Length Haplotypes and Network Analysis Revealed New Candidate Genes for Nematode Resistance and Body Weight in Blackface Lambs

Abstract The objectives of this study were to identify genomic regions by Bayesian methods (BayesA, BayesB, or BayesN) that fit fixed-length haplotypes or SNPs using GenSel. Covariates for haplo-type alleles of five lengths (125, 250, 500 kb, 1 or 2 Mb) were generated, and rare haplotypes were removed at three thresholds (1, 5, or 10%). Subsequently, we performed gene network analyses to investigate the biological processes shared by genes that were identified for the same across traits. Genotypes at 41,034 SNPs that were common on OvineSNP50 panel were phased for 751 Scottish Blackface (SBF) lambs. This is the first study to quantify the proportion of genetic variance using haplotypes across the whole genome in an SBF population. The genetic variance explained of haplotype-based GWAS was higher than that of SNP-based GWAS in across traits studied. In this population, fitting 500kb haplotypes with a 1% frequency threshold resulted in the highest proportion of genetic variance explained for nematode resistance and fitting 2Mb haplotypes with a 10% frequency threshold improved genetic variance explained for body weight comparable to fitting SNPs by BayesB. Candidate genes, including CXCR4, STAT4, CCL1, CCL2, CCL3, CCL5, CCL8, CCL16, CCL18, CARMIL2, and HSPA14 were identified for nematode resistance and ADH5, PPP3CA, and FABP4 for body weight traits. Network analysis provided annotation results linking to all identified candidate genes. This study supported previous results from GWAS of nematode resistance and body weight and revealed additional regions in the ovine genome associated with these economically important traits. These results suggest that network analysis can provide new information regarding biological mechanisms and genes leading to complex phenotypes, like nematode resistance and body weight of lamb.

Rapid and strong population genetic differentiation and genomic signatures of climatic adaptation in an invasive mealybug

AbstractAimA growing number of studies suggest that adaptation of invasive species plays key roles in their successful establishment in novel environments. However, adaptation of invasive species to climatic conditions remains poorly characterized. This study aimed to understand the population genetic structure produced by the cotton mealybug Phenacoccus solenopsis invasion and to identify preliminary signals of selection during its range expansion.LocationChina.MethodsWe examined genetic structure of 11 populations across China using SNPs, microsatellites and a segment of mitochondrial cox1 gene. ADMIXTURE, STRUCTURE and DAPC were used to infer population genetic structure; the dispersal routes were reconstructed by the DIYABC; SNPs potentially related to climate adaptation were identified by using four populations differentiation methods and three environmental association methods.ResultsStrong genetic differentiation was found among populations with FST values ranging from 0.097 to 0.640 based on SNPs. Populations located at the northern expansion edge exhibited the highest genetic differentiation and the lowest genetic diversity. Demographic analyses indicated that all populations were introduced from a single source population with small effective size and low recent gene flow. RDA analysis showed that climatic variables explained a higher proportion of genetic variance (43%) compared to population structure variables (15%). The top climatic variables associated with genetic differentiation were precipitation of the mean temperature of warmest quarter, mean temperature of driest quarter and isothermality. Genes related to climate candidate SNPs were mainly enriched to pathways of development, energy and xenobiotic metabolisms.Main conclusionsWe found that extremely rapid and strong population genetic differentiation among populations appears to have developed after introduction in the cotton mealybug. Our study points to rapid neutral evolution and suggests possible climatic adaptation despite low genetic diversity in this invasive species.

Identification of potential genomic regions for egg weight by a haplotype-based genome-wide association study using Bayesian methods

ABSTRACT 1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes as they are usually better at capturing multi-allelic QTL effects, compared to individual SNP genotypes in genome-wide association studies. The objectives of this study were to identify genomic regions related to egg weight traits by Bayesian methods (BayesA, BayesB, and BayesN) that fit fixed-length haplotypes using GenSel software. 2. Genotypes at 294,705 SNPs, that were common on a 600K Affymetrix chip, were phased for an egg-laying hen population of 1,063 birds. Recorded traits included first egg weight (FEW) and average egg weight at 28, 36, 56, 66, 72 and 80 weeks of age. 2. Fitting 1Mb haplotypes from BayesB resulted in the highest proportion of genetic variance explained for the egg weight traits. Based on the trait, the genetic variance explained by each marker ranged from 27% to 76%. 3. Different haplotype windows associated with egg weight traits only explained a small percentage of the genetic variance. 4. The top one 1-Mb window on GGA1 explained approximately 4.05% of total genetic variance for the FEW. Candidate genes, including PRKAR2B, HMGA2, LEMD3, GRIP1, EHBP1, MAP3K7, and MYH were identified for egg weight traits. 5. Several genomic regions, potentially associated with egg weight traits, were identified, some of which overlapped with known genes and previously reported QTL regions for egg production traits.

