Linkage Disequilibrium Estimates Research Articles

TOP-LD: A tool to explore linkage disequilibrium with TOPMed whole-genome sequence data

SummaryCurrent publicly available tools that allow rapid exploration of linkage disequilibrium (LD) between markers (e.g., HaploReg and LDlink) are based on whole-genome sequence (WGS) data from 2,504 individuals in the 1000 Genomes Project. Here, we present TOP-LD, an online tool to explore LD inferred with high-coverage (∼30×) WGS data from 15,578 individuals in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. TOP-LD provides a significant upgrade compared to current LD tools, as the TOPMed WGS data provide a more comprehensive representation of genetic variation than the 1000 Genomes data, particularly for rare variants and in the specific populations that we analyzed. For example, TOP-LD encompasses LD information for 150.3, 62.2, and 36.7 million variants for European, African, and East Asian ancestral samples, respectively, offering 2.6- to 9.1-fold increase in variant coverage compared to HaploReg 4.0 or LDlink. In addition, TOP-LD includes tens of thousands of structural variants (SVs). We demonstrate the value of TOP-LD in fine-mapping at the GGT1 locus associated with gamma glutamyltransferase in the African ancestry participants in UK Biobank. Beyond fine-mapping, TOP-LD can facilitate a wide range of applications that are based on summary statistics and estimates of LD. TOP-LD is freely available online.

Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires.

Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k single nucleotide polymorphism (SNP) panels. After imputation and quality control, 61,666 SNPs were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent single nucleotide polymorphisms (SNPs) across all chromosomes were 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help us to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

Limitations of lymphoblastoid cell lines for establishing genetic reference datasets in the immunoglobulin loci.

Lymphoblastoid cell lines (LCLs) have been critical to establishing genetic resources for biomedical science. They have been used extensively to study human genetic diversity, genome function, and inform the development of tools and methodologies for augmenting disease genetics research. While the validity of variant callsets from LCLs has been demonstrated for most of the genome, previous work has shown that DNA extracted from LCLs is modified by V(D)J recombination within the immunoglobulin (IG) loci, regions that harbor antibody genes critical to immune system function. However, the impacts of V(D)J on short read sequencing data generated from LCLs has not been extensively investigated. In this study, we used LCL-derived short read sequencing data from the 1000 Genomes Project (n = 2,504) to identify signatures of V(D)J recombination. Our analyses revealed sample-level impacts of V(D)J recombination that varied depending on the degree of inferred monoclonality. We showed that V(D)J associated somatic deletions impacted genotyping accuracy, leading to adulterated population-level estimates of allele frequency and linkage disequilibrium. These findings illuminate limitations of using LCLs and short read data for building genetic resources in the IG loci, with implications for interpreting previous disease association studies in these regions.

Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors

Summary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data or LD reference or heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate in detecting secondary signals in the summary-data-based conditional and joint association analysis, especially for imputed rare variants (false-positive rate reduced from >28% to <2% in the presence of heterogeneity between GWAS and LD reference). We further show that DENTIST can improve other summary-data-based analyses such as fine-mapping analysis.

Genome-wide association of single nucleotide polymorphism loci and candidate genes for frogeye leaf spot (Cercospora sojina) resistance in soybean

BackgroundFrogeye leaf spot (FLS) is a destructive fungal disease that affects soybean production. The most economical and effective strategy to control FLS is the use of resistant cultivars. However, the use of a limited number of resistant loci in FLS management will be countered by the emergence of new high-virulence Cercospora sojina races. Therefore, we identified quantitative trait loci (QTL) that control resistance to FLS and identified novel resistant genes using a genome-wide association study (GWAS) on 234 Chinese soybean cultivars.ResultsA total of 30,890 single nucleotide polymorphism (SNP) markers were used to estimate linkage disequilibrium (LD) and population structure. The GWAS results showed four loci (p < 0.0001) distributed over chromosomes (Chr.) 5 and 20, that are significantly associated with FLS resistance. No previous studies have reported resistance loci in these regions. Subsequently, 45 genes in the two resistance-related haplotype blocks were annotated. Among them, Glyma20g31630 encoding pyruvate dehydrogenase (PDH), Glyma05g28980, which encodes mitogen-activated protein kinase 7 (MPK7), and Glyma20g31510, Glyma20g31520 encoding calcium-dependent protein kinase 4 (CDPK4) in the haplotype blocks deserves special attention.ConclusionsThis study showed that GWAS can be employed as an effective strategy for identifying disease resistance traits in soybean and narrowing SNPs and candidate genes. The prediction of candidate genes in the haplotype blocks identified by disease resistance loci can provide a useful reference to study systemic disease resistance.

