Single Nucleotide Polymorphism Genotype Data Research Articles

The genomics of adaptation to climate in European great tit (Parus major) populations.

The recognition that climate change is occurring at an unprecedented rate means that there is increased urgency in understanding how organisms can adapt to a changing environment. Wild great tit (Parus major) populations represent an attractive ecological model system to understand the genomics of climate adaptation. They are widely distributed across Eurasia and they have been documented to respond to climate change. We performed a Bayesian genome-environment analysis, by combining local climate data with single nucleotide polymorphisms genotype data from 20 European populations (broadly spanning the species' continental range). We found 36 genes putatively linked to adaptation to climate. Following an enrichment analysis of biological process Gene Ontology (GO) terms, we identified over-represented terms and pathways among the candidate genes. Because many different genes and GO terms are associated with climate variables, it seems likely that climate adaptation is polygenic and genetically complex. Our findings also suggest that geographical climate adaptation has been occurring since great tits left their Southern European refugia at the end of the last ice age. Finally, we show that substantial climate-associated genetic variation remains, which will be essential for adaptation to future changes.

Rs11726196 Single-Nucleotide Polymorphism of the Transient Receptor Potential Canonical 3 (TRPC3) Gene Is Associated with Chronic Pain.

Chronic pain is reportedly associated with the transient receptor potential canonical 3 (TRPC3) gene. The present study examined the genetic associations between the single-nucleotide polymorphisms (SNPs) of the TRPC3 gene and chronic pain. The genomic samples from 194 patients underwent linkage disequilibrium (LD) analyses of 29 SNPs within and around the vicinity of the TRPC3 gene. We examined the associations between the SNPs and the susceptibility to chronic pain by comparing the genotype distribution of 194 patients with 282 control subjects. All SNP genotype data were extracted from our previous whole-genome genotyping results. Twenty-nine SNPs were extracted, and a total of four LD blocks with 15 tag SNPs were observed within and around the TRPC3 gene. We further analyzed the associations between these tag SNPs and chronic pain. The rs11726196 SNP genotype distribution of patients was significantly different from the control subjects even after multiple-testing correction with the number of SNPs. The TT + TG genotype of rs11726196 is often carried by chronic pain patients, suggesting a causal role for the T allele. These results contribute to our understanding of the genetic risk factors for chronic pain.

Association of Human Leukocyte Antigen Variants with Progression of Monoclonal Gammopathy of Undetermined Significance to Multiple Myeloma

