Levels Of Linkage Disequilibrium Research Articles

PREDICTION OF PHENOTYPIC AND GENOTYPIC VALUES BY BLUP/GWS AND NEURAL NETWORKS

ABSTRACT Genome-wide selection (GWS) uses simultaneously the effect of the thousands markers covering the entire genome to predict genomic breeding values for individuals under selection. The possible benefits of GWS are the reduction of the breeding cycle, increase in gains per unit of time, and decrease of costs. However, the success of the GWS is dependent on the choice of the method to predict the effects of markers. Thus, the objective of this work was to predict genomic breeding values (GEBV) through artificial neural networks (ANN), based on the estimation of the effect of the markers, compared to the Ridge Regression-Best Linear Unbiased Predictor/Genome Wide Selection (RR-BLUP/GWS). Simulations were performed by software R to provide correlations concerning ANN and RR-BLUP/GWS. The prediction methods were evaluated using correlations between phenotypic and genotypic values and predicted GEBV. The results showed the superiority of the ANN in predicting GEBV in simulations with higher and lower marker densities, with higher levels of linkage disequilibrium and heritability.

SNPrune: an efficient algorithm to prune large SNP array and sequence datasets based on high linkage disequilibrium

BackgroundHigh levels of pairwise linkage disequilibrium (LD) in single nucleotide polymorphism (SNP) array or whole-genome sequence data may affect both performance and efficiency of genomic prediction models. Thus, this warrants pruning of genotyping data for high LD. We developed an algorithm, named SNPrune, which enables the rapid detection of any pair of SNPs in complete or high LD throughout the genome.MethodsLD, measured as the squared correlation between phased alleles (r2), can only reach a value of 1 when both loci have the same count of the minor allele. Sorting loci based on the minor allele count, followed by comparison of their alleles, enables rapid detection of loci in complete LD. Detection of loci in high LD can be optimized by computing the range of the minor allele count at another locus for each possible value of the minor allele count that can yield LD values higher than a predefined threshold. This efficiently reduces the number of pairs of loci for which LD needs to be computed, instead of considering all pairwise combinations of loci. The implemented algorithm SNPrune considered bi-allelic loci either using phased alleles or allele counts as input. SNPrune was validated against PLINK on two datasets, using an r2 threshold of 0.99. The first dataset contained 52k SNP genotypes on 3534 pigs and the second dataset contained simulated whole-genome sequence data with 10.8 million SNPs and 2500 animals.ResultsSNPrune removed a similar number of SNPs as PLINK from the pig data but SNPrune was almost 12 times faster than PLINK. From the simulated sequence data with 10.8 million SNPs, SNPrune removed 6.4 and 1.4 million SNPs due to complete and high LD. Results were very similar regardless of whether phased alleles or allele counts were used. Using allele counts and multi-threading with 10 threads, SNPrune completed the analysis in 21 min. Using a sliding window of up to 500,000 SNPs, PLINK removed ~ 43,000 less SNPs (0.6%) in the sequence data and SNPrune was 24 to 170 times faster, using one or ten threads, respectively.ConclusionsThe SNPrune algorithm developed here is able to remove SNPs in high LD throughout the genome very efficiently in large datasets.

Vers une sélection génomique chez les caprins laitiers

La sélection génomique, qui a révolutionné la sélection génétique des bovins laitiers notamment, est désormais envisagée dans d’autres filières animales. Chez les caprins laitiers français, le gain de précision attendu des valeurs génomiques était un des questionnements de la filière en raison de la petite taille de la population de référence disponible (825 mâles et 1945 femelles génotypés sur une puce SNP 50K). Le but de cette étude est de tester différentes techniques d’évaluation génomique afin d’obtenir les évaluations génomiques les plus précises possibles. Une étude de la structure génétique de la population de référence caprine constituée d’animaux de races Saanen et Alpine, a révélé de faibles niveaux de déséquilibre de liaison (0,17 entre deux SNP consécutifs), de consanguinité et de parenté au sein de la population, ce qui n’est pas favorable à une bonne précision des évaluations génomiques. Les méthodes d’évaluations génomiques (GBLUP ou Bayésiennes), basées sur des performances pré-corrigées n’ont pas permis une amélioration significative des précisions des évaluations génomiques pour les caractères évalués en routine (caractères de production, de morphologie et comptages de cellules somatiques). Cependant les évaluations génomiques basées sur les performances propres des femelles ont permis d’obtenir des précisions supérieures à celles obtenues sur ascendance. La sélection génomique est donc envisageable chez les caprins laitiers français. Ces précisions peuvent également être légèrement augmentées par l’inclusion de gènes majeurs tels que celui de la caséine αs1.