The Population of Sclerotinia sclerotiorum in Brazil Is Structured by Mycelial Compatibility Groups.

The genetic structure of the population of Sclerotinia sclerotiorum was analyzed using 238 individuals collected from different hosts. Individuals were characterized for microsatellite genotypes and mycelial compatibility groups (MCGs). A total of 22 MCGs and 64 multilocus lineages (MLLs) were identified. There was a close relationship between the MCGs and MLLs, but there was no association between MLLs and hosts or regions. At least 39 MCGs are present in Brazil, and 68.5% of the isolates were assigned to either MCG 1 or MCG 2. Eight new MCGs were found. Seven genetic groups were identified and associated with MCGs. Most genetic variation (70.0%) was because of differences among MCGs. High values of estimates of linkage disequilibrium among loci were more frequent in the total population (all MCGs). By contrast, there was evidence of random mating in subpopulations defined by MCGs 1 and 2. Additionally, there was evidence of outcrossing in the population of S. sclerotiorum in Brazil. The population was structured by MCGs; lineages originating from asexual reproduction or selfing prevail and are widely distributed in space, are persistent in time, and affect many hosts, but there is evidence of some degree of outcrossing, which may lead to a more genetically variable population in the future.

Inflamed Mind: Multiple Genetic Variants of IL6 Influence Suicide Risk Phenotypes in Interaction With Early and Recent Adversities in a Linkage Disequilibrium-Based Clumping Analysis.

Background: Understanding and predicting suicide remains a challenge, and a recent paradigm shift regarding the complex relationship between the immune system and the brain brought attention to the involvement of inflammation in neuropsychiatric conditions including suicide. Among cytokines, IL-6 has been most frequently implicated in suicide, yet only a few candidate gene studies and without considering the effect of stress investigated the role of IL6 in suicidal behaviour. Our study aimed to investigate the association of IL6 variation with a linkage disequilibrium-based clumping method in interaction with childhood adversities and recent stress on manifestations along the suicide spectrum.Methods: One thousand seven hundred and sixty-two participants provided information on previous suicide attempts, current suicidal ideation, thoughts of death, and hopelessness, and were genotyped for 186 variants in IL6. Early childhood adversities were recorded with an instrument adapted from the Childhood Trauma Questionnaire, recent life events were registered using the List of Threatening Life Events. Following a 3-step quality control, logistic and linear regression models were run to explore the effect of genotype and gene-environment interactions on suicide phenotypes. All regression models were followed by a clumping process based on empirical estimates of linkage disequilibrium between clumps of intercorrelated SNPs. Interaction effects of distinct types of recent life events were also analysed.Results: No clumps with significant main effects emerged, but we identified several clumps significantly interacting with childhood adversities on lifetime suicide attempts, current suicidal ideation, and current thoughts of death. We also identified clumps significantly interacting with recent negative life events on current suicidal ideation. We reported no clumps with significant effect on hopelessness either as a main effect or in interaction with childhood adversities or recent stress.Conclusion: We identified variant clumps in IL6 influencing suicidal behaviour, but only in interaction with childhood or recent adversities. Our results may bring us a step further in understanding the role of neuroinflammation and specifically of IL-6 in suicide, towards identifying novel biological markers of suicidal behaviour especially in those exposed to stressful experiences, and to fostering the adaptation of a new paradigm and identifying novel approaches and targets in the treatment of suicidal behaviour.

A beginner's guide to low-coverage whole genome sequencing for population genomics.

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and nonmodel species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analysed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency, genetic diversity, and linkage disequilibrium estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in nonmodel species, and discuss current limitations and future perspectives for lcWGS-based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption.

