Density Single Nucleotide Polymorphism Research Articles

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries.

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

Evaluation of non-additive genetic effects on carcass and meat quality traits in Korean Hanwoo cattle using genomic models

The traditional genetic evaluation methods generally consider additive genetic effects only and often ignore non-additive (dominance and epistasis) effects that may have contributed to genetic variation of complex traits of livestock species. The available dense single nucleotide polymorphisms (SNPs) panels offer to investigate the potential benefits of including non-additive genetic effects in the genomic evaluation models. Data from 16 971 genotyped (Illumina Bovine 50 K SNP chip) Korean Hanwoo cattle were used to estimate genetic variance components and prediction accuracy of genomic breeding values (GEBVs) for four carcass and meat quality traits: carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS). Five different genetic models were evaluated through including additive, dominance and epistatic interactions (additive by additive, A × A; additive by dominance, A × D and dominance by dominance, D × D) successively in the models. The estimates of additive genetic variances and narrow sense heritabilities (ha2) were found similar across the evaluated models and traits except when additive interaction (A × A) was included. The dominance variance estimates relative to phenotypic variance ranged from 1.7–3.4% for CWT and MS traits, whereas, they were close to zero for EMA and BFT traits. The magnitude of A × A epistatic heritability (haa2) ranged between 14.8 and 27.7% in all traits. However, heritability estimates for A × D and D × D epistatic interactions (had2 and hdd2) were quite low compared to haa2 and were contributed only 0.0–9.7% of the total phenotypic variation. In general, broad sense heritability (hG2) estimates were almost twice (ranging between 0.54 and 0.68) the ha2 for all of the investigated traits. The inclusion of dominance effects did not improve the prediction accuracy of GEBV but improved 2.0–3.0% when epistatic effects were included in the model. More importantly, rank correlation revealed that partitioning of variance components considering dominance and epistatic effects in the model would enable to re-rank of top animals with better prediction of GEBV. The present result suggests that dominance and epistatic effects could be included in the genomic evaluation model for better estimates of variance components and more accurate prediction of GEBV for carcass and meat quality traits in Korean Hanwoo cattle.

Comprehensive analysis of runs of homozygosity and heterozygosity in Holstein cattle on the basis of medium and high density SNP panels and large population sample

Abstract This study reports runs of homozygosity (ROH) and heterozygosity (ROHet) distributed in a large population of Holstein cattle on the basis of two microarrays of medium (50k; 2163 animals; 54 609 SNPs) and high single nucleotide polymorphism (SNP) density (HD; 600 animals; 777 692 SNPs). To assess the inbreeding values of Holstein cattle, the ROH-based genomic inbreeding coefficient (FROH) was calculated. The comparison of SNP panels suggested that FROH values above 4 Mb should be considered for panels of medium densities as a relatively reliable measure of inbreeding. Moreover, ROH hotspots and coldspots were identified and compared between the HD and 50k SNP panels and were carefully examined for association with production and functional traits. The obtained results pinpointed genomic regions presumably under selection pressure in Holstein cattle. The regions overlapped with a large number of genes, including GHR, GBF1, SUMF1, CCL28, NIM1K, U6, BTRC and FABP1, many of which are involved in important Holstein cattle characteristics. We also found that some ROH hotspots and coldspots identified with the HD panel were not detected with the 50k panel, mainly because of insufficient SNP density in certain genomic regions. This suggests that using medium-density panels might not be the best choice when precise identification of ROH patterns is the main goal. In summary, in this work, we confirmed that a high-density SNP panel compared to a medium-density SNP panel allows for more precise identification of ROH patterns, especially in the case of short ROH that could be associated with ancestral inbreeding.

Development of a Gene-Based Soybean-Origin Discrimination Method Using Allele-Specific Polymerase Chain Reaction.

