Extent Of Linkage Disequilibrium Research Articles

Analysis of genetic diversity and genetic structure of indigenous chicken populations in Guizhou province based on genome-wide single nucleotide polymorphism markers

Guizhou province in China is rich in indigenous chicken breeds, playing an essential role in the genetic improvement of modern chickens. Genetic diversity has decreased in recent decades due to accelerated breeding processes and changing conservation priorities. To determine the genetic diversity and population structure of Guizhou indigenous chicken breeds, we used 55K genotyping arrays to conduct population genetic analysis on 233 individuals from 8 Guizhou indigenous breeds and 263 individuals from 9 Guizhou indigenous chicken populations. We evaluated the genetic diversity parameter (heterozygosity, proportion of polymorphic markers, and nucleotide diversity), linkage disequilibrium (LD), population structure, and genetic differentiation (FST and genetics distance). Genetic diversity results indicated that the genetic diversity of chicken breeds in Guizhou province is relatively affluent. Among Guizhou breeds, Baiyi black-bone and Guizhou yellow chicken displayed the lowest genetic diversity, as the 2 breeds exhibit lower PN and heterozygosity, the extent of linkage disequilibrium is higher. According to the LD pattern, Guizhou indigenous breeds can be divided into 3 categories. Population structure analysis showed a certain degree of genetic differentiation among local chickens in Guizhou. We argue that Chishui black-bone and Puan black-bone chickens are 2 different geographical regional groups of the same breed. In principal component analysis, individuals from the 2 groups clustered together, and the phylogenetic tree results showed that the 2 groups clustered together to form a branch independent of other breeds, and they displayed an identical pattern of ancestral lineage composition. The research results will provide a reference for protecting local chicken genetic resources in Guizhou Province and promote the protection and utilization of genetic resources.

Effective population size in field pea

BackgroundEffective population size (Ne) is a pivotal parameter in population genetics as it can provide information on the rate of inbreeding and the contemporary status of genetic diversity in breeding populations. The population with smaller Ne can lead to faster inbreeding, with little potential for genetic gain making selections ineffective. The importance of Ne has become increasingly recognized in plant breeding, which can help breeders monitor and enhance the genetic variability or redesign their selection protocols. Here, we present the first Ne estimates based on linkage disequilibrium (LD) in the pea genome.ResultsWe calculated and compared Ne using SNP markers from North Dakota State University (NDSU) modern breeding lines and United States Department of Agriculture (USDA) diversity panel. The extent of LD was highly variable not only between populations but also among different regions and chromosomes of the genome. Overall, NDSU had a higher and longer-range LD than the USDA that could extend up to 500 Kb, with a genome-wide average r2 of 0.57 (vs 0.34), likely due to its lower recombination rates and the selection background. The estimated Ne for the USDA was nearly three-fold higher (Ne = 174) than NDSU (Ne = 64), which can be confounded by a high degree of population structure due to the selfing nature of pea.ConclusionsOur results provided insights into the genetic diversity of the germplasm studied, which can guide plant breeders to actively monitor Ne in successive cycles of breeding to sustain viability of the breeding efforts in the long term.

On the estimation of genome-average recombination rates.

The rate at which recombination events occur in a population is an indicator of its effective population size and the organism's reproduction mode. It determines the extent of linkage disequilibrium along the genome and, thereby, the efficacy of both purifying and positive selection. The population recombination rate can be inferred using models of genome evolution in populations. Classic methods based on the patterns of linkage disequilibrium provide the most accurate estimates, providing large sample sizes are used and the demography of the population is properly accounted for. Here, the capacity of approaches based on the sequentially Markov coalescent (SMC) to infer the genome-average recombination rate from as little as a single diploid genome is examined. SMC approaches provide highly accurate estimates even in the presence of changing population sizes, providing that (1) within genome heterogeneity is accounted for and (2) classic maximum-likelihood optimization algorithms are employed to fit the model. SMC-based estimates proved sensitive to gene conversion, leading to an overestimation of the recombination rate if conversion events are frequent. Conversely, methods based on the correlation of heterozygosity succeed in disentangling the rate of crossing over from that of gene conversion events, but only when the population size is constant and the recombination landscape homogeneous. These results call for a convergence of these two methods to obtain accurate and comparable estimates of recombination rates between populations.