Background: Multiple Myeloma (MM), a neoplasm of terminally differentiated plasma cells that produce a monoclonal immunoglobulin, represents 10% of hematologic malignancies. Virtually all MM arises from monoclonal gammopathy of undetermined significance (MGUS), an asymptomatic premalignant phase, at a rate of progression of about 1% annually. However, the risk of progression is not uniform as some individuals with MGUS could have progression rates as high as 58% in 20 years. Novel predictive markers are critically needed to accurately identify MGUS patients at high-risk of malignant transformation, so that early intervention can be implemented to prevent end-organ damage associated with frank myeloma. Genome-wide association studies (GWAS) have repeatedly identified loci at the major histocompatibility complex (MHC) that are associated with the risk of many cancers including MM. However, the impact of specific MHC loci on the MGUS-to-MM progression risk has not been elucidated. Methods: We used the Illumina Infinium Global Screening Array to scan the buffy coat samples of 259 MGUS and 654 MM patients enrolled from the Medical College of Wisconsin (MCW) Tissue Bank. All MGUS and MM patients have been chart reviewed and confirmed diagnosis. After a standard quality control (QC), 249 MGUS and 619 MM patients with 11,882 genotyped single nucleotide polymorphisms (SNPs) in the MHC region (20 to 40 mb at chromosome 6) were used for analysis. The SNP2HLA v1.0.3 software and HLA*IMP:03 were applied to impute classical human leukocyte antigen (HLA) alleles at a four-digit resolution based on SNP genotyping. SNP2HLA uses Beagle to impute all missing SNPs, classical HLA alleles, and amino acid polymorphisms to the HapMap reference panel. HLA*IMP:03 uses the Michigan Imputation Server to impute phased haplotypes to a multi-ethnic panel of 10,561 individuals with SNP genotype data and lab-based typing of alleles at 11 HLA genes. A post-imputation QC was conducted to remove variants with low imputation quality score (R2 < 0.3). Logistic regression analysis was performed using PLINK, a whole genome association analysis toolset, which adjusted for age, race, sex, and top principal components. Results: As evidenced by the quantile-quantile (QQ) plot (Figure 1), there was no evidence for hidden substructure or cryptic relatedness in the GWAS (λ=1.02). The final set of imputed variants from both tools used in association analysis were of high quality (R2 ≥0.9). Although we did not observe any HLA variants showing associations with the progression of MGUS to MM at Bonferroni adjusted significance level for SNP2HLA (P = 0.05/5865 = 8.53 × 10-6) and for HLA*IMP:03 (P = 0.05/215 = 2.33 × 10-4), we found 4 classical HLA alleles and 30 HLA Amino Acids were associated with MGUS progression at a P-value of < 0.05 via association analysis with SNP2HLA. Association analysis for HLA*IMP:03 imputation data identified 8 classical HLA alleles were associated with MGUS progression at P value < 0.05. Among them, two classical HLA alleles were identified in both tools, they are HLA_B*08:01 (odds ratio, OR=0.38, 95% confidence interval, CI =0.19-0.77 and OR=0.43, 95% CI=0.21-0.86 for SNP2HLA and HLA*IMP:03, respectively) and HLA_C*07:01 (OR=0.61, 95% CI=0.41-0.89 and OR=0.63, 95% CI=0.41-0.98 for SNP2HLA and HLA*IMP:03, respectively) (Table 1). Conclusions: These results implicate that HLA alleles may contribute to the progression of MGUS to MM. Further studies are needed to validate our findings with larger sample sizes and explore the biological mechanisms. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

G-DIRT: a web server for identification and removal of duplicate germplasms based on identity-by-state analysis using single nucleotide polymorphism genotyping data.

Maintaining duplicate germplasms in genebanks hampers effective conservation and utilization of genebank resources. The redundant germplasm adds to the cost of germplasm conservation by requiring a large proportion of the genebank financial resources towards conservation rather than enriching the diversity. Besides, genome-wide-association analysis using an association panel with over-represented germplasms can be biased resulting in spurious marker-trait associations. The conventional methods of germplasm duplicate removal using passport information suffer from incomplete or missing passport information and data handling errors at various stages of germplasm enrichment. This limitation is less likely in the case of genotypic data. Therefore, we developed a web-based tool, Germplasm Duplicate Identification and Removal Tool (G-DIRT), which allows germplasm duplicate identification based on identity-by-state analysis using single-nucleotide polymorphism genotyping information along with pre-processing of genotypic data. A homozygous genotypic difference threshold of 0.1% for germplasm duplicates has been determined using tetraploid wheat genotypic data with 94.97% of accuracy. Based on the genotypic difference, the tool also builds a dendrogram that can visually depict the relationship between genotypes. To overcome the constraint of high-dimensional genotypic data, an offline version of G-DIRT in the interface of R has also been developed. The G-DIRT is expected to help genebank curators, breeders and other researchers across the world in identifying germplasm duplicates from the global genebank collections by only using the easily sharable genotypic data instead of physically exchanging the seeds or propagating materials. The web server will complement the existing methods of germplasm duplicate identification based on passport or phenotypic information being freely accessible at http://webtools.nbpgr.ernet.in/gdirt/.

Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits.

As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.

Maintenance of divergent lineages of the Rice Blast Fungus Pyricularia oryzae through niche separation, loss of sex and post-mating genetic incompatibilities.