A low soybean self-sufficiency rate in South Korea has caused a high import dependence and considerable price variation between domestic and foreign soybeans, causing the false labeling of foreign soybeans as domestic. Conventional soybean origin discrimination methods prevent a single-grain analysis and rely on the presence or absence of several compounds or concentration differences. This limits the origin discrimination of mixed samples, demonstrating the need for a method that analyzes individual grains. Therefore, we developed a method for origin discrimination using genetic analysis. The whole-genome sequencing data of the Williams 82 reference cultivar and 15 soybean varieties cultivated in South Korea were analyzed to identify the dense variation blocks (dVBs) with a high single-nucleotide polymorphism density. The PCR primers were prepared and validated for the insertion-deletion (InDel) sequences of the dVBs to discriminate each soybean variety. Our method effectively discriminated domestic and foreign soybean varieties, eliminating their false labeling.

Identification and high-throughput genotyping of single nucleotide polymorphism markers in a non-model conifer (Abies nordmanniana (Steven) Spach)

Single nucleotide polymorphism (SNP) markers are powerful tools for investigating population structures, linkage analysis, and genome-wide association studies, as well as for breeding and population management. The availability of SNP markers has been limited to the most commercially important timber species, primarily due to the cost of genome sequencing required for SNP discovery. In this study, a combination of reference-based and reference-free approaches were used to identify SNPs in Nordmann fir (Abies nordmanniana), a species previously lacking genomic sequence information. Using a combination of a genome assembly of the closely related Silver fir (Abies alba) species and a de novo assembly of low-copy regions of the Nordmann fir genome, we identified a high density of reliable SNPs. Reference-based approaches identified two million SNPs in common between the Silver fir genome and low-copy regions of Nordmann fir. A combination of one reference-free and two reference-based approaches identified 250 shared SNPs. A subset of 200 SNPs were used to genotype 342 individuals and thereby tested and validated in the context of identity analysis and/or clone identification. The tested SNPs successfully identified all ramets per clone and five mislabeled individuals via identity and genomic relatedness analysis. The identified SNPs will be used in ad hoc breeding of Nordmann fir in Denmark.

A simulation-based assessment of the efficiency of QTL mapping under environment and genotype x environment interaction effects.

The objective of this simulation-based study was to assess how genes, environments, and genotype x environment (GxE) interaction affect the quantitative trait loci (QTL) mapping efficiency. The simulation software performed 50 samplings of 300 recombinant inbred lines (RILs) from a F2, which were assessed in six environments. The RILs were genotyped for 977 single nucleotide polymorphisms (SNP) and phenotyped for grain yield. The average SNP density was 2 cM. We defined six QTLs and 190 minor genes. The trait heritability ranged from 30 to 80%. We fitted the single QTL model and the multiple QTL model on multiple phenotypes. The environment and complex GxE interaction effects led to a low correlation between the QTL heritability and power. The single- and across-environment analyses allowed all QTLs be declared, with an average power of 28 to 100%. In the across-environment analysis, five QTLs showed average power in the range 46 to 82%. Both models provided a good control of the false positive rate (6%, on average) and a precise localization of the QTLs (bias of 2 cM, on average). The QTL power in each environment has a high positive correlation with the range between QTL genotypes for the sum of the additive, environment, and GxE interaction effects (0.76 to 0.96). The uncertainty about the magnitude and sign of the environment and GxE interaction effects makes QTL mapping in multi-environment trials unpredictable. Unfortunately, this uncertainty has no solution because the geneticist has no control over the magnitude and sign of the environment and GxE interaction effects. However, the single- and across-environment analyses are efficient even under a low correlation between QTL heritability and power.

Prioritized imputed sequence variants from multi-population GWAS improve prediction accuracy for sea lice count in Atlantic salmon (Salmo salar)