Assessing introgressive hybridization between blue wildebeest (Connochaetes taurinus) and black wildebeest (Connochaetes gnou) from South Africa

Introgressive hybridization poses a threat to the genetic integrity of black wildebeest (Connochaetes gnou) and blue wildebeest (Connochaetes taurinus) populations in South Africa. Black wildebeest is endemic to South Africa and was driven to near extinction in the early 1900s due to habitat destruction, hunting pressure and disease outbreaks. Blue wildebeest on the other hand are widely distributed in southern and east Africa. In South Africa the natural distribution ranges of both species overlap, however, extensive translocation of black wildebeest outside of its normal distribution range in South Africa have led to potential hybridization between the two species. The molecular identification of pure and admixed populations is necessary to design viable and sustainable conservation strategies, since phenotypic evidence of hybridization is inconclusive after successive generations of backcrossing. The aim of this study was to assess levels of hybridization in wildebeest using both species-specific and cross-species microsatellite markers. Black wildebeest (157) and blue wildebeest (122) from provincial and national parks and private localities were included as reference material, with 180 putative hybrid animals also screened. A molecular marker panel consisting of 13 cross-species and 11 species-specific microsatellite markers was developed. We used a Bayesian clustering model to confirm the uniqueness of blue- and black wildebeest reference groups, assign individuals to each of the two clusters, and determine levels of admixture. Results indicated a clear partition between black wildebeest and blue wildebeest (the average proportions of membership to black wildebeest and blue wildebeest clusters were QI = 0.994 and QI = 0.955 respectively). From the putative hybrid samples, only five hybrid individuals were confirmed. However, high levels of linkage disequilibrium were observed in the putative hybrid populations which may indicate historical hybridization. Measures of genetic diversity in the black wildebeest populations were found to be lower than that of the blue wildebeest. The observed lower level of genetic diversity was expected due to the demographic history of the specie. This study will make a significant contribution to inform a national conservation strategy to conserve the genetic integrity of both species.

Contrasting results from GWAS and QTL mapping on wing length in great reed warblers.

A major goal in evolutionary biology is to understand the genetic basis of adaptive traits. In migratory birds, wing morphology is such a trait. Our previous work on the great reed warbler (Acrocephalus arundinaceus) shows that wing length is highly heritable and under sexually antagonistic selection. Moreover, a quantitative trait locus (QTL) mapping analysis detected a pronounced QTL for wing length on chromosome 2, suggesting that wing morphology is partly controlled by genes with large effects. Here, we re-evaluate the genetic basis of wing length in great reed warblers using a genomewide association study (GWAS) approach based on restriction site-associated DNA sequencing (RADseq) data. We use GWAS models that account for relatedness between individuals and include covariates (sex, age and tarsus length). The resulting association landscape was flat with no peaks on chromosome 2 or elsewhere, which is in line with expectations for polygenic traits. Analysis of the distribution of p-values did not reveal biases, and the inflation factor was low. Effect sizes were however not uniformly distributed on some chromosomes, and the Z chromosome had weaker associations than autosomes. The level of linkage disequilibrium (LD) in the population decayed to background levels within c. 1 kbp. There could be several reasons to why our QTL study and GWAS gave contrasting results including differences in how associations are modelled (cosegregation in pedigree vs. LD associations), how covariates are accounted for in the models, type of marker used (multi- vs. biallelic), difference in power or a combination of these. Our study highlights that the genetic architecture even of highly heritable traits is difficult to characterize in wild populations.

Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits.

Multiple methods have been developed to estimate narrow-sense heritability, h2, using single nucleotide polymorphisms (SNPs) in unrelated individuals. However, a comprehensive evaluation of these methods has not yet been performed, leading to confusion and discrepancy in the literature. We present the most thorough and realistic comparison of these methods to date. We utilized thousands of real whole genome sequences to simulate phenotypes under varying genetic architectures and confounding variables, and used array, imputed, or whole genome sequence SNPs to obtain “SNP-heritability” estimates (ĥ2SNP). We show that ĥ2SNP can be highly sensitive to assumptions about the frequencies, effect sizes, and levels of linkage disequilibrium (LD) of underlying causal variants, but that methods that bin SNPs according to minor allele frequency and LD are less sensitive to these assumptions across a wide range of genetic architectures and possible confounding factors. These findings provide guidance for best practices and proper interpretation of published estimates.

Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat.

To evaluate the potential of genomic selection (GS), a selection experiment with GS and phenotypic selection (PS) was performed in an allogamous crop, common buckwheat (Fagopyrum esculentum Moench). To indirectly select for seed yield per unit area, which cannot be measured on a single-plant basis, a selection index was constructed from seven agro-morphological traits measurable on a single plant basis. Over 3 years, we performed two GS and one PS cycles per year for improvement in the selection index. In GS, a prediction model was updated every year on the basis of genotypes of 14,598–50,000 markers and phenotypes. Plants grown from seeds derived from a series of generations of GS and PS populations were evaluated for the traits in the selection index and other yield-related traits. GS resulted in a 20.9% increase and PS in a 15.0% increase in the selection index in comparison with the initial population. Although the level of linkage disequilibrium in the breeding population was low, the target trait was improved with GS. Traits with higher weights in the selection index were improved more than those with lower weights, especially when prediction accuracy was high. No trait changed in an unintended direction in either GS or PS. The accuracy of genomic prediction models built in the first cycle decreased in the later cycles because the genetic bottleneck through the selection cycles changed linkage disequilibrium patterns in the breeding population. The present study emphasizes the importance of updating models in GS and demonstrates the potential of GS in mass selection of allogamous crop species, and provided a pilot example of successful application of GS to plant breeding.

Uneven recombination rate and linkage disequilibrium across a reference SNP map for common bean (Phaseolus vulgaris L.).

Recombination (R) rate and linkage disequilibrium (LD) analyses are the basis for plant breeding. These vary by breeding system, by generation of inbreeding or outcrossing and by region in the chromosome. Common bean (Phaseolus vulgaris L.) is a favored food legume with a small sequenced genome (514 Mb) and n = 11 chromosomes. The goal of this study was to describe R and LD in the common bean genome using a 768-marker array of single nucleotide polymorphisms (SNP) based on Trans-legume Orthologous Group (TOG) genes along with an advanced-generation Recombinant Inbred Line reference mapping population (BAT93 x Jalo EEP558) and an internationally available diversity panel. A whole genome genetic map was created that covered all eleven linkage groups (LG). The LGs were linked to the physical map by sequence data of the TOGs compared to each chromosome sequence of common bean. The genetic map length in total was smaller than for previous maps reflecting the precision of allele calling and mapping with SNP technology as well as the use of gene-based markers. A total of 91.4% of TOG markers had singleton hits with annotated Pv genes and all mapped outside of regions of resistance gene clusters. LD levels were found to be stronger within the Mesoamerican genepool and decay more rapidly within the Andean genepool. The recombination rate across the genome was 2.13 cM / Mb but R was found to be highly repressed around centromeres and frequent outside peri-centromeric regions. These results have important implications for association and genetic mapping or crop improvement in common bean.

Identification of SSR and retrotransposon-based molecular markers linked to morphological characters in oily sunflower ( $$\textit{Helianthus annuus}$$ Helianthus annuus L.) under natural and water-limited states

Sunflower is an important source of edible oil. Drought is known as an important factor limiting the growth and productivity of field crops in most parts of the world. Agricultural biotechnology mainly aims at developing crops with higher tolerance to the challenging environmental conditions, such as drought. This study examined a number of morphological characters, along with relative water content (RWC) in 100 inbred sunflower lines. A 10 × 10 simple lattice design with two replications was employed to measure the mentioned parameters under natural and water-limited states during two successive years. In molecular trial, 30 simple sequence repeat (SSR) primer pairs, as well as 14 inter-retrotransposon amplified polymorphism (IRAP) and 14 retrotransposon-microsatellite amplified polymorphism (REMAP) primer combinations were used for DNA fingerprinting of the lines. Most of the examined characters had lower average values under water-limited than natural states. Maximum and minimum reductions were observed in the cases of yield and oil percentage, respectively. The broad-sense heritabilities for all the examined characters were 0.20-0.73 and 0.10-0.34 under natural and water-limited states, respectively. In the studied samples, 8.97% of the 435 possible locus pairs of the SSRs represented significant linkage disequilibrium (LD) levels. In the association analysis using SSR markers, 22 and 21 markers were identified (P ≤ 0.05) for the studied characters under natural and water-limited states, respectively. The corresponding values were 50 and 37 using retrotransposon-based molecular markers. Some detected markers were communal between the characters under water-limited and natural states. This was in line with the phenotypic correlations detected between the characters. Communal markers facilitate the simultaneous selection of several characters and can thus improve the efficacy of selection based on markers in the plant-breeding activities.