Estrogen Receptor 1 Gene Polymorphism and its Association with Idiopathic Short Stature in a North Indian Population

In the hypothalamic-pituitary-gonadotrophin axis, estrogen plays a key role in the regulation of bone maturation and growth plate closure. This study was designed to explore the link between single nucleotide polymorphisms (SNPs) in the estrogen receptor 1 (ESR1) gene with idiopathic short stature (ISS) susceptibility in a North Indian population. Four SNPs of ESR1 (rs543650, rs6557177, rs2234693 and rs9340799) were genotyped by Sanger sequencing in ISS patients and controls. Linkage disequilibrium (LD) and haplotyping were done by SNPStat and SHEsisPlus software. The extent of LD was determined by calculating D’ and R2 values in SNP paired combinations. Fifty-two ISS patients were compared with 68 controls. A significant positive association was found between rs6557177 and rs543650 genotype and ISS susceptibility. The frequencies of the rs6557177 CC genotype [p=0.030; odds ratio (OR)=0.13; 95% confidence interval (CI): 0.01-1.10] and rs543650 genotype TT (p=0.043; OR=0.29; 95% CI: 0.09-0.92) were increased in the ISS group compared with controls. However, no significant correlation was observed between clinical parameters of patients and these SNPs. rs543650 showed strong LD with rs2234693 and rs9340799, similarly rs2234693 and rs9340799. Our study showed that the CC genotype at rs6557177 and TT genotype at rs543650 of ESR1 constituted a risk factor for developing ISS in North Indian children. These findings may lead to a better understanding of the SNPs associated with ISS susceptibility.

Genetic insights into crossbred dairy cattle of Pakistan: exploring allele frequency, linkage disequilibrium, and effective population size at a genome-wide scale.

Linkage disequilibrium (LD) affects genomic studies accuracy. High-density genotyping platforms identify SNPs across animal genomes, increasing LD evaluation resolution for accurate analysis. This study aimed to evaluate the decay and magnitude of LD in a cohort of 81 crossbred dairy cattle using the GGP_HDv3_C Bead Chip. After quality control, 116,710 Single Nucleotide Polymorphisms (SNPs) across 2520.241Mb of autosomes were retained. LD extent was assessed between autosomal SNPs within a 10Mb range using the r2 statistics. LD value declined as inter-marker distance increased. The average r2 value was 0.24 for SNP pairs < 10kb apart, decreasing to 0.13 for 50-100kb distances. Minor allele frequency (MAF) and sample size significantly impact LD. Lower MAF thresholds result in smaller r2 values, while higher thresholds show increased r2 values. Additionally, smaller sample sizes exhibit higher average r2 values, especially for larger physical distance intervals (> 50kb) between SNP pairs. Effective population size and inbreeding coefficient were 150 and 0.028 for the present generation, indicating a decrease in genetic diversity over time. These findings imply that the utilization of high-density SNP panels and customized/breed-specific SNP panels represent a highly favorable approach for conducting genome-wide association studies (GWAS) and implementing genomic selection (GS) in the Bos indicus cattle breeds, whose genomes are still largely unexplored. Furthermore, it is imperative to devise a meticulous breeding strategy tailored to each herd, aiming to enhance desired traits while simultaneously preserving genetic diversity.