Scalable bias-corrected linkage disequilibrium estimation under genotype uncertainty.

Linkage disequilibrium (LD) estimates are often calculated genome-wide for use in many tasks, such as SNP pruning and LD decay estimation. However, in the presence of genotype uncertainty, naive approaches to calculating LD have extreme attenuation biases, incorrectly suggesting that SNPs are less dependent than in reality. These biases are particularly strong in polyploid organisms, which often exhibit greater levels of genotype uncertainty than diploids. A principled approach using maximum likelihood estimation with genotype likelihoods can reduce this bias, but is prohibitively slow for genome-wide applications. Here, we present scalable moment-based adjustments to LD estimates based on the marginal posterior distributions of the genotypes. We demonstrate, on both simulated and real data, that these moment-based estimators are as accurate as maximum likelihood estimators, but are almost as fast as naive approaches based only on posterior mean genotypes. This opens up bias-corrected LD estimation to genome-wide applications. In addition, we provide standard errors for these moment-based estimators. All methods discussed in this manuscript are implemented in the ldsep package, available on the Comprehensive R Archive Network ( https://cran.r-project.org/package=ldsep ).

Genetic Diversity, Linkage Disequilibrium and Population Structure of Bulgarian Bread Wheat Assessed by Genome-Wide Distributed SNP Markers: From Old Germplasm to Semi-Dwarf Cultivars.

Genetic diversity and population structure are key resources for breeding purposes and genetic studies of important agronomic traits in crops. In this study, we described SNP-based genetic diversity, linkage disequilibrium and population structure in a panel of 179 bread wheat advanced cultivars and old accessions from Bulgaria, using an optimized wheat 25K Infinium iSelect array. Out of 19,019 polymorphic SNPs, 17,968 had a known chromosome position on the A (41%), B (42%) and D (11%) genome, and 6% were not assigned to any chromosome. Homoeologous group 4, in particular chromosome 4D, was the least polymorphic. In the total population, the Nei’s gene diversity was within the range 0.1–0.5, and the polymorphism information content ranged from 0.1 to 0.4. Significant differences between the old and modern collections were revealed with respect to the linkage disequilibrium (LD): the average values for LD (r2), the percentage of the locus pairs in LD and the LD decay were 0.64, 16% and 3.3 for the old germplasm, and 0.43, 30% and 4.1 for the modern releases, respectively. Structure and k-means clustering algorithm divided the panel into three groups. The old accessions formed a distinct subpopulation. The cluster analysis further distinguished the modern releases according to the geographic region and genealogy. Gene exchange was evidenced mainly between the subpopulations of contemporary cultivars. The achieved understanding of the genetic diversity and structure of the Bulgarian wheat population and distinctiveness of the old germplasm could be of interest for breeders developing cultivars with improved characteristics. The obtained knowledge about SNP informativeness and the LD estimation are worthwhile for selecting markers and for considering the composition of a population in association mapping studies of traits of interest.

Evaluation of polygenic prediction methodology within a reference-standardized framework.

The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.

Identification of Bacterial Blight Resistance Loci in Rice (Oryza sativa L.) against Diverse Xoo Thai Strains by Genome-Wide Association Study

Bacterial leaf blight (BLB) is a serious disease affecting global rice agriculture caused by Xanthomonas oryzae pv. oryzae (Xoo). Most resistant rice lines are dependent on single genes that are vulnerable to resistance breakdown caused by pathogen mutation. Here we describe a genome-wide association study of 222 predominantly Thai rice accessions assayed by phenotypic screening against 20 Xoo isolates. Loci corresponding to BLB resistance were detected using >142,000 SNPs. We identified 147 genes according to employed significance thresholds across chromosomes 1–6, 8, 9 and 11. Moreover, 127 of identified genes are located on chromosomal regions outside estimated Linkage Disequilibrium influences of known resistance genes, potentially indicating novel BLB resistance markers. However, significantly associated SNPs only occurred across a maximum of six Xoo isolates indicating that the development of broad-spectrum Xoo strain varieties may prove challenging. Analyses indicated a range of gene functions likely underpinning BLB resistance. In accordance with previous studies of accession panels focusing on indica varieties, our germplasm displays large numbers of SNPs associated with resistance. Despite encouraging data suggesting that many loci contribute to resistance, our findings corroborate previous inferences that multi-strain resistant varieties may not be easily realised in breeding programs without resorting to multi-locus strategies.