Many species of fungal plant pathogens coexist as multiple lineages on the same host, but the factors underlying the origin and maintenance of population structure remain largely unknown. The rice blast fungus Pyricularia oryzae is a widespread model plant pathogen displaying population subdivision. However, most studies of natural variation in P. oryzae have been limited in genomic or geographic resolution, and host adaptation is the only factor that has been investigated extensively as a contributor to population subdivision. In an effort to complement previous studies, we analyzed genetic and phenotypic diversity in isolates of the rice blast fungus covering a broad geographical range. Using single-nucleotide polymorphism genotyping data for 886 isolates sampled from 152 sites in 51 countries, we showed that population subdivision of P. oryzae in one recombining and three clonal lineages with broad distributions persisted with deeper sampling. We also extended previous findings by showing further population subdivision of the recombining lineage into one international and three Asian clusters, and by providing evidence that the three clonal lineages of P. oryzae were found in areas with different prevailing environmental conditions, indicating niche separation. Pathogenicity tests and bioinformatic analyses using an extended set of isolates and rice varieties indicated that partial specialization to rice subgroups contributed to niche separation between lineages, and differences in repertoires of putative virulence effectors were consistent with differences in host range. Experimental crosses revealed that female sterility and early post-mating genetic incompatibilities acted as strong additional barriers to gene flow between clonal lineages. Our results demonstrate that the spread of a fungal pathogen across heterogeneous habitats and divergent populations of a crop species can lead to niche separation and reproductive isolation between distinct, widely distributed, lineages.

Abstract 5883: Development and testing of a polygenic risk score for proliferative breast cancers

Abstract Background: Identification of women at elevated risk for highly proliferative, poor prognosis breast cancers could have important implications for screening and prevention. Genome-wide association studies (GWAS) have found &amp;gt;200 single nucleotide polymorphisms (SNPs) associated with breast cancer risk, with many SNPs differentially associated with ER status or intrinsic subtype. We hypothesized that some of these SNPs are preferentially associated with more proliferative tumors, while others are preferentially associated with less proliferative tumors. In this study, we aimed to build a polygenic risk score (PRS) predictive of proliferative tumors, using the GWAS-identified SNPs. Methods: We used data from 3 studies that included array-based SNP genotyping and tumor transcriptomic data: The Cancer Genome Atlas, METABRIC, and the I-SPY 2 Trial (total n=2,467). Our outcome was the risk of recurrence score weighted on proliferation (ROR-P), a validated tumor prognostic signature. Using the breast cancer risk SNPs, we built respective linear regression models to predict ROR-P, with genetic ancestry, study, and ER status as covariates. We performed 5-fold cross-validation and used the model r2 to identify the optimal p-value threshold for including SNPs in the PRS. To decrease uncertainty of our estimates, we performed 100 repeats across the pooled datasets. To test whether this model predicted poor prognosis breast cancers, we first used it to impute ROR-P among genotyped breast cancer cases in UK Biobank (UKB). We then examined the association between “genetically predicted” ROR-P and breast cancer-specific survival using Cox proportional hazards models adjusted for genetic ancestry and age at diagnosis. Results: Associations between 224 breast cancer SNPs and ROR-P were tested. The best-performing model in cross-validation contained 96 SNPs, each associated with ROR-P at p &amp;lt; 0.45, with model r2 0.054. The SNPs with the strongest positive correlations with ROR-P included those discovered in GWAS for HER2-positive and ER-negative cancers, both of which tend to be highly proliferative. Among 7,247 incident cancers in UKB, higher genetically predicted ROR-P was associated with shorter survival, with a per-standard deviation hazard ratio of 1.14 (95% CI 1.05-1.24, p = 0.002). Conclusions: We used breast cancer susceptibility SNPs to construct a PRS fitted to ROR-P, a prognostic signature recapitulating tumor proliferation. This PRS was associated with worse clinical outcomes in breast cancer cases from UKB. Our results suggest that correlations between SNPs and tumor gene expression can be used to “tune” PRS to tumor phenotype, e.g. proliferation. Highly proliferative tumors are more likely to present as advanced cancers even among women getting routine screening. If replicated in other datasets, our findings could be used to identify women who may especially benefit from tailored screening and prevention. Citation Format: Yiwey Shieh, Jacquelyn Roger, Scott Huntsman, Donglei Hu, Jovia L. Nierenberg, Pooja Middha Kapoor, Christina Yau, Gillian Hirst, Laura van 't Veer, Laura Esserman. Development and testing of a polygenic risk score for proliferative breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5883.

