Sequence Single Nucleotide Polymorphism Research Articles

Review of SNP assays for disaster victim identification: Cost, time, and performance information for decision-makers.

In mass disaster events, forensic DNA laboratories may be called upon to quickly pivot their operations toward identifying bodies and reuniting remains with family members. Ideally, laboratories have considered this possibility in advance and have a plan in place. Compared with traditional short tandem repeat (STR) typing, single nucleotide polymorphisms (SNPs) may be better suited to these disaster victim identification (DVI) scenarios due to their small genomic target size, resulting in an improved success rate in degraded DNA samples. As the landscape of technology has shifted toward DNA sequencing, many forensic laboratories now have benchtop instruments available for massively parallel sequencing (MPS), facilitating this operational pivot from routine forensic STR casework to DVI SNP typing. Herein, we present the commercially available SNP sequencing assays amenable to DVI, we use data simulations to explore the potential for kinship prediction from SNP panels of varying sizes, and we give an example DVI scenario as context for presenting the matrix of considerations: kinship predictive potential, cost, and throughput of current SNP assay options. This information is intended to assist laboratories in choosing a SNP system for disaster preparedness.

Interferon Regulatory Factor 5 Gene Polymorphisms and mRNA Expression Levels Are Associated with Neuromyelitis Optica Spectrum Disorder.

Interferon regulatory factor 5 (IRF5) is a critical transcription factor in the toll-like receptor signaling pathway. It is associated with autoimmune disorders, such as rheumatoid arthritis, systemic lupus erythematosus, and inflammatory bowel disease. However, the relationship between the functional single nucleotide polymorphisms (SNPs) of IRF5 and its mRNA expression level in patients with neuromyelitis optica spectrum disorder remains unclear. The present study aimed to investigate the relationship between polymorphisms and mRNA expression levels of the IRF5 gene with the incidence of neuromyelitis optica spectrum disorder (NMOSD) in northern Chinese Han people. Two loci of the IRF5 gene (rs2004640 and rs2280714) of 164 patients with NMOSD and 269 healthy subjects were genotyped using the multiple SNaPshot technique. The frequencies of alleles, genotypes, and haplotypes were compared. Stratified analysis was performed according to age, sex, AQP4 status, onset age, and Expanded Disability Status Scale (EDSS) score. The IRF5 mRNA levels in peripheral blood mononuclear cells (PBMCs) of 64 NMOSD patients (32 patients in the acute stage and 32 patients in the remission stage) and 35 healthy subjects were detected by real-time PCR. The association of SNP polymorphisms with the mRNA expression level was determined by nonparametric tests. Allele and genotype frequency distributions of rs2004640 showed significant differences between both groups. Compared to healthy controls, the frequency of rs2004640 T allele markedly increased in patients (OR = 1.51, 95% CI = 1.09-2.08, P = 0.005). Minor allele T and GT genotype of rs2004640 that significantly increases the risk of NMOSD were discovered using genetic inheritance models (codominant, dominant, and overdominant) and haplotype analyses. Subsequent haplotype analyses revealed that the major haplotype "T-A" containing the risk alleles (the SNP sequence of the alleles was rs2004640 and rs2280714) had adverse effects on NMOSD. Based on the stratification analysis according to the EDSS score, the GT genotype frequency in the EDSS ≥ 4 group (38.2%) was markedly lower than that in the EDSS < 4 group (61.8%) (OR = 0.32, 95% CI = 0.15-0.68, P = 0.0054), with a significant difference. The IRF5 mRNA expression level was increased in NMOSD patients compared to that in normal subjects. IRF5 gene polymorphisms may be tightly associated with the genesis and progression of NMOSD in northern Chinese Han people. IRF5 mRNA expression was increased in patients with NMOSD and significantly increased in patients with acute phase. Perhaps IRF5 expression levels can be used as a predictor of disease activity in the future.