Genomic islands of differentiation in two songbird species reveal candidate genes for hybrid female sterility.

Hybrid sterility is a common first step in the evolution of postzygotic reproductive isolation. According to Haldane's Rule, it affects predominantly the heterogametic sex. While the genetic basis of hybrid male sterility in organisms with heterogametic males has been studied for decades, the genetic basis of hybrid female sterility in organisms with heterogametic females has received much less attention. We investigated the genetic basis of reproductive isolation in two closely related avian species, the common nightingale (Luscinia megarhynchos) and the thrush nightingale (L.luscinia), that hybridize in a secondary contact zone and produce viable hybrid progeny. In accordance with Haldane's Rule, hybrid females are sterile, while hybrid males are fertile, allowing gene flow to occur between the species. Using transcriptomic data from multiple individuals of both nightingale species, we identified genomic islands of high differentiation (FST ) and of high divergence (Dxy ), and we analysed gene content and patterns of molecular evolution within these islands. Interestingly, we found that these islands were enriched for genes related to female meiosis and metabolism. The islands of high differentiation and divergence were also characterized by higher levels of linkage disequilibrium than the rest of the genome in both species indicating that they might be situated in genomic regions of low recombination. This study provides one of the first insights into genetic basis of hybrid female sterility in organisms with heterogametic females.

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50–80% of the phenotypic variation. For personality traits these effects are smaller, with 34–48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0 and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ~20,000 individuals in the Generation Scotland family cohort genotyped for ~700,000 single-nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWAS of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence, and education is consistent with mutation-selection balance.

Linkage disequilibrium levels and allele frequency distribution in Blanco Orejinegro and Romosinuano Creole cattle using medium density SNP chip data.

The linkage disequilibrium (LD) between molecular markers affects the accuracy of genome-wide association studies and genomic selection application. High-density genotyping platforms allow identifying the genotype of thousands of single nucleotide polymorphisms (SNPs) distributed throughout the animal genomes, which increases the resolution of LD evaluations. This study evaluated the distribution of minor allele frequencies (MAF) and the level of LD in the Colombian Creole cattle breeds Blanco Orejinegro (BON) and Romosinuano (ROMO) using a medium density SNP panel (BovineSNP50K_v2). The LD decay in these breeds was lower than those reported for other taurine breeds, achieving optimal LD values (r2 ≥ 0.3) up to a distance of 70 kb in BON and 100 kb in ROMO, which is possibly associated with the conservation status of these cattle populations and their effective population size. The average MAF for both breeds was 0.27 ± 0.14 with a higher SNP proportion having high MAF values (≥ 0.3). The LD levels and distribution of allele frequencies found in this study suggest that it is possible to have adequate coverage throughout the genome of these breeds using the BovineSNP50K_v2, capturing the effect of most QTL related with productive traits, and ensuring an adequate prediction capacity in genomic analysis.

Use of Observed Genomic Information to Infer Linkage Disequilibrium between Markers and QTLs

Conducting genomic selection in admixed populations is challenging and its accuracy in this case largely depends on the persistence of linkage disequilibrium between single nucleotide polymorphisms (SNP) and quantitative trait loci (QTL). Inferring linkage disequilibrium (LD) between SNP markers and QTLs could be important in understanding the change of SNP marker effects across different breeds. Predicting the change in linkage disequilibrium between markers and QTLs across two divergent breeds was explored using information from the genotype data. Two different models (M1, M2) that differ in the definition of the explanatory variables were used to infer the level of LD between SNP markers and QTLs using all markers in the panel or windows of fixed number of markers. Three simulation scenarios were conducted using different number of SNPs and QTLs. In the first scenario, the resulting coefficient of determination (R2) was 0.65 and 0.52 using M1 and M2, respectively. In the second scenario, average R2 equaled 0.12 using all markers in the panel and 0.25 using 100 marker windows. Across the three simulation scenarios, it was clear that a significant portion of the variation in the change in LD between SNP markers and QTLs could be explained by information already available in the observed SNP marker data.