Whole-genome sequencing reveals novel ethnicity-specific rare variants associated with Alzheimer’s disease

Alzheimer’s disease (AD) is the most common multifactorial neurodegenerative disease among elderly people. Genome-wide association studies (GWAS) have been highly successful in identifying genetic risk factors. However, GWAS investigate common variants, which tend to have small effect sizes, and rare variants with potentially larger phenotypic effects have not been sufficiently investigated. Whole-genome sequencing (WGS) enables us to detect those rare variants. Here, we performed rare-variant association studies by using WGS data from 140 individuals with probable AD and 798 cognitively normal elder controls (CN), as well as single-nucleotide polymorphism genotyping data from an independent large Japanese AD cohort of 1604 AD and 1235 CN subjects. We identified two rare variants as candidates for AD association: a missense variant in OR51G1 (rs146006146, c.815 G > A, p.R272H) and a stop-gain variant in MLKL (rs763812068, c.142 C > T, p.Q48X). Subsequent in vitro functional analysis revealed that the MLKL stop-gain variant can contribute to increases not only in abnormal cells that should die by programmed cell death but do not, but also in the ratio of Aβ42 to Aβ40. We further detected AD candidate genes through gene-based association tests of rare variants; a network-based meta-analysis using these candidates identified four functionally important hub genes (NCOR2, PLEC, DMD, and NEDD4). Our findings will contribute to the understanding of AD and provide novel insights into its pathogenic mechanisms that can be used in future studies.

The Relationship Between Low-Density Lipoprotein Cholesterol and Progression of Mild Cognitive Impairment: The Influence of rs6859 in PVRL2.

Background: Genome-wide association studies have identified many Alzheimer’s disease (AD) genetic-risk single nucleotide polymorphisms (SNPs) and indicated the important role of the cholesterol/lipid metabolism pathway in AD pathogenesis. This study aims to investigate the effects of cholesterol and genetic risk factors on progression of mild cognitive impairment (MCI) to AD. Methods: We prospectively followed 316 MCI participants aged ≥50 years with a baseline cholesterol profile and SNP genotyping data for 4.5 years on average in a sub-cohort of the Shanghai Aging Study. Total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol in serum were measured at baseline. SNP genotyping was performed using a MassARRAY system. At follow-up, consensus diagnosis of incident dementia and AD were established based on medical, neurological, and neuropsychological examinations. Cox regression models were used to assess the association of cholesterol and SNP with incident AD. Results: The AG/AA genotypes of PVRL2 rs6859 were significantly associated with increased incident AD in MCI participants, compared with GG genotype (adjusted hazard ratio [HR] 2.75, 95% confidence interval [CI] 1.32–5.76, p = .007, false discovery rate–adjusted p = .030). In PVRL2 rs6859 AG/AA carriers, each-1 mmol/L higher level of LDL-C was significantly associated with a 48% decreased risk of AD (adjusted HR 0.52, 95%CI 0.33–0.84, p = .007). Consistent results were obtained when using LDL-C as the categorical variable (P for trend = 0.016). Conclusion: The relationship between LDL-C and progression of MCI may be influenced by genetic variants.

On the Different Ways to Handle the Trend of Disease Risk in Genetic Association Tests

Genetic association studies usually apply the simple chi-square (χ2)-test for testing association between a single-nucleotide polymorphism (SNP) and a particular phenotype, assuming the genotypes and phenotypes are independent. So, the conventional χ2-test does not consider the increased risk of an individual carrying the increasing number of disease responsible allele (a particular genotype). But, the association tests should be performed with the consideration of this disease risk according to the mode of inheritance (additive, dominant, recessive). Practical demonstration of the two possible methods for considering such order or trends in contingency tables of genetic association studies using SNP genotype data is the purpose of this paper. One method is by pooling the genotypes, and the other is scoring the individual genotypes, based on the disease risk according to the inheritance pattern. The results show that the p-values obtained from both the methods are similar for the dominant and recessive models. The other important features of the methods were also extracted using the SNP genotype data for different inheritance patterns.

A Preliminary Outline of the Statistical Inference Process in Genetic Association Studies

The genome-wide association study (GWAS) is a powerful experimental design that is applied to detect disease susceptible genetic variants. The main goal of these studies is to provide a better understanding of the biology of disease, which further facilitates prevention or better treatment. A statistical inferential process is finally carried out in this study, where an association is usually observed between the single-nucleotide polymorphism (SNPs) and the traits in a case-control setting. To detect the disease responsible loci correctly, the investigation of the statistical association should be carefully conducted along with the other necessary steps. This research provides an introductory guideline for conducting such statistical association tests for these studies using SNP genotype data.

