Set Of Single Nucleotide Polymorphisms Research Articles

Integrative analysis reveals novel insights into juvenile idiopathic arthritis pathogenesis and shared molecular pathways with associated traits.

Juvenile idiopathic arthritis (JIA) is an autoimmune joint disease that frequently co-occurs with other complex phenotypes, including cancers and other autoimmune diseases. Despite the identification of numerous risk variants through genome-wide association studies (GWAS), the affected genes, their connection to JIA pathogenesis, and their role in the development of associated traits remain unclear. This study aims to address these gaps by elucidating the gene-regulatory mechanisms underlying JIA pathogenesis and exploring its potential role in the emergence of associated traits. A two-sample Mendelian Randomization (MR) analysis was conducted to identify blood-expressed genes causally linked to JIA. A curated protein interaction network was subsequently used to identify sets of single-nucleotide polymorphisms (i.e., spatial eQTL SNPs) that regulate the expression of JIA causal genes and their protein interaction partners. These SNPs were cross-referenced against the GWAS catalog to identify statistically enriched traits associated with JIA. The two-sample MR analysis identified 52 genes whose expression changes in the blood are putatively causal for JIA. These genes (e.g., HLA, LTA, LTB, IL6ST) participate in a range of immune-related pathways (e.g., antigen presentation, cytokine signalling) and demonstrate cell type-specific regulatory patterns across different immune cell types (e.g., PPP1R11 in CD4+ T cells). The spatial eQTLs that regulate JIA causal genes and their interaction partners were statistically enriched for GWAS SNPs linked with 95 other traits, including both known and novel JIA-associated traits. This integrative analysis identified genes whose dysregulation may explain the links between JIA and associated traits, such as autoimmune/inflammatory diseases (genes at 6p22.1 locus), Hodgkin lymphoma (genes at 6p21.3 [FKBPL, PBX2, AGER]), and chronic lymphocytic leukemia (BAK1). Our approach provides a significant advance in understanding the genetic architecture of JIA and associated traits. The results suggest that the burden of associated traits may differ among JIA patients, influenced by their combined genetic risk across different clusters of traits. Future experimental validation of the identified connections could pave the way for refined patient stratification, the discovery of new biomarkers, and shared therapeutic targets.

Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases.

Single nucleotide polymorphisms (SNPs) from omics data create a reidentification risk for individuals and their relatives. Although the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly shown, the availability of small sets of noisy genotypes, from environmental DNA samples or functional genomics data, motivated us to quantify their informativeness. We present a computational tool suite, termed Privacy Leakage by Inference across Genotypic HMM Trajectories (PLIGHT), using population-genetics-based hidden Markov models (HMMs) of recombination and mutation to find piecewise alignment of small, noisy SNP sets to reference haplotype databases. We explore cases in which query individuals are either known to be in the database, or not, and consider several genotype queries, including those from environmental sample swabs from known individuals and from simulated "mosaics" (two-individual composites). Using PLIGHT on a database with ∼5000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ∼20 can identify both components in two-individual mosaics, and 20-30 can identify first-order relatives. Using noisy environmental-sample-derived SNPs, PLIGHT identifies individuals in a database using ∼30 SNPs. Even when the individuals are not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained SNP imputation. Finally, by quantifying privacy leakage from sparse SNP sets, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about population membership or allele frequency. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from genomic data.

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.

Identification of genetic associations and functional SNPs of bovine KLF6 gene on milk production traits in Chinese holstein

