Set Of Single Nucleotide Polymorphisms Research Articles

Abstract PS10-07: A second-generation polygenic risk score (PRS) based on genetic ancestry improves breast cancer (BC) risk prediction for all ancestries

Abstract Background: Common genetic variants, mainly single-nucleotide polymorphisms (SNPs) explain substantial genetic susceptibility to BC. PRS have been developed to quantify the combined effects of BC-associated SNPs, providing important information about BC risk. Historically, genome-wide association studies have been conducted in predominantly European populations, resulting in miscalibrated and inaccurate PRS for non-Europeans. We previously described a multiple-ancestry PRS (MA-PRS-149) based on 56 ancestry-informative and 93 BC-associated SNPs. The MA-PRS-149 achieved accuracy for all women by characterizing the genetic ancestry of each BC-associated SNP in terms of three reference ancestries (African, East Asian, and European), applying ancestry-specific SNP risks and frequencies, and combining the results as a weighted sum of three ancestry-specific PRS. Here, we aimed to improve the predictive accuracy of MA-PRS-149, particularly for non-Europeans, through the inclusion of additional BC-associated SNPs. Methods: Women referred for hereditary cancer testing and negative for pathogenic variants in BC-associated genes were divided into consecutive study cohorts to (1) quantify ancestry-specific SNP risks, (2) combine the three ancestry-specific PRS, and (3) pre-specified clinical validation. To select an optimal set of BC-associated SNPs, we developed a novel synthetic stepwise regression methodology that accounts for linkage disequilibrium. Ancestry-specific SNP risks were determined from meta-analyses of literature with clinical cohorts of 57,827 Black/African and 26,992 East Asian women. Ancestry-specific PRS were combined based on a diverse cohort of 157,740 women. Clinical validation was conducted in an independent cohort of 77,774 women. We used multivariable logistic regression adjusted for age, ancestry, and cancer history to test for improved BC risk prediction over clinical factors. We tested for improvement over the MA-PRS-149, and a European PRS, by including additional PRS as covariates. Calibration was assessed through goodness-of-fit tests. All analyses were conducted within the full cohort and ancestral subpopulations. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported per standard deviation within the corresponding population. Results: An optimal set of 383 SNPs (56 ancestry-informative and 327 BC-associated) was included in the final PRS (MA-PRS-383). MA-PRS-383 added significant predictive information to clinical factors in the full cohort and within each ancestry (Table 1). MA-PRS-383 had greater predictive accuracy than MA-PRS-149 or a 383-SNP PRS with European weights. Goodness-of-fit tests showed that MA-PRS-383 was well-calibrated and predicted risk accurately in the tails of the distribution for both European and non-European women. Conclusion: MA-PRS-383 was well-calibrated and substantially improved upon existing PRS in all tested ancestral populations. Incorporation of MA-PRS-383 into BC risk assessment may lead to more accurate identification of women who are most likely to benefit from screening and preventive medications. Citation Format: Timothy Simmons, Elisha Hughes, Dmitry Pruss, Matthew Kucera, Benjamin Roa, Thaddeus Judkins, Thomas Slavin, Victor Abkevich, Ryan Hoff, Srikanth Jammulapati, Susanne Wagner, Dale Muzzey, Jerry Lanchbury, Alexander Gutin. A second-generation polygenic risk score (PRS) based on genetic ancestry improves breast cancer (BC) risk prediction for all ancestries [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-07.

Selection of informative markers using machine learning approaches and genome-wide association studies to improve genomic prediction in Hanwoo cattle: a simulation study

The present study deploys a comparison of Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Genome Wide Association Studies (GWAS) in selecting optimum subsets of single nucleotide polymorphisms (SNPs) to be used in genomic prediction in cattle. The data simulation was carried out for 6,000 animals and 47,841 SNPs which include 43,633 polygenic markers and 4208 quantitative trait loci (QTL) using QMSim software. The genomic prediction was conducted with the best linear unbiased prediction (BLUP) method using the BLUPF90 program. The accuracy of prediction was computed in three different types, namely, Empirical all SNPs, Empirical QTL, and theoretical accuracy, Accuracy PEV . Among the three models, the highest Empirical all SNPs accuracy 0.79 was derived for GBM followed by 0.77 for XGBoost and 0.76 for GWAS. The Empirical QTL accuracy was almost equal for all three models. The maximum theoretical accuracy was obtained for GWAS which was 0.93, whereas GBM and XGBoost obtained 0.86 and 0.85 accuracy levels respectively. Our results indicate that all three models comparably performed in genomic predictions; however, subsets selected by both GBM and GWAS reported higher prediction accuracies compared to the whole SNP set. The number of QTL selected as a proportion of the total number of SNPs was superior in GWAS. These observations can be validated using real data which could enable further optimization of the analysis process.