Sea lice infestation is one of the major fish health problems that occurs during the grow-out phase in Atlantic salmon (Salmo salar) aquaculture. In this study, we integrated different genomic approaches, including whole-genome sequencing (WGS), genotype imputation and meta-analysis of genome-wide association studies (GWAS), to identify single-nucleotide polymorphisms (SNPs) associated with sea lice count in Atlantic salmon. Different sets of trait-associated SNPs were prioritized and compared against randomly chosen markers, based on the accuracy of genomic predictions for the trait. Lice count phenotypes and dense genotypes of five breeding populations challenged with sea lice were used. Genotype imputation was applied to increase SNP density of challenged animals to WGS level. The summary statistics from GWAS of each population were then combined in a meta-analysis to increase the sample size and improve the statistical power of associations. Eight different genotyping scenarios were considered for genomic prediction: 70K_array: 70 K standard genotyping panel; 70K_priori: 70 K SNPs with the highest p-values identified in the meta-analysis; 30K_priori: 30 K SNPs with the highest p-values identified in the meta-analysis; WGS: SNPs imputed to whole-genome sequencing level. The remaining four scenarios were the same SNP sets with a linkage disequilibrium (LD) pruning filter: 70K_array_LD; 70K_priori_LD; 30K_priori_LD and WGS_LD. Genomic prediction accuracy was evaluated using a five-fold cross-validation scheme in two different populations which were excluded from the meta-analysis to remove possible validation-reference bias. Results showed significant genetic variation for the number of sea lice in Atlantic salmon across populations, with heritabilities ranging from 0.06 to 0.24. The meta-analysis identified several SNPs associated with sea lice resistance, mainly in Ssa03 and Ssa09 chromosomes. Genomic prediction using the GWAS-based prioritized SNPs showed higher accuracy compared to the standard SNP array in most of scenarios; achieving up to 57% increase in accuracy. Accuracy of prioritized scenarios was higher for the 70K_priori than the 30K_priori. The use of WGS data in genomic prediction presented marginal or negative accuracy gain compared to the standard SNP array. The LD-pruning filter presented no benefits, reducing accuracy in most scenarios. Overall, our study demonstrated the potential of prioritized imputed sequence variants from multi-population GWAS meta-analysis to improve prediction accuracy for sea lice counts in Atlantic salmon. The findings suggest that incorporating WGS data and prioritized SNPs from GWAS meta-analysis can accelerate the genetic progress of selection for polygenic traits in salmon aquaculture.

Genetic mapping of quantitative trait loci associated with drought tolerance in chickpea (Cicer arietinum L.)

Elucidation of the genetic basis of drought tolerance is vital for genomics-assisted breeding of drought tolerant crop varieties. Here, we used genotyping-by-sequencing (GBS) to identify single nucleotide polymorphisms (SNPs) in recombinant inbred lines (RILs) derived from a cross between a drought tolerant chickpea variety, Pusa 362 and a drought sensitive variety, SBD 377. The GBS identified a total of 35,502 SNPs and subsequent filtering of these resulted in 3237 high-quality SNPs included in the eight linkage groups. Fifty-one percent of these SNPs were located in the genic regions distributed throughout the genome. The high density linkage map has total map length of 1069 cm with an average marker interval of 0.33 cm. The linkage map was used to identify 9 robust and consistent QTLs for four drought related traits viz. membrane stability index, relative water content, seed weight and yield under drought, with percent variance explained within the range of 6.29%–90.68% and LOD scores of 2.64 to 6.38, which were located on five of the eight linkage groups. A genomic region on LG 7 harbors quantitative trait loci (QTLs) explaining > 90% phenotypic variance for membrane stability index, and > 10% PVE for yield. This study also provides the first report of major QTLs for physiological traits such as membrane stability index and relative water content for drought stress in chickpea. A total of 369 putative candidate genes were identified in the 6.6 Mb genomic region spanning these QTLs. In-silico expression profiling based on the available transcriptome data revealed that 326 of these genes were differentially expressed under drought stress. KEGG analysis resulted in reduction of candidate genes from 369 to 99, revealing enrichment in various signaling pathways. Haplotype analysis confirmed 5 QTLs among the initially identified 9 QTLs. Two QTLs, qRWC1.1 and qYLD7.1, were chosen based on high SNP density. Candidate gene-based analysis revealed distinct haplotypes in qYLD7.1 associated with significant phenotypic differences, potentially linked to pathways for secondary metabolite biosynthesis. These identified candidate genes bolster defenses through flavonoids and phenylalanine-derived compounds, aiding UV protection, pathogen resistance, and plant structure.The study provides novel genomic regions and candidate genes which can be utilized in genomics-assisted breeding of superior drought tolerant chickpea cultivars.