Pairwise linkage disequilibrium estimation for polyploids.

Many tasks in statistical genetics involve pairwise estimation of linkage disequilibrium (LD). The study of LD in diploids is mature. However, in polyploids, the field lacks a comprehensive characterization of LD. Polyploids also exhibit greater levels of genotype uncertainty than diploids, yet no methods currently exist to estimate LD in polyploids in the presence of such genotype uncertainty. Furthermore, most LD estimation methods do not quantify the level of uncertainty in their LD estimates. Our study contains three major contributions. (i) We characterize haplotypic and composite measures of LD in polyploids. These composite measures of LD turn out to be functions of common statistical measures of association. (ii) We derive procedures to estimate haplotypic and composite LD in polyploids in the presence of genotype uncertainty. We do this by estimating LD directly from genotype likelihoods, which may be obtained from many genotyping platforms. (iii) We derive standard errors of all LD estimators that we discuss. We validate our methods on both real and simulated data. Our methods are implemented in the R package ldsep, available on the Comprehensive R Archive Network https://cran.r-project.org/package=ldsep.

Identification of Single Nucleotide Polymorphisms and Analysis of Linkage Disequilibrium in Different Bamboo Species Using the Candidate Gene Approach

Bamboos are one of the most beautiful and useful plants on Earth. The genetic background and population structure of bamboos are well known, which helps accelerate the process of artificial domestication of bamboo. Partial sequences of six genes involved in nitrogen use efficiency in 32 different bamboo species were analyzed for occurrence of single nucleotide polymorphisms (SNPs). The nucleotide diversity θw and total nucleotide polymorphisms πT of the sequenced DNA regions was 0.05137 and 0.03332, respectively. Both πnonsyn /πsyn and Ka/Ks values were 0.1. Estimation and analysis of linkage disequilibrium of five genes revealed that with the increase in nucleotide sequence length, the degree of SNP linkage disequilibrium decreased rapidly. We inferred the population genetic structure of 32 bamboo species based on the SNP loci of six genes with frequencies >18%. 32 bamboo species were divided into five categories, which indicated that the combined population of all bamboo species had obvious multivariate characteristics and was heterogeneous; red (Group 1) and green (Group 2) were the main groups.

Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations.

It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (n}{}{boldsymbol{h}}_{boldsymbol{g}}^{mathbf{2}}) and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates n}{}{boldsymbol{h}}_{boldsymbol{g}}^{mathbf{2}} by 10–60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate n}{}{boldsymbol{h}}_{boldsymbol{g}}^{mathbf{2}} and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of n}{}{boldsymbol{h}}_{boldsymbol{g}}^{mathbf{2}} and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of n}{}{boldsymbol{h}}_{boldsymbol{g}}^{mathbf{2}}. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size n}{} tau * is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.

Genome-Wide Association Studies Detect Multiple QTLs for Productivity in Mesoamerican Diversity Panel of Common Bean Under Drought Stress.