Linkage disequilibrium and effective population size in Indian goat breeds

Genomic linkage disequilibrium (LD) is an important population diversity measure and an indicator determining the efficacy of genomic applications. The present population genomic study was carried out to comprehensively characterise the extent and pattern of LD in the two divergent Indian goat breeds namely, Attappady Black and Malabari goats reared under small holder production system. The LD was estimated using genome-wide single nucleotide polymorphism (SNP) marker data obtained by Illumina SNP50 goat BeadChip genotyping. The LD between adjacent SNPs across all autosomes measured as correlation coefficient (r2) in the studied population was low and mean ± standard deviation of LD were 0.0657 ± 0.0921 and 0.0661 ± 0.0911 for Attappady Black and Malabari goats, respectively. The LD varied significantly (p < 0.0001) among chromosomes, interaction between chromosome and breed and among marker intervals. The LD (r2) of 0.2 and 0.3 were extended up to 20 kb and 10 kb marker intervals, respectively. Based on the genomic data, Ne was estimated as 512.8198 in Malabari and 371.2220 in Attappady Black goats at 17 generations back. The weak LD observed in Indian goat breeds indicates increased diversity of these Indian goat breeds and also implies that denser SNP panel might be more beneficial for genomic applications.

Genome-wide analysis of genetic diversity in a germplasm collection including wild relatives and interspecific clones of garden asparagus.

The Asparagus genus includes approximately 240 species, the most important of which is garden asparagus (Asparagus officinalis L.), as this is a vegetable crop cultivated worldwide for its edible spear. Along with garden asparagus, other species are also cultivated (e.g., Asparagus maritimus L.) or have been proposed as untapped sources of variability in breeding programs (e.g., Asparagus acutifolius L.). In the present work, we applied reduced-representation sequencing to examine a panel of 378 diverse asparagus genotypes, including commercial hybrids, interspecific lines, wild relatives of garden asparagus, and doubled haploids currently used in breeding programs, which enabled the identification of more than 200K single-nucleotide polymorphisms (SNPs). These SNPs were used to assess the extent of linkage disequilibrium in the diploid gene pool of asparagus and combined with preliminary phenotypic information to conduct genome-wide association studies for sex and traits tied to spear quality and production. Moreover, using the same phenotypic and genotypic information, we fitted and cross-validated genome-enabled prediction models for the same set of traits. Overall, our analyses demonstrated that, unlike the diversity detected in wild species related to garden asparagus and in interspecific crosses, cultivated and wild genotypes of A. officinalis L. show a narrow genetic basis, which is a contributing factor hampering the genetic improvement of this crop. Estimating the extent of linkage disequilibrium and providing the first example of genome-wide association study and genome-enabled prediction in this species, we concluded that the asparagus panel examined in the present study can lay the foundation for determination of the genetic bases of agronomically important traits and for the implementation of predictive breeding tools to sustain breeding.

Integration of pharmacogenomic and pharmacomicrobiomic data for personalized medicine

AbstractAdverse drug reaction (ADR) is quite common and poses a huge medical and economic burden all over the world. Understanding the role of genetic variant and microbes in drug response and using this knowledge in prescribing drugs will help to reduce ADR and improve drug efficacy. We have collected pharmacogenomic and pharmacomicrobiomics data currently available for several drugs. We found both the associated genetic variants and microbes for only five drugs (Fluorouracil, Irinotecan, Methotrexate, Ribavirin, Warfarin). Among them, 14 single nucleotide polymorphisms (SNPs) were found to be associated with response to fluorouracil, 6 with Ribavirin, 8 with warfarin, 1 with irinotecan, and 3 with methotrexate. Allelic distribution shows significant differences among different populations and African population have lower extent of linkage disequilibrium than non‐Africans. Various microbial species are associated with either increasing or decreasing drug effectivity or toxicity of those drugs. This study indicates a great potential if these two fields are explored more and used in combination for drug therapy.

Comparison of SNP variation and extent of linkage disequilibrium in Holstein and Jersey cattle populations in Korea

Comparison of SNP variation and extent of linkage disequilibrium in Holstein and Jersey cattle populations in Korea

Effects of marker density and training set size on the genomic selection accuracy for predicting sheathblight resistance in rice (Oryza sativa L.)