Genomic selection for hypoxia tolerance in large yellow croaker

The large yellow croaker (Larimichthys crocea) is one of the most economically important marine fishes on the southeast coast of China. However, high-density cultures, inbreeding depression, and environmental pollution have resulted in L. crocea often experiencing hypoxia, which has caused huge economic losses to the L. crocea farming industry. This study aimed to improve the hypoxia tolerance of farmed L. crocea using genomic selection (GS) for breeding. In this study, 399 and 354 samples from the reference and candidate populations, respectively, were genotyped using a 55 K liquid single-nucleotide polymorphism (SNP) array. Based on two different representations (survival status and survival time), the estimated values of heritability for the hypoxia tolerance trait in L. crocea using 38,472 clean SNPs were 0.65 and 0.62, respectively. A comparison of the genomic prediction accuracy under different GS models and SNP densities showed that GBLUP was the best GS model, and the 2500 SNPs selected using a genome-wide association study (GWAS)-based strategy were essentially close to all 38,472 SNPs. Based on the established optimal GS strategy (prediction accuracy was 0.4901 ± 0.0246), genomic estimated breeding values (GEBVs) were calculated for each candidate population. The tolerance capacity tests showed a significant improvement in hypoxia tolerance in the offspring of the candidate population with the top 10% GEBVs, having an 13.40% decrease and a 34.02% increase in the median lethal concentration (LC50) and median lethal time (LT50), respectively, compared to the control group. These results confirmed that GS is an effective genetic breeding technique when selecting for hypoxia tolerance in L. crocea.

Gene mining and functional analysis related to maize (Zea mays L.) seed size

Maize has widely been studied as a model of plant-growth promoting rhizobacteria (PGPR). Here, the genome sequences of 9P. The strains, together with 26 other sequenced Maize were comparatively studied. Phylogenetic analysis of the concatenated 244 single-copy core genes suggests that the 9P. The strains and 5 other Paenibacillus spp., isolated from diverse geographic regions and ecological niches, formed a closely related clade (here it is called Poly-clade). Analysis of single nucleotide polymorphisms (SNPs) reveals local diversification of the 14 Poly-clade genomes. SNPs were not evenly distributed throughout the 14 genomes and the regions with high SNP density contain the genes related to secondary metabolism, including genes coding for polyketide. Recombination played an important role in the genetic diversity of this clade, although the rate of recombination was clearly lower than mutation. The distinction among people and different creatures can be gotten by relative examinations. This study reveals that both maize and its closely related species have plant growth promoting traits and they have great potential uses in agriculture and horticulture as PGPR.

Evaluation of a High-Throughput Dense Single-Nucleotide Polymorphism PCR Multiplex Next Generation Sequencing Application for Human Remains Identification

Evaluation of a High-Throughput Dense Single-Nucleotide Polymorphism PCR Multiplex Next Generation Sequencing Application for Human Remains Identification

Shed skin as a source of DNA for genotyping-by-sequencing (GBS) in reptiles

Over a fifth of reptile species are classified as ‘Threatened’ and conservation efforts, especially those aimed at recovery of isolated or fragmented populations, will require genetic and genomic data and resources. Shed skins of snakes and other reptiles contain DNA; are a safe and ethical way of non-invasively sampling large numbers of individuals; and provide a simple mechanism by which to involve the public in scientific research. Here we test whether the DNA in dried shed skin is suitable for reduced representation sequencing approaches, specifically genotyping-by-sequencing (GBS). Shed skin-derived libraries resulted in fewer sequenced reads than those from snap-frozen muscle samples, and contained slightly fewer variants (70,685 SNPs versus 97,724), but this issue can easily be rectified with deeper sequencing of shed skin-derived libraries. Skin-derived libraries also have a very slight (but significantly different) profile of transitions and transversions, most likely as a result of DNA damage, but the impact of this is minimal given the large number of single nucleotide polymorphisms (SNPs) involved. SNP density tends to scale with chromosome length, and microchromosomes have a significantly higher SNP density than macrochromosomes, most likely because of their higher GC content. Overall, shed skin provides DNA of sufficient quality and quantity for the identification of large number of SNPs, but requires greater sequencing depth, and consideration of the GC richness of microchromosomes when selecting restriction enzymes.