BackgroundOur previous research identified the Kruppel like factor 6 (KLF6) gene as a prospective candidate for milk production traits in dairy cattle. The expression of KLF6 in the livers of Holstein cows during the peak of lactation was significantly higher than that during the dry and early lactation periods. Notably, it plays an essential role in activating peroxisome proliferator-activated receptor α (PPARα) signaling pathways. The primary aim of this study was to further substantiate whether the KLF6 gene has significant genetic effects on milk traits in dairy cattle.ResultsThrough direct sequencing of PCR products with pooled DNA, we totally identified 12 single nucleotide polymorphisms (SNPs) within the KLF6 gene. The set of SNPs encompasses 7 located in 5′ flanking region, 2 located in exon 2 and 3 located in 3′ untranslated region (UTR). Of these, the g.44601035G > A is a missense mutation that resulting in the replacement of arginine (CGG) with glutamine (CAG), consequently leading to alterations in the secondary structure of the KLF6 protein, as predicted by SOPMA. The remaining 7 regulatory SNPs significantly impacted the transcriptional activity of KLF6 following mutation (P < 0.005), manifesting as changes in transcription factor binding sites. Additionally, 4 SNPs located in both the UTR and exons were predicted to influence the secondary structure of KLF6 mRNA using the RNAfold web server. Furthermore, we performed the genotype-phenotype association analysis using SAS 9.2 which found all the 12 SNPs were significantly correlated to milk yield, fat yield, fat percentage, protein yield and protein percentage within both the first and second lactations (P < 0.0001 ~ 0.0441). Also, with Haploview 4.2 software, we found the 12 SNPs linked closely and formed a haplotype block, which was strongly associated with five milk traits (P < 0.0001 ~ 0.0203).ConclusionsIn summary, our study represented the KLF6 gene has significant impacts on milk yield and composition traits in dairy cattle. Among the identified SNPs, 7 were implicated in modulating milk traits by impacting transcriptional activity, 4 by altering mRNA secondary structure, and 1 by affecting the protein secondary structure of KLF6. These findings provided valuable molecular insights for genomic selection program of dairy cattle.

Identification of a Novel Pm65 Allele Conferring a Wide Spectrum of Resistance to Powdery Mildew in Wheat Accession PI 351817.

Powdery mildew is caused by the highly adaptive biotrophic fungus Blumeria graminis f. sp. tritici infecting wheat worldwide. Novel powdery mildew resistance genes are urgently needed that can be used rapidly in wheat cultivar development with minimal disruption of trait advances elsewhere. PI 351817 is a German cultivar exhibiting a wide spectrum of resistance to B. graminis f. sp. tritici isolates collected from different wheat-growing regions of the United States. Evaluation of an F2 population and 237 F2:3 lines derived from OK1059060-2C14 × PI 351817 for responses to B. graminis f. sp. tritici isolate OKS(14)-B-3-1 identified a single dominant gene, designated Pm351817, for powdery mildew resistance in PI 351817. Using bulked segregant analysis (BSA) and simple sequence repeat (SSR) markers, Pm351817 was mapped in the terminal region of the long arm of chromosome 2A. Deep sequencing of the genotyping-by-sequencing libraries of the two parental lines identified a set of single-nucleotide polymorphism (SNP) markers in the 2AL candidate gene region. Those SNP markers was subsequently converted to Kompetitive allele-specific PCR (KASP) markers for genotyping the mapping population. Linkage analysis delimited Pm351817 to a 634-kb interval between Stars-KASP656 (771,207,512 bp) and Stars-KASP662 (771,841,609 bp) on 2AL, based on the Chinese Spring reference sequence IWGSC RefSeq v 2.1. Tests of allelism indicated that Pm351817 is located at the Pm65 locus. Pm351817 shows resistance to all B. graminis f. sp. tritici isolates used in this study and can be used to enhance powdery mildew resistance in the United States. KASP markers flanking Pm351817 can be used to select Pm351817 in wheat breeding programs after further tests for polymorphism.

Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population

BackgroundAlthough the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data.ResultsWe used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN.ConclusionsThe SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns.