Heritabilities and genetic correlations in the same traits across different strata of herds created according to continuous genomic, genetic, and phenotypic descriptors

The most common approach in dairy cattle to prove genotype by environment interactions is a multiple-trait model application, and considering the same traits in different environments as different traits. We enhanced such concepts by defining continuous phenotypic, genetic, and genomic herd descriptors, and applying random regression sire models. Traits of interest were test-day traits for milk yield, fat percentage, protein percentage, and somatic cell score, considering 267,393 records from 32,707 first-lactation Holstein cows. Cows were born in the years 2010 to 2013, and kept in 52 large-scale herds from 2 federal states of north-east Germany. The average number of genotyped cows per herd (45,613 single nucleotide polymorphism markers per cow) was 133.5 (range: 45 to 415 genotyped cows). Genomic herd descriptors were (1) the level of linkage disequilibrium (r2) within specific chromosome segments, and (2) the average allele frequency for single nucleotide polymorphisms in close distance to a functional mutation. Genetic herd descriptors were the (1) intra-herd inbreeding coefficient, and (2) the percentage of daughters from foreign sires. Phenotypic herd descriptors were (1) herd size, and (2) the herd mean for nonreturn rate. Most correlations among herd descriptors were close to 0, indicating independence of genomic, genetic, and phenotypic characteristics. Heritabilities for milk yield increased with increasing intra-herd linkage disequilibrium, inbreeding, and herd size. Genetic correlations in same traits between adjacent levels of herd descriptors were close to 1, but declined for descriptor levels in greater distance. Genetic correlation declines were more obvious for somatic cell score, compared with test-day traits with larger heritabilities (fat percentage and protein percentage). Also, for milk yield, alterations of herd descriptor levels had an obvious effect on heritabilities and genetic correlations. By trend, multiple trait model results (based on created discrete herd classes) confirmed the random regression estimates. Identified alterations of breeding values in dependency of herd descriptors suggest utilization of specific sires for specific herd structures, offering new possibilities to improve sire selection strategies. Regarding genomic selection designs and genetic gain transfer into commercial herds, cow herds for the utilization in cow training sets should reflect the genomic, genetic, and phenotypic pattern of the broad population.

Establishment and evaluation of a peanut association panel and analysis of key nutritional traits.

Breeding programs aim to improve the yield and quality of peanut (Arachis hypogaea L.); using association mapping to identify genetic markers linked to these quantitative traits could facilitate selection efficiency. A peanut association panel was established consisting of 268 lines with extensive phenotypic and genetic variation, meeting the requirements for association analysis. These lines were grown over 3 years and the key agronomic traits, including protein and oil content were examined. Population structure (Q) analysis showed two subpopulations and clustering analysis was consistent with Q-based membership assignment and closely related to botanical type. Relative Kinship (K) indicated that most of the panel members have no or weak familial relatedness, with 52.78% of lines showing K = 0. Linkage disequilibrium (LD) analysis showed a high level of LD occurs in the panel. Model comparisons indicated false positives can be effectively controlled by taking Q and K into consideration and more false positives were generated by K than Q. A preliminary association analysis using a Q + K model found markers significantly associated with oil, protein, oleic acid, and linoleic acid, and identified a set of alleles with positive and negative effects. These results show that this panel is suitable for association analysis, providing a resource for marker-assisted selection for peanut improvement.

Abstract A34: The genetic etiology of esophageal cancer in South African Black populations