The success of genomic selection mainly depends on the extent of linkage disequilibrium between markers and quantitative trait loci, size of training set, heritability of the trait etc. The extent of linkage disequilibrium depends on the genetic structure of the population and marker density. This study was conducted to determine the effects of marker density and size of training set on prediction accuracy using 1545 recombinant inbred lines derived from eleven bi-parental rice populations. All RILs were genotyped with 6564 SNPs and screened in two hot spot locations to assess reaction against sheath blight.Bayesian B model was used to train the statistical model for calculation of marker effects and genomic estimated breeding values. To evaluate the genomic prediction accuracy, various levels of training set size (300, 500, 700, 900 and 1200 lines) and marker density (500, 800, 1100, 1400, 1700, 2000, 4000 and 6000 markers) were considered. In our study, the prediction accuracy increased with increase in training set size, however, average prediction accuracy of 0.717 was obtained for the training set comprising of 900 lines before reaching plateau with marginal increase in prediction accuracy with higher training set sizes. The predictive ability increased dramatically with more SNPs included in the analysis until 2000 markers with average prediction accuracy of 0.681, no significant improvement beyond this was observed. The results indicate that training set with approximately 900 lines and 2000 uniformly distributed SNP markers with good amount of polymorphism across populations would be enough to reach achievable accuracy to predict sheath blight resistance in rice.

Genotype imputation for soybean nested association mapping population to improve precision of QTL detection

Key messageSoftware for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification.Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.

Genetic structure analysis in several populations of cattle using SNP genotypes

Parameters such as effective population size (Ne), runs of homozygosity (ROH), and inbreeding based on ROH (FROH) can give new insight into the level of genetic diversity for the population under selection. This research aimed to measure the extent of linkage disequilibrium (LD), effective population size (Ne), Haplotype Block Structure, and runs of homozygosity (ROHs) in several populations of cattle using SNP genotypes. In this study, that the average r 2 decreased with the increasing distance of SNP pairs. A general decrease in Ne can be seen for all four populations, indicating a loss of genetic diversity. The Iranian Holstein had the lowest level of genomic inbreeding at an ROH of 1, 5, 10 Mb, while the French Holstein had the highest. The maximum number of ROH is seen at a distance of less than 1 Mb, and the lowest number of ROH is seen at a distance of 10 Mb. The number of ROH decreases with increasing distance due to the increased recombination rate. This is a concern as an increase in inbreeding leads to a reduction in the effective population size, which was also evident in the study populations.

Linkage disequilibrium and population structure in a core collection of Brassica napus (L.).

Estimation of genetic diversity in rapeseed is important for sustainable breeding program to provide an option for the development of new breeding lines. The objective of this study was to elucidate the patterns of genetic diversity within and among different structural groups, and measure the extent of linkage disequilibrium (LD) of 383 globally distributed rapeseed germplasm using 8,502 single nucleotide polymorphism (SNP) markers. We divided the germplasm collection into five subpopulations (P1 to P5) according to geographic and growth habit-related patterns. All subpopulations showed moderate genetic diversity (average H = 0.22 and I = 0.34). The pairwise Fst comparison revealed a great degree of divergence (Fst > 0.24) between most of the combinations. The rutabaga type showed highest divergence with spring and winter types. Higher divergence was also found between winter and spring types. Admixture model based structure analysis, principal component and neighbor-joining tree analysis placed all subpopulations into three distinct clusters. Admixed genotype constituted 29.24% of total genotypes, while remaining 70.76% belongs to identified clusters. Overall, mean linkage disequilibrium was 0.03 and it decayed to its half maximum within < 45 kb distance for whole genome. The LD decay was slower in C genome (< 93 kb); relative to the A genome (< 21 kb) which was confirmed by availability of larger haplotype blocks in C genome than A genome. The findings regarding LD pattern and population structure will help to utilize the collection as an important resource for association mapping efforts to identify genes useful in crop improvement as well as for selection of parents for hybrid breeding.

Genomic prediction models for traits differing in heritability for soybean, rice, and maize

BackgroundGenomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures.ResultsCorrelations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions.ConclusionsWe conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.

The pattern of linkage disequilibrium in Dual-Purpose Belgian Blue cattle.