Novel role of prostate cancer risk variant rs7247241 on PPP1R14A isoform transition through allelic TF binding and CpG methylation.

Although previous studies identified numerous single nucleotide polymorphisms (SNPs) and their target genes predisposed to prostate cancer (PrCa) risks, SNP-related splicing associations are rarely reported. In this study, we applied distance-based sQTL analysis (sQTLseekeR) using RNA-seq and SNP genotype data from benign prostate tissue (n = 467) and identified significant associations in 3344 SNP-transcript pairs (P ≤ 0.05) at PrCa risk loci. We characterized a common SNP (rs7247241) and its target gene (PPP1R14A) located in chr19q13, an sQTL with risk allele T associated with upregulation of long isoform (P = 9.99E-7). We confirmed the associations in both TCGA (P = 2.42E-24) and GTEX prostate cohorts (P = 9.08E-78). To functionally characterize this SNP, we performed chromatin immunoprecipitation qPCR and confirmed stronger CTCF and PLAGL2 binding in rs7247241 C than T allele. We found that CTCF binding enrichment was negatively associated with methylation level at the SNP site in human cell lines (r = -0.58). Bisulfite sequencing showed consistent association of rs7247241-T allele with nearby sequence CpG hypermethylation in prostate cell lines and tissues. Moreover, the methylation level at CpG sites nearest to the CTCF binding and first exon splice-in (ψ) of PPP1R14A was significantly associated with aggressive phenotype in the TCGA PrCa cohort. Meanwhile, the long isoform of the gene also promoted cell proliferation. Taken together, with the most updated gene annotations, we reported a set of sQTL associated with multiple traits related to human prostate diseases and revealed a unique role of PrCa risk SNP rs7247241 on PPP1R14A isoform transition.

The Contribution of Copy Number Variants and Single Nucleotide Polymorphisms to the Additive Genetic Variance of Carcass Traits in Cattle.

The relative contributions of both copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) to the additive genetic variance of carcass traits in cattle is not well understood. A detailed understanding of the relative importance of CNVs in cattle may have implications for study design of both genomic predictions and genome-wide association studies. The first objective of the present study was to quantify the relative contributions of CNV data and SNP genotype data to the additive genetic variance of carcass weight, fat, and conformation for 945 Charolais, 923 Holstein-Friesian, and 974 Limousin sires. The second objective was to jointly consider SNP and CNV data in a least absolute selection and shrinkage operator (LASSO) regression model to identify genomic regions associated with carcass weight, fat, and conformation within each of the three breeds separately. A genomic relationship matrix (GRM) based on just CNV data did not capture any variance in the three carcass traits when jointly evaluated with a SNP-derived GRM. In the LASSO regression analysis, a total of 987 SNPs and 18 CNVs were associated with at least one of the three carcass traits in at least one of the three breeds. The quantitative trait loci (QTLs) corresponding to the associated SNPs and CNVs overlapped with several candidate genes including previously reported candidate genes such as MSTN and RSAD2, and several potential novel candidate genes such as ACTN2 and THOC1. The results of the LASSO regression analysis demonstrated that CNVs can be used to detect associations with carcass traits which were not detected using the set of SNPs available in the present study. Therefore, the CNVs and SNPs available in the present study were not redundant forms of genomic data.

Population Structure and Genetic Diversity of Two-Rowed Barley Accessions from Kazakhstan Based on SNP Genotyping Data.