Single-Step Genome-Wide Association Study for Resistance to Piscirickettsia salmonis in Rainbow Trout (Oncorhynchus mykiss).

One of the main pathogens affecting rainbow trout (Oncorhynchus mykiss) farming is the facultative intracellular bacteria Piscirickettsia salmonis. Current treatments, such as antibiotics and vaccines, have not had the expected effectiveness in field conditions. Genetic improvement by means of selection for resistance is proposed as a viable alternative for control. Genomic information can be used to identify the genomic regions associated with resistance and enhance the genetic evaluation methods to speed up the genetic improvement for the trait. The objectives of this study were to i) identify the genomic regions associated with resistance to P. salmonis; and ii) identify candidate genes associated with the trait in rainbow trout. We experimentally challenged 2,130 rainbow trout with P. salmonis and genotyped them with a 57 K single nucleotide polymorphism (SNP) array. Resistance to P. salmonis was defined as time to death (TD) and as binary survival (BS). Significant heritabilities were estimated for TD and BS (0.48 ± 0.04 and 0.34 ± 0.04, respectively). A total of 2,047 fish and 26,068 SNPs passed quality control for samples and genotypes. Using a single-step genome wide association analysis (ssGWAS) we identified four genomic regions explaining over 1% of the genetic variance for TD and three for BS. Interestingly, the same genomic region located on Omy27 was found to explain the highest proportion of genetic variance for both traits (2.4 and 1.5% for TD and BS, respectively). The identified SNP in this region is located within an exon of a gene related with actin cytoskeletal organization, a protein exploited by P. salmonis during infection. Other important candidate genes identified are related with innate immune response and oxidative stress. The moderate heritability values estimated in the present study show it is possible to improve resistance to P. salmonis through artificial selection in the rainbow trout population studied here. Furthermore, our results suggest a polygenic genetic architecture for the trait and provide novel insights into the candidate genes underpinning resistance to P. salmonis in O. mykiss.

Genome-wide association analysis for body weight identifies candidate genes related to development and metabolism in rainbow trout (Oncorhynchus mykiss).

Growth is one of the most important traits from both aphysiological and economic perspective in aquaculture species. Thus, identifying the genomic regions and genes underpinning genetic variation for this trait is of particular interest in several fish species, including rainbow trout. In this work, we perform a genome-wide association study (GWAS) to identify the genomic regions associated withbody weight at tagging (BWT) and at 18 months (BW18M) using a dense SNP panel (57k) and 4596 genotyped rainbow trout from 105 full-sib families belonging to a Chilean breeding population. Analysis was performed by means of single-step GBLUP approach. Genetic variance explained by 20 adjacent SNP windows across the whole genome is reported. To further explore candidate genes, we focused on windows that explained the highest proportion of genetic variance in the top 10 chromosomes for each trait. The main window from the top 10 chromosomes was explored by BLAST using the first and last SNP position of each window to determine the targetnucleotide sequence. As expected,thepercentage of genetic variance explained by windows was relatively low, due to the polygenic nature of body weight. The most important genomic region for BWT and BW18M were located on chromosomes 15 and 24 and they explained 2.14% and 3.02% of the genetic variance for each trait, respectively. Candidate genes including several growth factors, genes involved in development of skeletal muscle and bone tissue and nutrient metabolism were identified within the associated regions for both traits BWT and BW18M. These results indicate that body weight is polygenic in nature in rainbow trout, with the most important loci explaining as muchas 3% of the genetic variance for the trait. The genes identified here represent good candidates for further functional validation to uncover biological mechanisms underlying variation for growth in rainbow trout.