Identification of oil palm cis-regulatory elements based on DNA free energy and single nucleotide polymorphism density

Transcription control through cis-regulatory elements (CREs) is one of important regulators of gene expression. This study aimed to identify the location of CREs in oil palm (Elaeis guineensis Jacq.) using the combination of DNA free energy and single nucleotide polymorphism (SNP) density approaches. Promoter region sequences were extracted oil palm genome spanning from 1500 nucleotides (nt) upstream to 1000 nt downstream of every annotated transcription start sites (TSS). Free energy profiles of each promoter region were calculated using PromPredict software. Raw reads from the deep sequencing of 59 oil palm origins were used to calculate SNP density of each promoter region. The result showed that the average free energy (AFE) on the upstream region of TSS is about 1.5 kcal/mol higher compared to the downstream region. Using DNA free energy method, 16,281 regions of CREs were predicted. Most of predicted CREs was located between 1 and 500 nt upstream of TSS. Anti-correlation pattern between free energy and SNP density was observed on the predicted regions of CREs. This anti-correlated pattern was also observed on an experimentally determined promoter of the oil palm metallothionein gene, EgMSP1. Considering the increasing use of promoter information on plant biotechnology, an easy and accurate promoter prediction using the combination of free energy and SNP density method could be recommended.

Lathyrus sativus Resistance Against the Existing and Emerging Pathogens Erysiphe pisi and E. trifolii: A Case of Commonalities or Total Discrepancy?

Powdery mildew on Lathyrus sativus (grass pea) is commonly caused by Erysiphe pisi, the causal agent of pea powdery mildew. E. trifolii could also pose an additional threat to grass pea, as it does to pea (Pisum sativum). In order to understand the potential threat and the availability of resistance sources, the response to both pathogens was analyzed on a worldwide germplasm collection of 189 grass pea accessions. Infection type and disease severity (DS) of grass pea accessions, independently inoculated with E. pisi and E. trifolii, were evaluated under controlled conditions. A wide range of responses were detected, with the previously uncharacterized partial resistance to E. trifolii in grass pea detected less frequently and uncorrelated with partial resistance against E. pisi. To test for the lack of correlation at the genetic level, an exploratory association mapping study was undertaken by statistically combining grass pea collection DS scores against both pathogens, with 5,651 previously screened genotype-by-sequencing-based single nucleotide polymorphisms (SNP). Mostly different genetic regions in grass pea were identified as being associated with the response to E. trifolii and E. pisi, anticipating an independent genetic basis that requires further validation in larger germplasm collections, with higher SNP densities. This study proposes common and unique partial resistance components against two different powdery mildews, implying the need for complementary approaches to introduce resistance to both pathogens into new grass pea varieties. The identified sources of resistance and predicted genomic targets will assist in breeding for resistance to multiple powdery mildews.

Genomic prediction of survival against Vibrio harveyi in leopard coral grouper (Plectropomus leopardus) using GBLUP, weighted GBLUP, and BayesCπ

Leopard coral grouper (Plectropomus leopardus) is a marine economic fish that widely inhabits the western Pacific. In recent years, the quantity of wild-captured leopard coral grouper has had difficulty meeting commercial and recreational needs. Therefore, it is necessary to develop the artificial culture of leopard coral grouper. However, disease outbreaks caused by Vibrio harveyi have hindered the sustainable development of the leopard coral grouper farming industry. The primary purposes of this study are to 1) estimate the genomic heritability of survival against V. harveyi in P. leopardus; 2) perform a genome-wide association study (GWAS) to determine the genetic architecture; and 3) compare the predictive accuracy variation of genomic methods using different density single nucleotide polymorphism (SNP) panels and cross-validation grouping strategies. We collected 800 leopard coral groupers from two commercial aquatic companies and challenged them using V. harveyi by abdominal injection to obtain the survival phenotypes. After resequencing and quality control, a total of 798 fish and 1,211,259 high-quality SNPs were retained for downstream analysis. The genomic heritability (underlying scale) of survival against V. harveyi in leopard coral grouper ranged from 0.16 to 0.24. Eight trait-related regions with genetic variance explained >1% were identified from the GWAS results, and thirty genes were annotated from these regions. The accuracies of three genomic methods, genomic BLUP (GBLUP), weighted GBLUP (wGBLUP), and BayesCπ, showed ascending trends with the number of SNPs varied from 2 k to 100 k and reach a plateau when 30 k or more SNPs were used for prediction. In addition, there was no obvious difference in predictive accuracy between the three methods when 30 k SNPs were used for prediction. In conclusion, survival against V. harveyi in leopard coral grouper belongs to a low-to-moderate-heritability trait and has a polygenic genetic architecture. Although it is feasible to apply genomic methods using the reference group constructed from nonfamily materials, a full investigation of the relationship between candidates and the reference group is necessary in leopard coral grouper commercial breeding programs, which would help maximize the predictive accuracy.