A novel feature extraction technique for the clustering of SNP data

In this work, we proposed a novel feature extraction technique for the Single NucleotidePolymorphism (SNP) data, which converts an SNP sequence into the numerical form. Later, we performed the clustering analysis on the feature vectors to group similar SNP sequences in one group.

High-throughput screening of glucocorticoid-induced enhancer activity reveals mechanisms of stress-related psychiatric disorders.

Exposure to stressful life events increases the risk for psychiatric disorders. Mechanistic insight into the genetic factors moderating the impact of stress can increase our understanding of disease processes. Here, we test 3,662 single nucleotide polymorphisms (SNPs) from preselected expression quantitative trait loci in massively parallel reporter assays to identify genetic variants that modulate the activity of regulatory elements sensitive to glucocorticoids, important mediators of the stress response. Of the tested SNP sequences, 547 were located in glucocorticoid-responsive regulatory elements of which 233 showed allele-dependent activity. Transcripts regulated by these functional variants were enriched for those differentially expressed in psychiatric disorders in the postmortem brain. Phenome-wide Mendelian randomization analysis in 4,439 phenotypes revealed potentially causal associations specifically in neurobehavioral traits, including major depression and other psychiatric disorders. Finally, a functional gene score derived from these variants was significantly associated with differences in the physiological stress response, suggesting that these variants may alter disease risk by moderating the individual set point of the stress response.

Association between fibrosis-related gene polymorphism and long-term allograft outcome in renal transplant recipients

BackgroundRenal allograft fibrosis is one of characteristic causes of long-term renal function loss. The purpose of our study is to investigate the association between fibrosis-related genes single nucleotide polymorphism (SNPs) and kidney function in 5 years after kidney transplantation.MethodsA total of 143 recipients were eligible for screening with 5-year follow-up information and SNP sequencing information from blood samples were included in this study. Minor Allele Frequency (MAF) and Hardy–Weinberg Equilibrium (HWE) analysis was conducted to identify tagger single-nucleotide polymorphisms (SNPs) and haplotypes. SNPs associated with the fifth year chronic kidney disease (CKD) staging were screened by SPSS and the “SNPassoc” package in RStudio and used for subsequent prediction model construction.ResultsA total of 275 renal transplant-related SNPs identified after target sequencing analysis. 64 Tagger SNPs were selected, and two SNPs (rs13969 and rs243849) were statistically significant for stage of CKD in 5 years. Finally, a model based on Gender, Age, rs1396, and rs243849 was constructed by multivariate linear regression analysis. Additionally, this model has a good performance in predicting uremia five years after kidney transplantation.ConclusionTwo SNPs (rs13969 and rs243849) were identified to be significantly associated with long-term renal allograft function. Based on this, a prediction model for long-term allograft function was established containing Gender, Age, rs1396, and rs243849. However, an independent cohort should be enrolled to validate the predicting performance.

Identification of a regulatory pathway governing TRAF1 via an arthritis-associated non-coding variant

TRAF1/C5 was among the first loci shown to confer risk for inflammatory arthritis in the absence of an associated coding variant, but its genetic mechanism remains undefined. Using Immunochip data from 3,939 patients with juvenile idiopathic arthritis (JIA) and 14,412 control individuals, we identified 132 plausible common non-coding variants, reduced serially by single-nucleotide polymorphism sequencing (SNP-seq), electrophoretic mobility shift, and luciferase studies to the single variant rs7034653 in the third intron of TRAF1. Genetically manipulated experimental cells and primary monocytes from genotyped donors establish that the risk G allele reduces binding of Fos-related antigen 2 (FRA2), encoded by FOSL2, resulting in reduced TRAF1 expression and enhanced tumor necrosis factor (TNF) production. Conditioning on this JIA variant eliminated attributable risk for rheumatoid arthritis, implicating a mechanism shared across the arthritis spectrum. These findings reveal that rs7034653, FRA2, and TRAF1 mediate a pathway through which a non-coding functional variant drives risk of inflammatory arthritis in children and adults.