Abstract Esophageal squamous cell carcinoma (ESCC) is common in Black populations of the sub-Saharan Africa, with high incidence regions in East and South Africa. Clinical presentation in Africa is late, and treatment is mainly palliative with a very poor prognosis. Various environmental risk factors have been identified, but the possible contribution of genetics to disease risk is an important question, which is unresolved. The aim of this project is to test the hypothesis that genetic variation in the South African Black population contributes significantly to the risk of ESCC. We have begun by testing genetic risk factors for ESCC which were identified by genome-wide association scans in other populations for association with ESCC in the South African Black population. Cases with a histologically confirmed diagnosis of ESCC and population controls were recruited, with informed consent and institutional ethical approval. All individuals were from the Black population of the Western Cape, with 98.2% being from the Xhosa ethnic group. A total of 513 ESCC cases and 820 controls were genotyped for 15 single nucleotide polymorphisms (SNPs) by validated TaqMan assays on a 7900HT (Applied Biosystems). SNPs were tested for association using an allelic chi-square test; odds ratios (OR) and 95% confidence intervals (CI) used the common allele as reference. A significance threshold of p=0.0033 was applied, from a Bonferroni multiple testing correction for 15 SNPs. No significant evidence of association was observed for the index SNPs at the previously reported loci ATP1B2, CASP8, c20orf54, HEATR3, PDE4D, PLCE1, PTPN2, RUNX1, SMG6, ST6GAL1, TMEM173 and UNC5L loci. However, the SNP rs2239815 at XBP1 on chromosome 22q12 was associated with ESCC (OR =1.41, P = 0.00087), as were 2 SNPs in CHEK2, rs4822983 (OR = 1.31, P = 0.0013) and rs1033667 (OR = 1.29, P = 0.0025). These 3 SNPs are within 70kb of each other and are in partial linkage disequilibrium in African populations. Fine-mapping of this locus will be required to determine the causal gene and variant/s driving this association. The lack of association with ESCC in the Black South African population at most of the loci previously associated in Asian GWAS suggests that at least some of these may not contribute to susceptibility to ESCC in the South African population. However, expansion of samples sizes and thus statistical power is required for more definitive exclusions, and the lower level of linkage disequilibrium (LD) across African genomes may produce a negative result if the SNP which is tagging the causal variant in Asian or European populations is in weak LD with the causal SNP in African populations. In order to address these issues we are now expanding the power and density of this study by genotyping 36 SNPs from loci of interest in 1000 South African ESCC cases and 940 controls from the Johannesburg Cancer Study by mass spectrometry using the MassArray genotyping system (Agena Bioscience). Citation Format: Wenlong Carl Chen, Hannah Bye, Marco Matejcic, Robyn Kerr, Elvira Singh, Natalie J. Prescott, Cathryn M. Lewis, Chantal Babb de Villers, Iqbal Parker, Christopher G. Mathew. The genetic etiology of esophageal cancer in South African Black populations [abstract]. In: Proceedings of the AACR International Conference: New Frontiers in Cancer Research; 2017 Jan 18-22; Cape Town, South Africa. Philadelphia (PA): AACR; Cancer Res 2017;77(22 Suppl):Abstract nr A34.

Population genomics of the eastern cottonwood (Populus deltoides)

Despite its economic importance as a bioenergy crop and key role in riparian ecosystems, little is known about genetic diversity and adaptation of the eastern cottonwood (Populus deltoides). Here, we report the first population genomics study for this species, conducted on a sample of 425 unrelated individuals collected in 13 states of the southeastern United States. The trees were genotyped by targeted resequencing of 18,153 genes and 23,835 intergenic regions, followed by the identification of single nucleotide polymorphisms (SNPs). This natural P. deltoides population showed low levels of subpopulation differentiation (F ST = 0.022–0.106), high genetic diversity (θW = 0.00100, π = 0.00170), a large effective population size (N e ≈ 32,900), and low to moderate levels of linkage disequilibrium. Additionally, genomewide scans for selection (Tajima's D), subpopulation differentiation (XTX), and environmental association analyses with eleven climate variables carried out with two different methods (LFMM and BAYENV2) identified genes putatively involved in local adaptation. Interestingly, many of these genes were also identified as adaptation candidates in another poplar species, Populus trichocarpa, indicating possible convergent evolution. This study constitutes the first assessment of genetic diversity and local adaptation in P. deltoides throughout the southern part of its range, information we expect to be of use to guide management and breeding strategies for this species in future, especially in the face of climate change.

Unlocking new alleles for leaf rust resistance in the Vavilov wheat collection.