Drought stress is an important abiotic factor limiting common bean yield, with great impact on the production worldwide. Understanding the genetic basis regulating beans’ yield and seed weight (SW) is a fundamental prerequisite for the development of superior cultivars. The main objectives of this work were to conduct genome-wide marker discovery by genotyping a Mesoamerican panel of common bean germplasm, containing cultivated and landrace accessions of broad origin, followed by the identification of genomic regions associated with productivity under two water regimes using different genome-wide association study (GWAS) approaches. A total of 11,870 markers were genotyped for the 339 genotypes, of which 3,213 were SilicoDArT and 8,657 SNPs derived from DArT and CaptureSeq. The estimated linkage disequilibrium extension, corrected for structure and relatedness (r2sv), was 98.63 and 124.18 kb for landraces and breeding lines, respectively. Germplasm was structured into landraces and lines/cultivars. We carried out GWASs for 100-SW and yield in field environments with and without water stress for 3 consecutive years, using single-, segment-, and gene-based models. Higher number of associations at high stringency was identified for the SW trait under irrigation, totaling ∼185 QTLs for both single- and segment-based, whereas gene-based GWASs showed ∼220 genomic regions containing ∼650 genes. For SW under drought, 18 QTLs were identified for single- and segment-based and 35 genes by gene-based GWASs. For yield, under irrigation, 25 associations were identified, whereas under drought the total was 10 using both approaches. In addition to the consistent associations detected across experiments, these GWAS approaches provided important complementary QTL information (∼221 QTLs; 650 genes; r2 from 0.01% to 32%). Several QTLs were mined within or near candidate genes playing significant role in productivity, providing better understanding of the genetic mechanisms underlying these traits and making available molecular tools to be used in marker-assisted breeding. The findings also allowed the identification of genetic material (germplasm) with better yield performance under drought, promising to a common bean breeding program. Finally, the availability of this highly diverse Mesoamerican panel is of great scientific value for the analysis of any relevant traits in common bean.

Evaluation of runs of homozygosity and genomic inbreeding in Holstein cattle from Colombia

Traditional selection programs for dairy cattle, based on quantitative principles, have worked well and allowed strong selection processes in the world over many decades. The objectives of this work were to estimate linkage disequilibrium (LD) levels at varying SNPs densities, to evaluate the effective population size of Holstein cattle, to characterize runs of homozygosity (ROH) distribution through Holstein cattle from Nariño and, to estimate and compare inbreeding coefficient (F) based on genomic markers information, runs of homozygosity (FROH), genomic relationship matrix (FGRM), and excess of homozygous (FSNP). After quality control, the dataset used was composed of 606 Holstein animals and 22200 SNP markers. PLINK program was used to identify LD, Ne, ROH segment and FROH and FSNP, FGRM was calculated with BLUPF90 family of programs. The average of r2 in all chromosomes was 0.011, the highest r2 was found in BTA3 (0.0323), and the lowest in BTA12 (0.0039). 533 ROH segments were identified in 319 animals; findings obtained in this study suggest that on average 0,28% of Holstein genome is autozygous. Total length of ROH was composed mostly of small segments (ROH1-4Mb and ROH4-8Mb). These segments accounted for approximately 96%, while larger ROH (ROH&gt;8Mb) were 3.37% of all ROH detected. Inbreeding averages FROH, FSNP and FGRM methodologies were 0.28%, 3.11% and 3.36% respectively. The Pearson’s correlation among these different F values was: 0.49 (FROH-FSNP), 0.25 (FROH-FGRM), 0.22 (FSNP-FGRM). The distribution of ROH shared regions identified on 19 autosome chromosomes, cover a relevant number of genes inside these ROH. Our result evidenced lowest LD extension levels compared with other Holstein populations; inbreeding results suggest that FGRM and FSNP may be useful estimators of individual autozygosity in Holstein from Colombia. Genes related with production and reproduction were found, but the most important are the two that may be related to adaptation to Colombian high tropics. This work is a pioneer and be the starting point for programs of genetic improvement and genomic population studies in the country and mainly in high tropic areas where the dairy breeds have an important production.

Genome-Wide Association Mapping Identifies Novel Loci for Quantitative Resistance to Blackleg Disease in Canola.

Blackleg disease, caused by the fungal pathogen Leptosphaeria maculans, continues to be a major concern for sustainable production of canola (Brassica napus L.) in many parts of the world. The deployment of effective quantitative resistance (QR) is recognized as a durable strategy in providing natural defense to pathogens. Herein, we uncover loci for resistance to blackleg in a genetically diverse panel of canola accessions by exploiting historic recombination events which occurred during domestication and selective breeding by genome-wide association analysis (GWAS). We found extensive variation in resistance to blackleg at the adult plant stage, including for upper canopy infection. Using the linkage disequilibrium and genetic relationship estimates from 12,414 high quality SNPs, GWAS identified 59 statistically significant and “suggestive” SNPs on 17 chromosomes of B. napus genome that underlie variation in resistance to blackleg, evaluated under field and shade-house conditions. Each of the SNP association accounted for up to 25.1% of additive genetic variance in resistance among diverse panel of accessions. To understand the homology of QR genomic regions with Arabidopsis thaliana genome, we searched the synteny between QR regions with 22 ancestral blocks of Brassicaceae. Comparative analyses revealed that 25 SNP associations for QR were localized in nine ancestral blocks, as a result of genomic rearrangements. We further showed that phenological traits such as flowering time, plant height, and maturity confound the genetic variation in resistance. Altogether, these findings provided new insights on the complex genetic control of the blackleg resistance and further expanded our understanding of its genetic architecture.