Advances in high-throughput sequencing technologies have made it possible to access millions of measurements from thousands of people. Single nucleotide polymorphisms (SNPs), the most common type of mutation in the human genome, have been shown to play a significant role in the development of complex and multifactorial diseases. However, studying the synergistic interactions between different SNPs in explaining multifactorial diseases is challenging due to the high dimensionality of the data and methodological complexities. Existing solutions often use a multi-objective approach based on metaheuristic optimization algorithms such as harmony search. However, previous studies have shown that using a multi-objective approach is not sufficient to address complex disease models with no or low marginal effect. In this research, we introduce a locus-driven harmony search (LDHS), an improved harmony search algorithm that focuses on using SNP locus information and genetic inheritance patterns to initialize harmony memories. The proposed method integrates biological knowledge to improve harmony memory initialization by adding SNP combinations that are likely candidates for interaction and disease causation. Using a SNP grouping process, LDHS generates harmonies that include SNPs with a higher potential for interaction, resulting in greater power in detecting disease-causing SNP combinations. The performance of the proposed algorithm was evaluated on 200 synthesized datasets for disease models with and without marginal effect. The results show significant improvement in the power of the algorithm to find disease-related SNP sets while decreasing computational cost compared to state-of-the-art algorithms. The proposed algorithm also demonstrated notable performance on real breast cancer data, showing that integrating prior knowledge can significantly improve the process of detecting disease-related SNPs in both real and synthesized data.

Genotyping-by-sequencing reveals a high number and quality of single nucleotide polymorphisms in guinea pigs (Cavia porcellus) from the Peruvian Andes.

Guinea pigs are a major source of animal protein for Peruvian Andean families. Despite the economic and cultural relevance of guinea pigs, their genomic characterization has been scarcely addressed. Genotyping-by-sequencing (GBS) has emerged as an affordable alternative to genotyping of livestock and native animals. Here, we report the use of GBS for single nucleotide polymorphism (SNP) discovery of traditionally raised guinea pigs from six regions of the Peruvian Andes and one group of breeding animals. The paired-end (2 × 150 bp) sequencing of 40 guinea pig DNA samples generated a mean of 6.4 million high-quality sequencing reads per sample. We obtained an average sequencing depth of 10× with an 88.5% mapping rate to the Cavia porcellus reference genome. A total of 279 965 SNPs (102 SNPs/Mbp) were identified after variant calling and quality filtering. Based on this SNP set, we assessed the genetic diversity and distance within our selected guinea pig populations. An overall average minor allele frequency of 0.13, an observed heterozygosity of 0.31, an expected heterozygosity of 0.35, and an F-value of 0.1 were obtained, while the SNP-based neighbor-joining tree suggests a closer genetic relationship between individuals from geographically close locations. We showed that GBS is a cost-effective tool for SNP discovery and genetic characterization of Peruvian guinea pig populations. Therefore, it may be considered as a suitable and affordable tool for genomic characterization of poorly studied native animal species.

Characteristics of insulin resistance in Korean adults from the perspective of circadian and metabolic sensing genes

BackgroundThe biological clock allows an organism to anticipate periodic environmental changes and adjust its physiology and behavior accordingly.ObjectiveThis retrospective cross-sectional study examined circadian gene polymorphisms and clinical characteristics associated with insulin resistance (IR).MethodsWe analyzed data from 1,404 Korean adults aged 30 to 55 with no history of cancer and cardio-cerebrovascular disease. The population was classified according to sex and homeostasis model assessment of insulin resistance (HOMA-IR) values. Demographics, anthropometric and clinical characteristics, and single nucleotide polymorphisms (SNPs) were analyzed with respect to sex, age, and HOMA-IR values. We used association rule mining to identify sets of SNPs from circadian and metabolic sensing genes that may be associated with IR.ResultsAmong the subjects, 15.0% of 960 women and 24.3% of 444 men had HOMA-IR values above 2. Most of the parameters differed significantly between men and women, as well as between the groups with high and low insulin sensitivity. Body fat mass of the trunk, which was significantly higher in insulin-resistant groups, had a higher correlation with high sensitivity C-reactive protein and hemoglobin levels in women, and alanine aminotransferase and aspartate aminotransferase levels in men. Homozygous minor allele genotype sets of SNPs rs17031578 and rs228669 in the PER3 gene could be more frequently found among women with HOMA-IR values above 2 (p = .014).ConclusionOxidative stress enhanced by adiposity and iron overload, which may also be linked to NRF2 and PER3-related pathways, is related to IR in adulthood. However, due to the small population size in this study, more research is needed.