The genetic relationship and population structure of two-rowed barley accessions from Kazakhstan were assessed using single-nucleotide polymorphism (SNP) markers. Two different approaches were employed in the analysis: (1) the accessions from Kazakhstan were compared with barley samples from six different regions around the world using 1955 polymorphic SNPs, and (2) 94 accessions collected from six breeding programs from Kazakhstan were studied using 5636 polymorphic SNPs using a 9K Illumina Infinium assay. In the first approach, the neighbor-joining tree showed that the majority of the accessions from Kazakhstan were grouped in a separate subcluster with a common ancestral node; there was a sister subcluster that comprised mainly barley samples that originated in Europe. The Pearson’s correlation analysis suggested that Kazakh accessions were genetically close to samples from Africa and Europe. In the second approach, the application of the STRUCTURE package using 5636 polymorphic SNPs suggested that Kazakh barley samples consisted of five subclusters in three major clusters. The principal coordinate analysis plot showed that, among six breeding origins in Kazakhstan, the Krasnovodopad (KV) and Karaganda (KA) samples were the most distant groups. The assessment of the pedigrees in the KV and KA samples showed that the hybridization schemes in these breeding stations heavily used accessions from Ethiopia and Ukraine, respectively. The comparative analysis of the KV and KA samples allowed us to identify 214 SNPs with opposite allele frequencies that were tightly linked to 60 genes/gene blocks associated with plant adaptation traits, such as the heading date and plant height. The identified SNP markers can be efficiently used in studies of barley adaptation and deployed in breeding projects to develop new competitive cultivars.

HandyCNV: Standardized Summary, Annotation, Comparison, and Visualization of Copy Number Variant, Copy Number Variation Region, and Runs of Homozygosity.

Detection of CNVs (copy number variants) and ROH (runs of homozygosity) from SNP (single nucleotide polymorphism) genotyping data is often required in genomic studies. The post-analysis of CNV and ROH generally involves many steps, potentially across multiple computing platforms, which requires the researchers to be familiar with many different tools. In order to get around this problem and improve research efficiency, we present an R package that integrates the summarization, annotation, map conversion, comparison and visualization functions involved in studies of CNV and ROH. This one-stop post-analysis system is standardized, comprehensive, reproducible, timesaving, and user-friendly for researchers in humans and most diploid livestock species.

BGISEQ-500RS sequencing of a 448-plex SNP panel for forensic individual identification and kinship analysis

Next generation sequencing (NGS)-based single nucleotide polymorphism (SNP) genotyping is widely used in the field of forensics. SNP genotyping data from several NGS platforms have been published, but forensic application trials of DNA nanoball sequencing platforms have been very limited. In this work, we developed a 448-plex SNP panel on the BGISEQ-500RS platform. The sequencing metrics of a total of 261 samples that were sequenced with this panel are reported in detail. The average sequencing depth was 8373 × and the average heterozygosity of the 448-plex assay was 0.85. Sensitivity analysis showed that 325 SNPs were successfully genotyped with as little as 50 pg of genomic DNA, with the mean quality score of the sequencing data above Q30. Forensic parameters were calculated based on the data of 142 unrelated Chinese Han individuals and the combined matching probability was as low as 5.21 × 10−101. Kinship analyses based on experiments and computer simulations showed that the 448-panel was as effective as the ForenSeq™ DNA Signature Prep Kit for second-degree kinship identification, and when the two panels were merged, the related pairs were almost completely distinguished from unrelated pairs. The 448-plex SNP panel on the BGISEQ-500RS platform provides a powerful tool for forensic individual identification and kinship analysis.

Investigation of high correlation with carcass traits of SNPs of the PLCB1, C/EBPα, and TDRKH genes and the combinations of SNPs using the MDR method in the Hanwoo.

Recently, many researchers focus on the best way to produce high-quality meat, as the trend in food consumption today is to focus on quality. In general, consumers' preferences in beef differ depending on taste and meatiness. Therefore, researchers are interested in how the marbling score affects the flavors of meat or the various factors that make up the meatiness to captivate the consumers' tastes. This study identifies single nucleotide polymorphisms (SNPs) or gene combinations that affect the carcass traits of Korean cattle (Hanwoo) by using the multifactor dimensionality reduction (MDR) method. We collected the candidate SNPs to identify SNPs related to marbling scores from whole-exome sequencing and bovine SNP genotyping data. Using 96 Hanwoo samples, we performed PCR amplification to investigate the polymorphism status. In addition, we investigated genetic relationships between carcass traits and SNPs using 612 Hanwoo samples. Furthermore, each candidate SNP genotype and the combinations of SNP genotypes were verified to improve the accuracy of genetic relationships using MDR method. Twenty-four candidate SNPs associated with carcass trait and marbling scores were identified from SNP genotyping and whole-exome sequencing. Among them, three SNP markers (c.459T > C of the PLCB1 gene, c.271 A > C of the C/EBPα gene, and g.17257 A > G of the TDRKH gene) were showed statistically significant differences between intramuscular fat and genotypes. Especially, two candidate SNPs, including c.459T > C located in the PLCB1 gene and c.271 A > C located in the C/EBPα gene, could be highly associated with the intramuscular fat of Hanwoo quality grade. In addition, the combination of SNP genotypes is showed higher significant differences with carcass weight, backfat thickness, and longissimus dorsi muscle area. Three SNP genotypes and the combination of SNP genotypes in the PLCB1, C/EBPα, and TDRKH genes may be useful genetic markers for improving beef quality.