A mediation analysis framework based on variance component to remove genetic confounding effect.

Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.

Stable Variable Selection Method with Shrinkage Regression Applied to the Selection of Genetic Variants Associated with Alzheimer’s Disease

In this work, we aimed to establish a stable and accurate procedure with which to perform feature selection in datasets with a much higher number of predictors than individuals, as in genome-wide association studies. Due to the instability of feature selection where many potential predictors are measured, a variable selection procedure is proposed that combines several replications of shrinkage regression models. A weighted formulation is used to define the final predictors. The procedure is applied for the investigation of single nucleotide polymorphism (SNP) predictors associated with Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Furthermore, the two following data scenarios are investigated: one that solely considers the set of SNPs, and another with the covariates of age, sex, educational level, and ε4 allele of the Apolipoprotein E (APOE4) genotype. The SNP rs2075650 and the APOE4 genotype are provided as risk factors for Alzheimer’s disease, which is in line with the literature, and another four new SNPs are indicated, thus cultivating new hypotheses for in vivo analyses. These experiments demonstrate the potential of the new method for stable feature selection.

Genome-wide association study (GWAS) reveals polygenic architecture for limber pine quantitative disease resistance to white pine blister rust.

Development of durable resistance effective against a broad range of pathotypes is crucial for restoration of pathogen-damaged ecosystems. This study dissected the complex genetic architecture for limber pine quantitative disease resistance (QDR) to Cronartium ribicola using a genome-wide association study (GWAS). Eighteen-month-old seedlings were inoculated for resistance screening under controlled conditions. Disease development was quantitatively assessed for QDR-related traits over four years post inoculation. To reveal genomic architecture contributing to QDR-related traits, a set of genes related to disease resistance with genome-wide distribution was selected for targeted sequencing for genotyping of single-nucleotide polymorphisms (SNPs). GWAS revealed a set of SNPs significantly associated with quantitative traits for limber pine QDR to WPBR, including number of needle spots and stem cankers, as well as survival four years post-inoculation. The peaks of marker-trait associations displayed a polygenic pattern with genomic regions as potential resistant quantitative trait loci (QTLs), distributed over ten of the 12 linkage groups (LGs) of Pinus. None of them were linked to the Cr4-controlled major gene resistance (MGR) previously mapped on LG08. Both normal canker and bole infection were mapped on LG05, and the associated SNPs explained their phenotypic variance up to 52%, tagging a major resistant QTL. Candidate genes containing phenotypically associated SNPs encoded putative nucleotide-binding site leucine-rich repeat (NBS-LRR) proteins, LRR-receptor-like kinase (LRR-RLK), cytochrome P450 superfamily protein, heat shock cognate protein 70, glutamate receptor, RNA-binding family protein, and unknown protein. The confirmation of resistant QTLs broadens the genetic pool of limber pine resistance germplasm for resistance breeding.

Sequencing vs. amplification for the estimation of allele dosages in sugarcane (Saccharum spp.)

AbstractPremiseDetecting single‐nucleotide polymorphisms (SNPs) in a cost‐effective way is fundamental in any plant breeding pipeline. Here, we compare three genotyping techniques for their ability to reproduce the allele dosage of SNPs of interest in sugarcane (Saccharum spp.).MethodsTo identify a reproducible technique to estimate allele dosage for the validation of SNP markers, the correlation between Flex‐Seq, kompetitive allele‐specific PCR (KASP), and genotyping‐by‐sequencing and restriction site–associated DNA sequencing (GBS+RADseq) was determined for a set of 76 SNPs. To find alternative methodologies for allele dosage estimation, the KASP and Flex‐Seq techniques were compared for the same set of SNPs. For the three techniques, a population of 53 genotypes from the diverse sugarcane panel of the Centro de Investigación de la Caña de Azúcar (Cenicaña), Colombia, was selected.ResultsThe average Pearson correlation coefficients between GBS+RADseq and Flex‐Seq, GBS+RADseq and KASP, and Flex‐Seq and KASP were 0.62 ± 0.27, 0.38 ± 0.27, and 0.38 ± 0.30, respectively.DiscussionFlex‐Seq reproduced the allele dosages determined using GBS+RADseq with good levels of precision because of its depth of sequencing and ability to target specific positions in the genome. Additionally, Flex‐Seq outperformed KASP by allowing the conversion of a higher number of SNPs and a more accurate estimation of the allele dosage. Flex‐Seq has therefore become the genotyping methodology of choice for marker validation at Cenicaña.