Identification of single nucleotide polymorphisms (SNPs) associated with chronic graft-versus-host disease in patients undergoing allogeneic hematopoietic cell transplantation

IntroductionChronic graft-versus-host disease (cGVHD) is a debilitating side effect of allogeneic hematopoietic cell transplantation (HCT), affecting the quality of life of patients. We used whole exome sequencing to identify candidate SNPs and complete a multi-marker gene-level analysis using a cohort of cGVHD( +) (N = 16) and cGVHD( −) (N = 66) HCT patients.MethodsSaliva samples were collected from HCT patients (N = 82) pre-conditioning in a multi-center study from March 2011 to May 2018. Exome sequencing was performed and FASTQ files were processed for sequence alignments. Significant SNPs were identified by logistic regression using PLINK2v3.7 and Fisher’s exact test. One cGVHD( −) patient sample was excluded from further analysis since no SNP was present in at least 10% of the sample population. The FUMA platform’s SNP2GENE was utilized to annotate SNPs and generate a MAGMA output. Chromatin state visualization of lead SNPs was completed using Epilogos tool. FUMA’s GENE2FUNC was used to obtain gene function and tissue expression from lead genomic loci.ResultsLogistic regression classified 986 SNPs associated with cGVHD( +). SNP2GENE returned three genomic risk loci, four lead SNPs, 48 candidate SNPs, seven candidate GWAS tagged SNPs, and four mapped genes. Fisher’s exact test identified significant homozygous genotypes of four lead SNPs (p < 0.05). GENE2FUNC analysis of multi-marker SNP sets identified one positional gene set including lead SNPs for KANK1 and KDM4C and two curated gene sets including lead SNPs for PTPRD, KDM4C, and/or KANK1.ConclusionsOur data suggest that SNPs in three genes located on chromosome 9 confer genetic susceptibility to cGVHD in HCT patients. These genes modulate STAT3 expression and phosphorylation in cancer pathogenesis. The findings may have implications in the modulation of pathways currently targeted by JAK inhibitors in cGVHD clinical trials.

Causal Effects of Gut Microbiota on Age-Related Macular Degeneration: A Mendelian Randomization Study.

Recently, the association between gut microbiota and age-related macular degeneration (AMD) through the gut-retina axis has attracted great interest. However, the causal relationship between them has not been elucidated. Using publicly available genome-wide association study summary statistics, we conducted a two-sample Mendelian randomization (MR) analysis to examine the causal relationship between the gut microbiota and the occurrence of AMD. The study used a variety of quality control techniques to select instrumental single nucleotide polymorphisms (SNPs) with strong exposure associations. We used a set of SNPs as instrumental variable that were below the genome-wide statistical significance threshold (5 × 10-8). Additionally, a separate group of SNPs below the locus-wide significance level (1 × 10-5) were selected as instrumental variables to ensure a comprehensive conclusion. Inverse variance-weighted (IVW) analysis was the primary technique we used to examine causality in order to confirm the validity of our findings. The MR-Egger intercept test, Cochran's Q test, and leave-one-out sensitivity analysis were used to evaluate the horizontal pleiotropy, heterogeneities, and stability of the genetic variants. IVW results showed that genus Anaerotruncus (P=5.00 × 10-3), genus Candidatus Soleaferrea (P=1.83 × 10-2), and genus unknown id.2071 (P=3.12 × 10-2) were protective factors for AMD. The Eubacterium oxidoreducens group (P=3.17 × 10-2), genus Faecalibacterium (P=2.67 × 10-2), and genus Ruminococcaceae UCG-011 (P=4.04 × 10-2) were risk factors of AMD. No gut microbiota (GM) taxa were found to be causally related to AMD at the phylum, class, order, and family levels (P>0.05). The robustness of MR results were confirmed by heterogeneity and pleiotropy analysis. (P>0.05). We also performed a bidirectional analysis, which showed that genus Anaerotruncus, genus Candidatus Soleaferrea, genus unknown id.2071 and the Eubacterium oxidoreducens group had an interaction with AMD, whereas genus Faecalibacterium showed only a unilateral unfavorable effect on AMD. We confirmed a causal relationship between AMD and GM taxa, including the Eubacterium oxidoreducens group, Faecalibacterium, Ruminococcaceae UCG-011, Anaerotruncus, and Candidatus Soleaferrea. These strains have the potential to serve as new biomarkers, offering valuable insights into the treatment and prevention of AMD.

Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors: A Report From COG-ALTE03N1, BMTSS, and CCSS

BackgroundAnthracyclines are highly effective in treating cancer, albeit with increased cardiomyopathy risk. Although risk is attributed to associations with single nucleotide polymorphisms (SNPs), multiple SNPs on a gene and their interactions remain unexamined. ObjectivesThis study examined gene-level associations with cardiomyopathy among cancer survivors using whole-exome sequencing data. MethodsFor discovery, 278 childhood cancer survivors (129 cases; 149 matched control subjects) from the COG (Children’s Oncology Group) study ALTE03N1 were included. Logic regression (machine learning) was used to identify gene-level SNP combinations for 7,212 genes and ordinal logistic regression to estimate gene-level associations with cardiomyopathy. Models were adjusted for primary cancer, age at cancer diagnosis, sex, race/ethnicity, cumulative anthracycline dose, chest radiation, cardiovascular risk factors, and 3 principal components. Statistical significance threshold of 6.93 × 10−6 accounted for multiple testing. Three independent cancer survivor populations (COG study, BMTSS [Blood or Marrow Transplant Survivor Study] and CCSS [Childhood Cancer Survivor Study]) were used to replicate gene-level associations and examine SNP-level associations from discovery genes using ordinal logistic, conditional logistic, and Cox regression models, respectively. ResultsMedian age at cancer diagnosis for discovery cases and control subjects was 6 years and 8 years, respectively. Gene-level association for P2RX7 (OR: 0.10; 95% CI: 0.04-0.27; P = 2.19 × 10−6) was successfully replicated (HR: 0.65; 95% CI: 0.47-0.90; P = 0.009) in the CCSS cohort. Additional signals were identified on TNIK, LRRK2, MEFV, NOBOX, and FBN3. Individual SNPs across all discovery genes, except FBN3, were replicated. ConclusionsIn our study, SNP sets having 1 or no copies of P2RX7 variant alleles were associated with reduced risk of cardiomyopathy, presenting a potential therapeutic target to mitigate cardiac outcomes in cancer survivors.

Modeling of African population history using f-statistics is biased when applying all previously proposed SNP ascertainment schemes.

f-statistics have emerged as a first line of analysis for making inferences about demographic history from genome-wide data. Not only are they guaranteed to allow robust tests of the fits of proposed models of population history to data when analyzing full genome sequencing data-that is, all single nucleotide polymorphisms (SNPs) in the individuals being analyzed-but they are also guaranteed to allow robust tests of models for SNPs ascertained as polymorphic in a population that is an outgroup in a phylogenetic sense to all groups being analyzed. True "outgroup ascertainment" is in practice impossible in humans because our species has arisen from a substructured ancestral population that does not descend from a homogeneous ancestral population going back many hundreds of thousands of years into the past. However, initial studies suggested that non-outgroup-ascertainment schemes might produce robust enough results using f-statistics, and that motivated widespread fitting of models to data using non-outgroup-ascertained SNP panels such as the "Affymetrix Human Origins array" which has been genotyped on thousands of modern individuals from hundreds of populations, or the "1240k" in-solution enrichment reagent which has been the source of about 70% of published genome-wide data for ancient humans. In this study, we show that while analyses of population history using such panels work well for studies of relationships among non-African populations and one African outgroup, when co-modeling more than one sub-Saharan African and/or archaic human groups (Neanderthals and Denisovans), fitting of f-statistics to such SNP sets is expected to frequently lead to false rejection of true demographic histories, and failure to reject incorrect models. Analyzing panels of SNPs polymorphic in archaic humans, which has been suggested as a solution for the ascertainment problem, has limited statistical power and retains important biases. However, by carrying out simulations of diverse demographic histories, we show that bias in inferences based on f-statistics can be minimized by ascertaining on variants common in a union of diverse African groups; such ascertainment retains high statistical power while allowing co-analysis of archaic and modern groups.