Sample identification and pedigree reconstruction in Wolverine (Gulo gulo) using SNP genotyping of non-invasive samples

For conservation genetic studies using non-invasively collected samples, genome-wide data may be hard to acquire. Until now, such studies have instead mostly relied on analyses of traditional genetic markers such as microsatellites (SSRs). Recently, high throughput genotyping of single nucleotide polymorphisms (SNPs) has become available, expanding the use of genomic methods to include non-model species of conservation concern. We have developed a 96-marker SNP array for use in applied conservation monitoring of the Scandinavian wolverine (Gulo gulo) population. By genotyping more than a thousand non-invasively collected samples, we were able to obtain precise estimates of different types of genotyping errors and sample dropout rates. The SNP panel significantly outperforms the SSR markers (and DBY intron markers for sexing) both in terms of precision in genotyping, sex assignment and individual identification, as well as in the proportion of samples successfully genotyped. Furthermore, SNP genotyping offers a simplified laboratory and analysis pipeline with fewer samples needed to be repeatedly genotyped in order to obtain reliable consensus data. In addition, we utilised a unique opportunity to successfully demonstrate the application of SNP genotype data for reconstructing pedigrees in wild populations, by validating the method with samples from wild individuals with known relatedness. By offering a simplified workflow with improved performance, we anticipate this methodology will facilitate the use of non-invasive samples to improve genetic management of many different types of populations that have previously been challenging to survey.

Effects of single nucleotide polymorphism ascertainment on population structure inferences.

Single nucleotide polymorphism (SNP) data are widely used in research on natural populations. Although they are useful, SNP genotyping data are known to contain bias, normally referred to as ascertainment bias, because they are conditioned by already confirmed variants. This bias is introduced during the genotyping process, including the selection of populations for novel SNP discovery and the number of individuals involved in the discovery panel and selection of SNP markers. It is widely recognized that ascertainment bias can cause inaccurate inferences in population genetics and several methods to address these bias issues have been proposed. However, especially in natural populations, it is not always possible to apply an ideal ascertainment scheme because natural populations tend to have complex structures and histories. In addition, it was not fully assessed if ascertainment bias has the same effect on different types of population structure. Here, we examine the effects of bias produced during the selection of population for SNP discovery and consequent SNP marker selection processes under three demographic models: the island, stepping-stone, and population split models. Results show that site frequency spectra and summary statistics contain biases that depend on the joint effect of population structure and ascertainment schemes. Additionally, population structure inferences are also affected by ascertainment bias. Based on these results, it is recommended to evaluate the validity of the ascertainment strategy prior to the actual typing process because the direction and extent of ascertainment bias vary depending on several factors.

The number of SNPs required for distinguishing Japanese from other East Asians.

In some cases, it is necessary to estimate the national origin of an unknown subject in forensic medicine. The use of single nucleotide polymorphism (SNP) markers appears to be very effective for this purpose, since genome-wide SNP genotype data of many human populations are publicly available. In this study, we examined the number of SNPs that could objectively and accurately distinguish Japanese subjects (1KG-JPT) from the other East Asians (1KG-CDX, -CHB, -CHS, and -KHV) using the combination of principal component analysis and hierarchical cluster analysis. A computer simulation showed that approximately 3000 randomly selected SNPs were enough for the discrimination. Our results suggest that at least a 0.024% coverage is needed in the next generation sequencing experiment to objectively determine whether an unknown person is Japanese or not if the amount of DNA sample from him/her is insufficient or the quality is low.