Quantifying the level of linkage disequilibrium (LD), non-random association of alleles at two or more loci, is important to determine the number of markers needed for genomic selection. The aims of this study were to evaluate the extent of LD in Dual-Purpose Belgian Blue (DPBB) and to compare the level of LD in DPBB with that of Walloon Holstein. Data of 28,427 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes(BTA), of 639 DPBB and 398 Holstein bulls were used. The level of LD between pairwise SNPs separated by up to 10Mb was evaluated, separately for each breed, using the squared correlation of the alleles at two loci. The analysis of molecular variance showed that the percentage of variation within populations (85.48%) was higher than between populations (14.52%). However, permutation tests showed a significant genetic differentiation between the two studied populations (p<.01). The average LD found between adjacent SNP pairs in DPBB (0.16 (SD=0.22)) was generally lower than in Holstein (0.23 (SD=0.27)). The proportion of SNPs in useful LD (r2 >0.30) within a genomic distance of ≤0.10Mb between SNPs was 18.58% and 28.23% in DPBB and Holstein bulls, respectively. In both breeds, the effective population size decreased over generations; however, the decline was greater in DPBB than that in Holstein. Based on results, it can be concluded that at least 68,000 SNPs are needed for implementing genomic selection in DPBB cattle with enough accuracy.

Estimation of linkage disequilibrium levels and allele frequency distribution in crossbred Vrindavani cattle using 50K SNP data.

The objective of this study was to calculate the extent and decay of linkage disequilibrium (LD) in 96 crossbred Vrindavani cattle genotyped with Bovine SNP50K Bead Chip. After filtering, 43,821 SNPs were retained for final analysis, across 2500.3 Mb of autosome. A significant percentage of SNPs was having minor allele frequency of less than 0.20. The extent of LD between autosomal SNPs up to 10 Mb apart across the genome was measured using r2 statistic. The mean r2 value was 0.43, if pairwise distance of marker was less than10 kb and it decreased further to 0.21 for 25–50 kb markers distance. Further, the effect of minor allele frequency and sample size on LD estimate was investigated. The LD value decreased with the increase in inter-marker distance, and increased with the increase of minor allelic frequency. The estimated inbreeding coefficient and effective population size were 0.04, and 46 for present generation, which indicated small and unstable population of Vrindavani cattle. These findings suggested that a denser or breed specific SNP panel would be required to cover all genome of Vrindavani cattle for genome wide association studies (GWAS).

Genomic inference of contemporary effective population size in a large island population of collared flycatchers (Ficedula albicollis).

Due to its central importance to many aspects of evolutionary biology and population genetics, the long-term effective population size (Ne ) has been estimated for numerous species and populations. However, estimating contemporary Ne is difficult and in practice this parameter is often unknown. In principle, contemporary Ne can be estimated using either analyses of temporal changes in allele frequencies, or the extent of linkage disequilibrium (LD) between unlinked markers. We applied these approaches to estimate contemporary Ne of a relatively recently founded island population of collared flycatchers (Ficedula albicollis). We sequenced the genomes of 85 birds sampled in 1993 and 2015, and applied several temporal methods to estimate Ne at a few thousand (4000-7000). The approach based on LD provided higher estimates of Ne (20,000-32,000) and was associated with high variance, often resulting in infinite Ne . We conclude that whole-genome sequencing data offers new possibilities to estimate high (>1000) contemporary Ne , but also note that such estimates remain challenging, in particular for LD-based methods for contemporary Ne estimation.

A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars.

The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat (Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected for traits under each condition, whereas 17 marker-trait associations were common between terminal drought stress and well-watered conditions. The identified marker-trait associations were mostly single nucleotide polymorphisms with minor allele effects. This study examined the effect of population structure, genomic selection method (ridge regression-best linear unbiased prediction, genomic best-linear unbiased predictions, and Bayesian ridge regression), training set size, and type of marker set on genomic prediction accuracy. The prediction accuracies were low (-0.32) to moderate (0.52). A marker set including 93 significant markers identified through genome-wide association studies with P values ≤ 0.001 increased the genomic prediction accuracy for all traits under both conditions. This study concluded that obtaining the highest genomic prediction accuracy depends on the extent of linkage disequilibrium, the genetic architecture of trait, genetic diversity of the population, and the genomic selection method. The results encouraged the integration of genome-wide association study and genomic selection to enhance genomic prediction accuracy in applied breeding programs.