Resistance to Powdery Mildew Is Conferred by Different Genetic Loci at the Adult-Plant and Seedling Stages in Winter Wheat Line Tianmin 668.

Winter wheat line Tianmin 668 was crossed with susceptible cultivar Jingshuang 16 to develop 216 recombinant inbred lines (RILs) for dissecting its adult-plant resistance (APR) and all-stage resistance (ASR) against powdery mildew. The RIL population was genotyped on a 16K genotyping by target sequencing single-nucleotide polymorphism array and phenotyped in six field trials and in the greenhouse. Three loci-QPmtj.caas-2BL, QPmtj.caas-2AS, and QPmtj.caas-5AL-conferring APR to powdery mildew were detected on chromosomes 2BL, 2AS, and 5AL, respectively, of Tianmin 668. The effect of resistance to powdery mildew for QPmtj.caas-2BL was greater than that of the other two loci. A Kompetitive allele-specific PCR marker specific for QPmtj.caas-2BL was developed and verified on 402 wheat cultivars or breeding lines. Results of virulence and avirulence patterns to 17 Blumeria graminis f. sp. tritici isolates, bulked segregant analysis-RNA-sequencing, and a genetic linkage mapping identified a resistance allele at locus Pm4 in Tianmin 668 based on the seedling phenotypes of the RIL population. The PCR-based DNA sequence alignment and cosegregation of the functional marker with the phenotypes of the RIL population demonstrated that Pm4d was responsible for the ASR to isolate Bgt1 in Tianmin 668. The dissection of genetic loci for APR and ASR may facilitate the application of Tianmin 668 in developing powdery mildew-resistant wheat cultivars.

New genotyping rescues old phenotypes: beneficial traits in Heterorhabditis bacteriophora wild type material and association to single nucleotide polymorphisms

Summary The entomopathogenic nematode (EPN), Heterorhabditis bacteriophora, is a biocontrol agent with almost world-wide distribution, thus providing a huge source of biodiversity available for exploitation in breeding programmes to improve beneficial traits. For management of economically important insect pests, the nematode is mass-produced in liquid culture, formulated with inert carriers, and transported under controlled temperature conditions to maintain its quality. The biocontrol potential of nematode materials is defined by their genetic background. Previous reports have assessed the phenotypic plasticity of H. bacteriophora. Major beneficial traits are field efficacy influenced by their virulence, longevity and field persistence and tolerance to abiotic stresses like oxidative stress, desiccation and extreme low and high temperatures. For several of these traits high heritability values (h2) have been reported, providing a platform for trait improvement by selection. Modern breeding uses molecular markers to track beneficial traits. Recent studies reported hundreds of single nucleotide polymorphisms (SNPs) in H. bacteriophora. This investigation revived a large set of cryopreserved wild types, hybrids and phenotyped materials of H. bacteriophora. Additional phenotypic data on the virulence (LC50) of H. bacteriophora strains against mealworm (Tenebrio molitor) were produced ranging from 1.4 ± 0.3 to 8.0 ± 3.6 nematodes per insect. Markers linked to beneficial traits were used for a comprehensive genotype analysis transferring reported SNP sequence information into the multiplex platform SeqSNP. Specific alleles associated to beneficial traits were identified and 65 materials were classified for the presence of relevant SNPs associated to beneficial traits. This information provides a valuable platform for exploitation of the nematode’s biodiversity for future breeding and selection approaches.

Original and introduced lineages co-driving the persistence of Brucella abortus circulating in West Africa.