Design and validation of a high-density single nucleotide polymorphism array for the Eastern oyster (Crassostrea virginica).

Dense single nucleotide polymorphism (SNP) arrays are essential tools for rapid high-throughput genotyping for many genetic analyses, including genomic selection and high-resolution population genomic assessments. We present a high-density (200 K) SNP array developed for the Eastern oyster (Crassostrea virginica), which is a species of significant aquaculture production and restoration efforts throughout its native range. SNP discovery was performed using low-coverage whole-genome sequencing of 435 F1 oysters from families from 11 founder populations in New Brunswick, Canada. An Affymetrix Axiom Custom array was created with 219,447 SNPs meeting stringent selection criteria and validated by genotyping more than 4,000 oysters across 2 generations. In total, 144,570 SNPs had a call rate >90%, most of which (96%) were polymorphic and were distributed across the Eastern oyster reference genome, with similar levels of genetic diversity observed in both generations. Linkage disequilibrium was low (maximum r2 ∼0.32) and decayed moderately with increasing distance between SNP pairs. Taking advantage of our intergenerational data set, we quantified Mendelian inheritance errors to validate SNP selection. Although most of SNPs exhibited low Mendelian inheritance error rates overall, with 72% of called SNPs having an error rate of <1%, many loci had elevated Mendelian inheritance error rates, potentially indicating the presence of null alleles. This SNP panel provides a necessary tool to enable routine application of genomic approaches, including genomic selection, in C. virginica selective breeding programs. As demand for production increases, this resource will be essential for accelerating production and sustaining the Canadian oyster aquaculture industry.

JLOH: Inferring loss of heterozygosity blocks from sequencing data

Heterozygosity is a genetic condition in which two or more alleles are found at a genomic locus. Individuals that are the offspring of genetically divergent yet still interfertile parents (e.g. hybrids) are highly heterozygous. One of the most studied aspects in the genomes of these individuals is the loss of their original heterozygosity (LOH) when multi-allelic sites lose one of their two alleles by converting it to the other, or by remaining hemizygous at that site. The region undergoing LOH may involve a single nucleotide polymorphism (SNP) or a longer stretch of DNA. LOH is deeply interconnected with adaptation but the in silico techniques to infer evolutionary relevant LOH blocks are hardly standardised, and a general tool to infer and analyse them across genomic contexts and species is missing. Here, we present JLOH, a computational toolkit for the inference and exploration of LOH blocks in genomes with at least 1% heterozygosity. JLOH only requires commonly available genomic sequencing data as input. Starting from mapped reads, called variants and a reference genome sequence, JLOH infers candidate LOH blocks based on SNP density (SNPs/kbp) and read coverage per position. Considering that most organisms that undergo extensive LOH are hybrids, JLOH has been designed to capture any subgenomic LOH pattern, assigning each LOH block to its subgenome of origin.

Draft genome sequencing and secretome profiling of Sclerotinia sclerotiorum revealed effector repertoire diversity and allied broad-host range necrotrophy