Enhancing the genomic prediction accuracy of swine agricultural economic traits using an expanded one-hot encoding in CNN models1

Deep learning (DL) methods like Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs) have been applied to predict the complex traits in animal and plant breeding. However, improving the genomic prediction accuracy still presents significant challenges. In this study, we applied CNNs to predict swine traits using previously published data. Specifically, we extensively evaluated the CNN model’s performance by employing various sets of Single Nucleotide Polymorphisms (SNPs) and concluded that the CNN model achieved optimal performance when utilizing SNP sets comprising 1,000 SNPs. Furthermore, we adopted a novel approach using the one-hot encoding method that transforms the 16 different genotypes into sets of eight binary variables. This innovative encoding method significantly enhanced the CNN’s prediction accuracy for swine traits, outperforming the traditional one-hot encoding techniques. Our findings suggest that the expanded one-hot encode method can improve the accuracy of DL methods in the genomic prediction of swine agricultural economic traits. This discovery has significant implications for swine breeding programs, where genomic prediction is pivotal in improving breeding strategies. Furthermore, future research endeavors can explore additional enhancements to DL methods by incorporating advanced data pre-processing techniques.

Identification of the efficacy of parentage testing based on bi-allelic autosomal single nucleotide polymorphism markers in Taiwanese population.

Parentage testing is crucial for forensic DNA analysis, using short tandem repeats (STRs). Single nucleotide polymorphisms (SNPs) with high minor allele frequency (MAF) are promising for human identification. This study aimed to develop SNP markers for parentage testing in the Taiwanese population and compare their accuracy with STRs. The TPMv1 SNP microarray (714,457 SNPs) was used to screen 180,000 Taiwanese individuals and analyze the SNP data using PLINK. After quality control, allelic distribution, and MAF considerations, a set of SNPs with significant inheritance information was selected. Parentage testing was conducted on 355 single parent-child pairs using both STRs and SNPs, employing three kinship algorithms: identity by descent, kinship-based inference for genome-wide association studies, and the combined paternity index/probability of paternity (CPI/PP). An Affymetrix signature probe for kinship testing (ASP) was also used. Based on the quality control and selection criteria, 176 SNPs with MAF > 0.4995 were selected from the Taiwanese population. The CPI/PP results calculated using SNPs were consistent with the STR results. The accuracy of the SNPs used in the single-parent-child parentage testing was > 99.99%. The set of 176 SNPs had a higher identification rate in the single parent-child parentage test than in the ASP. The CPI/PP value calculated using 176 SNPs was also more accurate than that calculated using ASP. Our findings suggest that these 176 SNPs could be used for single-parent-child parentage identification in the Taiwanese population.

Optimized Pepper Target SNP-Seq Applied in Population Structure and Genetic Diversity Analysis of 496 Pepper (Capsicum spp.) Lines.

Peppers are a major vegetable crop worldwide. With the completion of additional genome assemblies, a multitude of single-nucleotide polymorphisms (SNPs) can be utilized for population structure and genetic diversity analysis. In this study, we used target SNP-sequencing as a new high-throughput sequencing technology, screening out 425 perfect SNPs for analyzing the genetic diversity and population structure among 496 pepper lines from five pepper species in China and abroad. The perfect SNP panel exhibited commendable discriminative ability, as indicated by the average values of polymorphism information content, observed heterozygosity, minor allele frequency, and genetic diversity, which were 0.346, 0.011, 0.371, and 0.449, respectively. Based on phylogenetic, population structure, and principal component analyses, 484 C. annuum lines were divided into four subpopulations according to the shape of fruit: blocky fruit, wide-horn fruit, narrow-horn fruit, and linear fruit. These subpopulations displayed clear clustering with minimal or no overlap. Moreover, F statistic (Fst) analysis revealed considerable distinctions among these subpopulations. Additionally, we established a set of 47 core SNPs that could effectively differentiate among all pepper lines. This core SNP set could precisely classify the C. annuum lines into four distinct fruit-shape groups. The blocky and narrow-horn fruit subpopulations displayed the lowest and highest genetic diversity, respectively. This study highlights the importance of fruit shape as a crucial trait in pepper breeding. Moreover, this work indicates the immense potential of optimized target SNP technology in the addition of foreground markers of important traits to improve molecular breeding efficiency, and demonstrates its broad application prospects in the genetic analysis and variety identification of peppers.