Brucellosis, a serious public health issue affecting animals and humans, is neglected in West Africa (WA). In the present study, bio-typing, multi-locus sequence typing (MLST), multiple-locus variable-number tandem repeat analysis (MLVA), and whole genome sequencing single-nucleotide polymorphism (WGS-SNP) analysis were used to characterize the Brucella abortus (B. abortus) strains from WA. All of the 309 strains analyzed in this study were extracted and downloaded from the international MLVA bank and were from 10 hosts (cattle, humans, ovine, buffalo, dromedaries, horse, sheep, zebu, dog, and cat) distributed in 17 countries in WA. Based on the bio-typing, three biovars, dominated by B. abortus bv.3, were observed and reported across seven decades (1958-2019). With MLST, 129 B. abortus strains from the present study were sorted into 14 STs, with ST34 as the predicted founder. These 14 STs clustered into the global MLST data into three clone complexes (C I-C III) with the majority of strains clustering in C I, while C II forms an independent branch, and C III harbors three STs shared by different continents. These data revealed that most cases were caused by strains from native lineages. According to the MLVA-11 comparison, 309 strains were divided into 22 MLVA-11 genotypes, 15 of which were unique to WA and the remaining seven had a global distribution. MLVA-16 analysis showed that there were no epidemiological links among these strains. Based on the MLVA data, B. abortus strains from WA have high genetic diversity, and predominated genotypes were descended from a native lineage. While the MLVA-16 globally highlights that the dominant native and few introduced lineages (from Brazil, the USA, South Korea, Argentina, India, Italy, Portugal, the UK, Costa Rica, and China) co-driving the B. abortus ongoing prevalence in WA. The high-resolution SNP analysis implied the existence of introduced B. abortus lineages, which may be reasonably explained by the movement and trade of dominant hosts (cattle) and/or their products. Our results indicated that B. abortus strains in WA consist of native and introduced strains that necessitate control such as vaccination, testing, slaughtering, and movement control by the relevant country authorities to reduce brucellosis in livestock.

Genome–wide association study and genomic prediction of growth traits in bighead catfish (Clarias macrocephalus Günther, 1864)

Next-generation sequencing (NGS) enables cost-effective exploration of genome-wide single nucleotide polymorphisms (SNPs). This new technology has made enormous contributions in various fields including genetic improvement. This study was conducted to identify SNPs that associated with growth traits (body weight, standard length, and total length) of bighead catfish (Clarias macrocephalus Günther, 1864) using genome-wide association studies (GWAS), and to estimate genetic parameters of these growth traits using genomic best linear unbiased prediction (GBLUP). Individual DNA samples from 991 eight-month-old bighead catfish across 74 full-sibs and 31 half-sibs were collected and subjected to next-generation sequencing using the DArTSeq platform. In return, 9530 SNPs were obtained and used for analysis together with the recorded phenotypic data. The analysis was performed using weighted genomic best linear unbiased prediction (wGBLUP), facilitated by the BLUPF90 family of programs, and identified a set of 19 markers associated with all growth traits; the proportion of explained variance ranged from 1.02 to 6.59%. Four SNP sequences were annotated to Egl nine 2-like gene (egln2), zinc finger FYVE-type containing 21 gene (zfyve21), junctophilin 3a (jph3a), and a hypothetical protein. Furthermore, GBLUP was used to estimate GEBV and heritability of the growth traits. PBLUP was also performed using pedigrees obtained from SNP-based parentage information to estimate PEBV and heritability. The results revealed that GBLUP improved accuracy of EBV estimates over PBLUP. Heritability of growth traits estimated by GBLUP ranged between 0.29 ± 0.05 (for standard length) and 0.55 ± 0.06 (for body weight), with accuracy ranging from 0.672 to 0.724 and prediction bias close to one. When compared to GBLUP, heritability estimated by PBLUP was lower, and with lower accuracy, while bias was lower only for standard length. In conclusion, the growth-associated SNPs identified herein explained only a small portion of genetic variance, and thus using these SNPs for marker-assisted selection is not promising. Instead, genomic selection based on GEBV is a better approach, provided that genotyping cost can be optimized. The knowledge gained from this study is beneficial to genetic improvement of bighead catfish and other related species.