A genomic estimated breeding value-assisted reduction method of single nucleotide polymorphism sets: a novel approach for determining the cutoff thresholds in genome-wide association studies and best linear unbiased prediction

ABSTRACT Traditionally, the p-value is the criterion for the cutoff threshold to determine significant markers in genome-wide association studies (GWASs). Choosing the best subset of markers for the best linear unbiased prediction (BLUP) for improved prediction ability (PA) has become an interesting issue. However, when dealing with many traits having the same marker information, the p-values’ themselves cannot be used as an obvious solution for having a confidence in GWAS and BLUP. We thus suggest a genomic estimated breeding value-assisted reduction method of the single nucleotide polymorphism (SNP) set (GARS) to address these difficulties. GARS is a BLUP-based SNP set decision presentation. The samples were Landrace pigs and the traits used were back fat thickness (BF) and daily weight gain (DWG). The prediction abilities (PAs) for BF and DWG for the entire SNP set were 0.8 and 0.8, respectively. By using the correlation between genomic estimated breeding values (GEBVs) and phenotypic values, selecting the cutoff threshold in GWAS and the best SNP subsets in BLUP was plausible as defined by GARS method. 6,000 SNPs in BF and 4,000 SNPs in DWG were considered as adequate thresholds. Gene Ontology (GO) analysis using the GARS results of the BF indicated neuron projection development as the notable GO term, whereas for the DWG, the main GO terms were nervous system development and cell adhesion.

Genetic diversity of North American popcorn germplasm and the effect of population structure on nicosulfuron response

AbstractPopcorn (Zea mays) is an important crop in the United States; however, genetic analyses of popcorn are limited and tend to utilize relatively few markers that cannot capture the total genomic variation. To improve the genomic resources in popcorn, a panel of 320 popcorn accessions was evaluated using 308,811 single nucleotide polymorphisms (SNPs) generated using a genotyping‐by‐sequencing approach. Using this genomic data, several model‐based clustering analyses identified two major groups. The first comprised North American Yellow Pearl Popcorns and accessions of the Chilean Curagua landrace, separated into three subgroups. The second, the Pointed and Latin American Popcorns, included all remaining North American, Latin American, and global accessions. These groups exhibited differences in population structure and genetic diversity. The North American Yellow Pearl Popcorns contain limited genetic diversity compared to the Pointed and Latin American Popcorns. Additionally, phenotypic differences between the two groups were observed in kernel color and nicosulfuron sensitivity. A filtered set of SNPs was curated and used for genome‐wide association studies and popcorn‐specific candidate genes for nicosulfuron tolerance were identified. The genomic characterization described here can be used by breeding programs to accelerate the rate of genetic gain and incorporate genetic diversity into elite popcorn germplasm.