Identification of eQTLs using different sets of single nucleotide polymorphisms associated with carcass and body composition traits in pigs

BackgroundMapping expression quantitative trait loci (eQTLs) in skeletal muscle tissue in pigs is crucial for understanding the relationship between genetic variation and phenotypic expression of carcass traits in meat animals. Therefore, the primary objective of this study was to evaluate the impact of different sets of single nucleotide polymorphisms (SNP), including scenarios removing SNPs pruned for linkage disequilibrium (LD) and SNPs derived from SNP chip arrays and RNA-seq data from liver, brain, and skeletal muscle tissues, on the identification of eQTLs in the Longissimus lumborum tissue, associated with carcass and body composition traits in Large White pigs. The SNPs identified from muscle mRNA were combined with SNPs identified in the brain and liver tissue transcriptomes, as well as SNPs from the GGP Porcine 50 K SNP chip array. Cis- and trans-eQTLs were identified based on the skeletal muscle gene expression level, followed by functional genomic analyses and statistical associations with carcass and body composition traits in Large White pigs.ResultsThe number of cis- and trans-eQTLs identified across different sets of SNPs (scenarios) ranged from 261 to 2,539 and from 29 to 13,721, respectively. Furthermore, 6,180 genes were modulated by eQTLs in at least one of the scenarios evaluated. The eQTLs identified were not significantly associated with carcass and body composition traits but were significantly enriched for many traits in the “Meat and Carcass” type QTL. The scenarios with the highest number of cis- (n = 304) and trans- (n = 5,993) modulated genes were the unpruned and LD-pruned SNP set scenarios identified from the muscle transcriptome. These genes include 84 transcription factor coding genes.ConclusionsAfter LD pruning, the set of SNPs identified based on the transcriptome of the skeletal muscle tissue of pigs resulted in the highest number of genes modulated by eQTLs. Most eQTLs are of the trans type and are associated with genes influencing complex traits in pigs, such as transcription factors and enhancers. Furthermore, the incorporation of SNPs from other genomic regions to the set of SNPs identified in the porcine skeletal muscle transcriptome contributed to the identification of eQTLs that had not been identified based on the porcine skeletal muscle transcriptome alone.

An insight into the causal relationship between sarcopenia-related traits and venous thromboembolism: A mendelian randomization study.

As a geriatric syndrome, sarcopenia has a high prevalence in the old population and represents an impaired state of health with adverse health outcomes. A strong clinical interest in its relationship with venous thromboembolism (VTE), which is a complex trait disease with a heterogeneous annual incidence rate in different countries, has emerged. The relationship between sarcopenia and venous thromboembolism has been reported in observational studies but the causality from sarcopenia to VTE remained unclarified. We aimed to assess the causal effect of sarcopenia on the risk of VTE with the two-sample Mendelian randomization (MR) method. Two sets of single-nucleotide polymorphisms (SNPs), derived from two published genome-wide association study (GWAS) meta-analyses and genetically indexing muscle weakness and lean muscle mass separately, were pooled into inverse variance weighted (IVW), weighted median and MR-Egger analyses. No evidence was found for the causal effect of genetically predicted muscle weakness (IVW: OR = 0.90, 95% CI = 0.76-1.06, p = 0.217), whole body lean mass (IVW: OR = 1.01, 95% CI = 0.87-1.17, p = 0.881) and appendicular lean mass (IVW: OR = 1.13, 95% CI = 0.82-1.57, p = 0.445) on the risk of VTE. However, both genetically predicted whole-body lean mass and appendicular lean mass can causally influence diabetes mellitus (IVW of whole-body lean mass: OR = 0.87, 95% CI = 0.78-0.96, p = 0.008; IVW of appendicular lean mass: OR = 0.71, 95% CI = 0.54-0.94, p = 0.014) and hypertension (IVW of whole-body lean mass: OR = 0.92, 95% CI = 0.87-0.98, p = 0.007; IVW of appendicular lean mass: OR = 0.84, 95% CI = 0.73-0.96, p = 0.013). Genetically predicted sarcopenia does not causally influence VTE directly, but it might still have an indirect effect on VTE incidence via diabetes mellitus and hypertension.

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.