Haplotype Block Analysis Reveals Candidate Genes and QTLs for Meat Quality and Disease Resistance in Chinese Jiangquhai Pig Breed.

The Jiangquhai (JQ) pig breed is one of the most widely recognized pig populations in China due to its unique and dominant characteristics. In this study, we examined the extent of Linkage disequilibrium (LD) and haplotype block structure of the JQ pig breed, and scanned the blocks for possible genes underlying important QTLs that could either be responsible for some adaptive features in these pigs or might have undergone some selection pressure. We compared some of our results with other Chinese and Western pig breeds. The results show that the JQ breed had the highest total block length (349.73 Mb ≈ 15% of its genome), and the coverage rate of blocks in most of its chromosomes was larger than those of other breeds except for Sus scrofa chromosome 4 (SSC4), SSC6, SSC7, SSC8, SSC10, SSC12, SSC13, SSC14, SSC17, SSC18, and SSCX. Moreover, the JQ breed had more SNPs that were clustered into haplotype blocks than the other breeds examined in this study. Our shared and unique haplotype block analysis revealed that the Hongdenglong (HD) breed had the lowest percentage of shared haplotype blocks while the Shanzhu (SZ) breed had the highest. We found that the JQ breed had an average r2 > 0.2 at SNPs distances 10–20 kb and concluded that about 120,000–240,000 SNPs would be needed for a successful GWAS in the breed. Finally, we detected a total of 88 genes harbored by selected haplotype blocks in the JQ breed, of which only 4 were significantly enriched (p-value ≤ 0.05). These genes were significantly enriched in 2 GO terms (p-value < 0.01), and 2 KEGG pathways (p-value < 0.02). Most of these enriched genes were related to health. Also, most of the overlapping QTLs detected in the haplotype blocks were related to meat and carcass quality, as well as health, with a few of them relating to reproduction and production. These results provide insights into the genetic architecture of some adaptive and meat quality traits observed in the JQ pig breed and also revealed the pattern of LD in the genome of the pig. Our result provides significant guidance for improving the statistical power of GWAS and optimizing the conservation strategy for this JQ pig breed.

A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments.

SummaryThe incorporation of resistance genes into wheat commercial varieties is the ideal strategy to combat stripe or yellow rust (YR). In a search for novel resistance genes, we performed a large‐scale genomic association analysis with high‐density 660K single nucleotide polymorphism (SNP) arrays to determine the genetic components of YR resistance in 411 spring wheat lines. Following quality control, 371 972 SNPs were screened, covering over 50% of the high‐confidence annotated gene space. Nineteen stable genomic regions harbouring 292 significant SNPs were associated with adult‐plant YR resistance across nine environments. Of these, 14 SNPs were localized in the proximity of known loci widely used in breeding. Obvious candidate SNP variants were identified in certain confidence intervals, such as the cloned gene Yr18 and the major locus on chromosome 2BL, despite a large extent of linkage disequilibrium. The number of causal SNP variants was refined using an independent validation panel and consideration of the estimated functional importance of each nucleotide polymorphism. Interestingly, four natural polymorphisms causing amino acid changes in the gene TraesCS2B01G513100 that encodes a serine/threonine protein kinase (STPK) were significantly involved in YR responses. Gene expression and mutation analysis confirmed that STPK played an important role in YR resistance. PCR markers were developed to identify the favourable TraesCS2B01G513100 haplotype for marker‐assisted breeding. These results demonstrate that high‐resolution SNP‐based GWAS enables the rapid identification of putative resistance genes and can be used to improve the efficiency of marker‐assisted selection in wheat disease resistance breeding.