Analysis of polymorphic markers located in the HEXA gene region associated with Tay-Sachs disease

Tay-Sachs Disease (TSD) is a genetic disorder resulting from mutations in the HEXA gene. Linkage analysis of polymorphic markers such as single nucleotide polymorphism (SNP) has been used in heterozygous carrier detection and prenatal diagnosis of the disease in families with an affected individual. A large number of SNP markers have been introduced for the HEXA gene in the databases. In the present study, the genotype and informative degree of rs2288258 and rs4777507genetic markers of HEXA gene region were investigated. This study has been conducted using Tetra-primer ARMS PCR and usual ARMS PCR in 148 healthy unrelated control individuals and 10 families in the Iranian population. The allele frequency, degree of heterozygosity and Hardy-Weinberg equilibrium (HWE) was estimated using the GENEPOP program. Also, the estimation of haplotype frequency and linkage disequilibrium for the unrelated individuals was performed using the PowerMmarker software. Based on the obtained results, the observed degree of heterozygosity for rs2288258 and rs4777507 were 18.24 and 27.70, respectively. The allele frequency of rs2288258 was 90.88% for allele A and 9.12% for allele G. Also, the allele frequency of rs4777507 was 84.8% and 15.2% for A and G allele respectively. The results showed two markers, rs2288258-rs4777507, were in linkage disequilibrium together. Consequently, A/A, A/G and G/G informative haplotypes in population level and A/A, A/G and G/A informative haplotypes in family level were identified by using a combination of these two markers. These haplotypes can serve as appropriate tools for linkage analysis and identification of heterozygous carriers and prenatal diagnosis of TSD in the Iranian families with affected children.

Estimation of Linkage Disequilibrium and Effective Population Size in Three Italian Autochthonous Beef Breeds.

Simple SummaryLinkage disequilibrium (LD) of genomic markers is related to various evolutionary forces, such as inbreeding, nonrandom mating, population bottleneck, drift, recombination, and mutations, and hence is an essential parameter to examine population history. In this analysis, we examined the LD pattern of three Italian local beef breeds (Calvana, Mucca Pisana, and Pontremolese) facing the risk of extinction, using the commercial Limousin beef breed as a control. Our results provide important information on the population history and the current status of the breeds and they can be further used for conservation and breeding purposes.The objective was to investigate the pattern of linkage disequilibrium (LD) in three local beef breeds, namely, Calvana (n = 174), Mucca Pisana (n = 270), and Pontremolese (n = 44). As a control group, samples of the Italian Limousin breed (n = 100) were used. All cattle were genotyped with the GeneSeek GGP-LDv4 33k SNP chip containing 30,111 SNPs. The genotype quality control for each breed was conducted separately, and SNPs with call rate < 0.95 and minor allele frequency (MAF) > 1% were used for the analysis. LD extent was estimated in PLINK v1.9 using the squared correlation between pairs of loci (r2) across autosomes. Moreover, r2 values were used to calculate historical and contemporary effective population size (Ne) in each breed. Average r2 was similar in Calvana and Mucca Pisana (~0.14) and higher in Pontremolese (0.17); Limousin presented the lowest LD extent (0.07). LD up to 0.11–0.15 was persistent in the local breeds up to 0.75 Mbp, while in Limousin, it showed a more rapid decay. Variation of different LD levels across autosomes was observed in all the breeds. The results demonstrated a rapid decrease in Ne across generations for local breeds, and the contemporary population size observed in the local breeds, ranging from 41.7 in Calvana to 17 in Pontremolese, underlined the demographic alarming situation.