Effectiveness of SNPs for Sibship Assignment in Farmed Banana Shrimp (Penaeus merguiensis)

Pedigrees are essential components in selective breeding programs to manage genetic diversity and obtain accurate genetic parameter estimates to ensure long-term response to selection in captive populations. High throughput and cost-effective sequencing technologies has offered opportunities of using single nucleotide polymorphisms (SNPs) to resolve penaeid shrimp pedigrees from mass spawning cohorts and communal rearing. Effects of SNPs for sibship assignment were investigated on 546 shrimp using two software programs, Colony and Sequoia. Assignment rates and accuracies using SNP subsets with six different minor allele frequencies (MAFs), four sets of SNPs, and five genotyping error rates were compared to the microsatellite-based pedigree established in a previous study. High MAFs and numbers of SNPs contributed to significant increases in assignment rates and accuracies, whereas genotyping error rates showed negligible impacts on assignment results. Sibship assignments achieved rates and accuracies of 98% and 83%, respectively, with a minimum number of 91 SNPs (average MAF ≥ 0.14), and the two different programs exhibited similar resulting patterns for different SNP subsets. High consistencies between SNP-based and microsatellite-based pedigrees showed that accurate pedigrees could be achieved by using SNPs and thus contribute to the long-term response to selection in farmed banana shrimp.

Polygenic risk scores in assessing lung cancer susceptibility in non-Hispanic White and Black populations.

10548 Background: Polygenic risk scores (PRS) have become an increasingly popular approach to evaluate cancer susceptibility, but have not adequately represented Black patients in model development. We used previously identified single nucleotide polymorphisms (SNPs) and annotated SNPs in associated gene regions to develop PRS in non-Hispanic Whites and Blacks using the INHALE dataset. Methods: Using the Multi-Ethnic Genotype Array, 1,204 SNPs for non-Hispanic Whites and 1,515 SNPs for Blacks were evaluated for their association with lung cancer risk adjusting for age, sex, total pack-years, family history of lung cancer, history of COPD and the top five PCs for genetic ancestry. Gene region-specific significant SNPs (p &lt; 0.05) were used to develop race-specific PRS. Results: The race-specific PRS included different sets of significant SNPs and were highly associated with lung cancer risk in both non-Hispanic Whites (OR = 1.07, 95% CI: 1.05-1.09, p = 3.44x10-9) and Blacks (OR = 1.12, 95% CI: 1.08-1.17, p = 9.14x10-8). These models remained significant for both Whites (OR = 1.05, 95% CI: 1.03-1.09, p = 0.0004) and Blacks (OR = 1.08, 95% CI: 1.01-1.15, p = 0.01) who currently do not meet USPSTF screening guidelines. AUC analysis demonstrated the Black-specific model (AUC = 0.68) outperformed the White-specific model (AUC = 0.57) (p = 0.03) when examined exclusively in the Black cohort. Conclusions: Using previously validated SNPs and gene regions, we developed race-specific PRS in non-Hispanic White and Black INHALE participants. Further validation of PRS could enable the incorporation of genetic risk modeling into lung cancer screening to identify patients who do not have traditional risk factors for lung cancer, as well as stratify patients into different levels of risk based on their genetic profile. Through the development of a reliable genetic risk factor prediction model, clinicians will have another method by which to evaluate lung cancer susceptibility, potentially leading to earlier diagnoses that portend more favorable treatment outcomes.

A genome-wide association study and genomic prediction for Phakopsora pachyrhizi resistance in soybean.