Thirteen potentially new leaf rust resistance loci were identified in a Vavilov wheat diversity panel. We demonstrated the potential of allele stacking to strengthen resistance against this important pathogen. Leaf rust (LR) caused by Puccinia triticina is an important disease of wheat (Triticum aestivum L.), and the deployment of genetically resistant cultivars is the most viable strategy to minimise yield losses. In this study, we evaluated a diversity panel of 295 bread wheat accessions from the N. I. Vavilov Institute of Plant Genetic Resources (St Petersburg, Russia) for LR resistance and performed genome-wide association studies (GWAS) using 10,748 polymorphic DArT-seq markers. The diversity panel was evaluated at seedling and adult plant growth stages using three P. triticina pathotypes prevalent in Australia. GWAS was applied to 11 phenotypic data sets which identified a total of 52 significant marker-trait associations representing 31 quantitative trait loci (QTL). Among them, 29 QTL were associated with adult plant resistance (APR). Of the 31 QTL, 13 were considered potentially new loci, whereas 4 co-located with previously catalogued Lr genes and 14 aligned to regions reported in other GWAS and genomic prediction studies. One seedling LR resistance QTL located on chromosome 3A showed pronounced levels of linkage disequilibrium among markers (r 2=0.7), suggested a high allelic fixation. Subsequent haplotype analysis for this region found seven haplotype variants, of which two were strongly associated with LR resistance at seedling stage. Similarly, analysis of an APR QTL on chromosome 7B revealed 22 variants, of which 4 were associated with resistance atthe adult plant stage. Furthermore, most of the tested lines in the diversity panel carried 10 or more combined resistance-associated marker alleles, highlighting the potential of allele stacking for long-lasting resistance.

Comparative analysis of core collection sampling methods for mandarin germplasm based on molecular and phenotypic data

AbstractGene banks have been established to conserve the genetic diversity of crop species. Large germplasm collections lead to management problems (space, maintenance costs, etc.), especially in collections involving species with recalcitrant seeds that must be maintained as growing plants. Core collections (CCs) are thus developed to reduce the size of large germplasm collections while keeping the maximum variability. This also facilitates fine phenotypic evaluation. In this study, several software packages (DARwin, PowerMarker and MSTRAT) and methods (Max length subtree, M strategy, simulated annealing and MinSD) were compared to define a mandarin (Citrus reticulata) CC. One hundred and sixty‐seven accessions were sampled from two germplasm collections, which were genotyped with 50 SSR, 24 InDel and 68 single nucleotide polymorphism markers. All the CC obtained were tested for the maintenance of the genetic variability parameters (Ho and He) of the initial collection, the level of linkage disequilibrium (LD) and the phenotypic diversity retention. The Max length subtree function from DARWin seemed to be the most appropriate method for establishing a CC in C. reticulata. It maintained 96.82% of the allelic richness and 17.96% of the size of the initial collection with only 30 accessions. Besides it did not increase the LD (r2 value) of the initial collection and retained the vast majority of the phenotypic variability. However, a CC with 70 accessions would be more helpful for genetic association studies.

A heuristic method to identify runs of homozygosity associated with reduced performance in livestock.

Although, for the most part, genome-wide metrics are currently used in managing livestock inbreeding, genomic data offer, in principle, the ability to identify functional inbreeding. Here, we present a heuristic method to identify haplotypes contained within a run of homozygosity (ROH) associated with reduced performance. Results are presented for simulated and swine data. The algorithm comprises 3 steps. Step 1 scans the genome based on marker windows of decreasing size and identifies ROH genotypes associated with an unfavorable phenotype. Within this stage, multiple aggregation steps reduce the haplotype to the smallest possible length. In step 2, the resulting regions are formally tested for significance with the use of a linear mixed model. Lastly, step 3 removes nested windows. The effect of the unfavorable haplotypes identified and their associated haplotype probabilities for a progeny of a given mating pair or an individual can be used to generate an inbreeding load matrix (ILM). Diagonals of ILM characterize the functional individual inbreeding load (IIL). We estimated the accuracy of predicting the phenotype based on IIL. We further compared the significance of the regression coefficient for IIL on phenotypes with genome-wide inbreeding metrics. We tested the algorithm using simulated scenarios (12 scenarios), combining different levels of linkage disequilibrium (LD) and number of loci impacting a quantitative trait. Additionally, we investigated 9 traits from 2 maternal purebred swine lines. In simulated data, as the LD in the population increased, the algorithm identified a greater proportion of the true unfavorable ROH effects. For example, the proportion of highly unfavorable true ROH effects identified rose from 32 to 41% for the low- to the high-LD scenario. In both simulated and real data, the haplotypes identified were contained within a much larger ROH (9.12-12.1 Mb). The IIL prediction accuracy was greater than 0 across all scenarios for simulated data (mean of 0.49 [95% confidence interval 0.47-0.52] for the high-LD scenario) and for nearly all swine traits (mean of 0.17 [SD 0.10]). On average, across simulated and swine data sets, the IIL regression coefficient was more closely related to progeny performance than any genome-wide inbreeding metric. A heuristic method was developed that identified ROH genotypes with reduced performance and characterized the combined effects of ROH genotypes within and across individuals.