White mold commonly known as Sclerotinia sclerotiorum causes stem rot disease and has emerged as one of the major fungal pathogens of oilseed Brassica across the world. In the present study, consistently virulent S. sclerotiorum isolate “ESR-01” was sequenced and an assembly size of ~ 41 Mb with 328 scaffolds having N50 of 447,128 was obtained. Additionally, 27,450 single nucleotide polymorphisms (SNPs) were identified from 155 scaffolds against S. sclerotiorum 1980 isolate, with an average SNP density of ~ 1.5 per kb genome. 667 repetitive elements were identified and approximately comprised 7% of the total annotated genes. The DDE_1 with 454 in numbers was found to be the most abundant and accounts for 68% of the total predicted repetitive elements. In total, 3844 simple sequence repeats are identified in the 328 scaffolds. A total of 9469 protein-coding genes were predicted from the whole genome assembly with an average gene length of 1587 bp and their distribution as 230.95 genes per Mb in the genome. Out of 9469 predicted protein-coding genes, 529 genes were observed encoding the CAZymes (Carbohydrate-Active enzymes) capable of degradation of the complex polysaccharides. Glycosyltransferase (GT) families were most abundant (49.71%) among the predicted CAZymes and GT2 (23%), GT4 (20%), and glycoside hydrolase (GH) 23% with GH18 (11%) were the prominent cell wall degrading enzyme families in the ESR-01 secretome. Besides this, 156 genes essential for the pathogen-host interactions were also identified. The effector analysis in the whole genome proteomics dataset revealed a total of 57 effector candidates (ECs) and 27 of them were having their analogs whereas the remaining 30 were novel ones. Eleven selected ECs were validated experimentally by analyzing the expression profile of the ESR-01 isolate of S. sclerotiorum. Together, the present investigation offers a better understanding of the S. sclerotiorum genome, secretome, and its effector repertoire which will help in refining the present knowledge on S. sclerotiorum-Brassica interactions and necrotrophic lifestyle of the phytopathogen in general.

GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data.

Total number born (TNB), number of stillborn (NSB), and gestation length (GL) are economically important traits in pig production, and disentangling the molecular mechanisms associated with traits can provide valuable insights into their genetic structure. Genotype imputation can be used as a practical tool to improve the marker density of single-nucleotide polymorphism (SNP) chips based on sequence data, thereby dramatically improving the power of genome-wide association studies (GWAS). In this study, we applied Beagle software to impute the 50 K chip data to the whole-genome sequencing (WGS) data with average imputation accuracy (R2) of 0.876. The target pigs, 2655 Large White pigs introduced from Canadian and French lines, were genotyped by a GeneSeek Porcine 50K chip. The 30 Large White reference pigs were the key ancestral individuals sequenced by whole-genome resequencing. To avoid population stratification, we identified genetic variants associated with reproductive traits by performing within-population GWAS and cross-population meta-analyses with data before and after imputation. Finally, several genes were detected and regarded as potential candidate genes for each of the traits: for the TNB trait: NOTCH2, KLF3, PLXDC2, NDUFV1, TLR10, CDC14A, EPC2, ORC4, ACVR2A, and GSC; for the NSB trait: NUB1, TGFBR3, ZDHHC14, FGF14, BAIAP2L1, EVI5, TAF1B, and BCAR3; for the GL trait: PPP2R2B, AMBP, MALRD1, HOXA11, and BICC1. In conclusion, expanding the size of the reference population and finding an optimal imputation strategy to ensure that more loci are obtained for GWAS under high imputation accuracy will contribute to the identification of causal mutations in pig breeding.

Population history and genome wide association studies of birth weight in a native high altitude Ladakhi population.

Pathological low birth weight due to fetal growth restriction (FGR) is an important predictor of adverse obstetric and neonatal outcomes. It is more common amongst native lowlanders when gestating in the hypoxic environment of high altitude, whilst populations who have resided at high altitude for many generations are relatively protected. Genetic study of pregnant populations at high altitude permits exploration of the role of hypoxia in FGR pathogenesis, and perhaps of FGR pathogenesis more broadly. We studied the umbilical cord blood DNA of 316 neonates born to pregnant women managed at the Sonam Norboo Memorial Hospital, Ladakh (altitude 3540m) between February 2017 and January 2019. Principal component, admixture and genome wide association studies (GWAS) were applied to dense single nucleotide polymorphism (SNP) genetic data, to explore ancestry and genetic predictors of low birth weight. Our findings support Tibetan ancestry in the Ladakhi population, with subsequent admixture with neighboring Indo-Aryan populations. Fetal growth protection was evident in Ladakhi neonates. Although no variants achieved genome wide significance, we observed nominal association of seven variants across genes (ZBTB38, ZFP36L2, HMGA2, CDKAL1, PLCG1) previously associated with birthweight.