Soybean brown rust (SBR), caused by Phakopsora pachyrhizi, is a devastating fungal disease that threatens global soybean production. This study conducted a genome-wide association study (GWAS) with seven models on a panel of 3,082 soybean accessions to identify the markers associated with SBR resistance by 30,314 high quality single nucleotide polymorphism (SNPs). Then five genomic selection (GS) models, including Ridge regression best linear unbiased predictor (rrBLUP), Genomic best linear unbiased predictor (gBLUP), Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), Random Forest (RF), and Support vector machines (SVM), were used to predict breeding values of SBR resistance using whole genome SNP sets and GWAS-based marker sets. Four SNPs, namely Gm18_57,223,391 (LOD = 2.69), Gm16_29,491,946 (LOD = 3.86), Gm06_45,035,185 (LOD = 4.74), and Gm18_51,994,200 (LOD = 3.60), were located near the reported P. pachyrhizi R genes, Rpp1, Rpp2, Rpp3, and Rpp4, respectively. Other significant SNPs, including Gm02_7,235,181 (LOD = 7.91), Gm02_7234594 (LOD = 7.61), Gm03_38,913,029 (LOD = 6.85), Gm04_46,003,059 (LOD = 6.03), Gm09_1,951,644 (LOD = 10.07), Gm10_39,142,024 (LOD = 7.12), Gm12_28,136,735 (LOD = 7.03), Gm13_16,350,701(LOD = 5.63), Gm14_6,185,611 (LOD = 5.51), and Gm19_44,734,953 (LOD = 6.02), were associated with abundant disease resistance genes, such as Glyma.02G084100, Glyma.03G175300, Glyma.04g189500, Glyma.09G023800, Glyma.12G160400, Glyma.13G064500, Glyma.14g073300, and Glyma.19G190200. The annotations of these genes included but not limited to: LRR class gene, cytochrome 450, cell wall structure, RCC1, NAC, ABC transporter, F-box domain, etc. The GWAS based markers showed more accuracies in genomic prediction than the whole genome SNPs, and Bayesian LASSO model was the ideal model in SBR resistance prediction with 44.5% ~ 60.4% accuracies. This study aids breeders in predicting selection accuracy of complex traits such as disease resistance and can shorten the soybean breeding cycle by the identified markers.

MinSNPs: an R package for the derivation of resolution-optimised SNP sets from microbial genomic data.

Here, we present the R package, minSNPs. This is a re-development of a previously described Java application named Minimum SNPs. MinSNPs assembles resolution-optimised sets of single nucleotide polymorphisms (SNPs) from sequence alignments such as genome-wide orthologous SNP matrices. MinSNPs can derive sets of SNPs optimised for discriminating any user-defined combination of sequences from all others. Alternatively, SNP sets may be optimised to determine all sequences from all other sequences, i.e., to maximise diversity. MinSNPs encompasses functions that facilitate rapid and flexible SNP mining, and clear and comprehensive presentation of the results. The minSNPs' running time scales in a linear fashion with input data volume and the numbers of SNPs and SNPs sets specified in the output. MinSNPs was tested using a previously reported orthologous SNP matrix of Staphylococcus aureus and an orthologous SNP matrix of 3,279 genomes with 164,335 SNPs assembled from four S. aureus short read genomic data sets. MinSNPs was shown to be effective for deriving discriminatory SNP sets for potential surveillance targets and in identifying SNP sets optimised to discriminate isolates from different clonal complexes. MinSNPs was also tested with a large Plasmodium vivax orthologous SNP matrix. A set of five SNPs was derived that reliably indicated the country of origin within three south-east Asian countries. In summary, we report the capacity to assemble comprehensive SNP matrices that effectively capture microbial genomic diversity, and to rapidly and flexibly mine these entities for optimised marker sets.

BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies

BackgroundGenome-wide association studies (GWAS) seek to identify single nucleotide polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated SNPs, correlated observations, and the number of SNPs being two orders of magnitude larger than the number of observations, GWAS procedures often suffer from high false positive rates.ResultsWe propose BGWAS, a novel Bayesian variable selection method based on nonlocal priors for linear mixed models specifically tailored for genome-wide association studies. Our proposed method BGWAS uses a novel nonlocal prior for linear mixed models (LMMs). BGWAS has two steps: screening and model selection. The screening step scans through all the SNPs fitting one LMM for each SNP and then uses Bayesian false discovery control to select a set of candidate SNPs. After that, a model selection step searches through the space of LMMs that may have any number of SNPs from the candidate set. A simulation study shows that, when compared to popular GWAS procedures, BGWAS greatly reduces false positives while maintaining the same ability to detect true positive SNPs. We show the utility and flexibility of BGWAS with two case studies: a case study on salt stress in plants, and a case study on alcohol use disorder.ConclusionsBGWAS maintains and in some cases increases the recall of true SNPs while drastically lowering the number of false positives compared to popular SMA procedures.