Novel alleles linked to brown rust resistance in sugarcane

AbstractSugarcane brown rust, caused by Puccinia melanocephala, is a severe foliar disease that occurs in almost all countries where sugarcane is grown. The main control strategy is the use of resistant cultivars. The aim of this work was to identify molecular markers linked to genomic regions associated with a novel brown rust resistance source in sugarcane. An F1 progeny of 300 clones was obtained from a cross between TUC 00–36 and RA 87–3, highly susceptible and highly resistant to brown rust, respectively. A total of 60 F1 clones with extreme phenotype, either highly susceptible or highly resistant to brown rust, were selected. This “pooled tail” population was tested for reactions to brown rust under natural infection in the field during two crop seasons and under artificial infection in the greenhouse. Whole‐genome profiling was performed by DArT‐seq technology. Phenotypic data under both conditions and 23,299 single‐nucleotide polymorphisms (SNPs) obtained from genotyping were analysed to identify markers linked to the resistance trait. Single mapping analyses and subsequent multiple regression showed that 34 SNP markers were significantly linked to resistance alleles. These SNPs jointly explained 69% and 66% of the total phenotypic variation (R2) observed for field and controlled conditions, respectively. The mapping of the 34 SNP sequences revealed that 19 markers aligned to the sugarcane genome, whereas 12 markers aligned to the sorghum genome, all grouped on chromosome 5 with some functional annotations related to vegetal defence response. These marker loci could contribute to the development of molecular tools for molecular marker‐assisted breeding.

P604: CATALASE EXPRESSION IN LEUKEMIA CELLS IS CONTROLLED BY GENETIC AND EPIGENETIC MECHANISMS

Background: Chronic lymphocytic leukemia (CLL) is the most prevalent form of leukemia in Western countries. It is an incurable disease characterized by an extremely variable clinical course and response to treatment. We have recently shown that high catalase (CAT) expression identifies a more aggressive clinical behavior in CLL. However, molecular mechanisms controlling catalase expression in leukemia cells are still poorly characterized. The rs1001179 single nucleotide polymorphism (SNP) as well as DNA methylation in the CAT promoter have been shown to regulate CAT expression in various cell types. Aims: With the aim to characterize regulatory mechanisms underlying differential expression of catalase in CLL, in this study we investigated the role of rs1001179 SNP and CpG Island II methylation encompassing this SNP in the regulation of CAT expression. Methods: Peripheral blood mononuclear cells (PBMC) from 55 CLL patients and 50 healthy donors (HD) were used for the study. CAT mRNA levels were measured using quantitative reverse transcription polymerase chain reaction (qRT-PCR). DNA samples were genotyped for the rs1001179 SNP using Restriction Fragment Length Polymorphism (RFLP)-PCR. Bioinformatic analysis was used to predict transcription factor (TF) binding to SNP sequences. Chromatin Immunoprecipitation (ChIP)-qPCR was performed to validate predicted TF binding sites. Methylation levels of CpG sites within the CAT promoter was determined by pyrosequencing of bisulfite-converted DNA. Results: The rs1001179 SNP genotyping showed that CLL cells harboring the T allele exhibited a significantly higher catalase expression compared with cells bearing the CC genotype. Interestingly, bioinformatic analysis predicted that the SNP provide distinct binding sites for various TF, including ETS-1 and GR-β. ChIP assay confirmed that CAT promoter harboring the T -but not C- allele was accessible to ETS-1 and GR-β, but not to the other analyzed TFs. In addition, CLL cells exhibited lower methylation levels within the CAT promoter compared with HD B cells, in line with the higher catalase mRNA and protein levels expressed by CLL versus HD B cells. Moreover, methylation levels negatively correlated with CAT expression in CLL cells. Remarkably, inhibition of methyltransferase activity in leukemic cells induced a significant increase of CAT mRNA and protein levels, thus showing that DNA methylation could control catalase expression in CLL. Moreover, expression of DNA methyl transferase 1 (DNMT1) resulted significantly reduced in CLL cells compared with HD B cells and inversely correlated with CAT expression in CLL, thus suggesting that differences in methylation levels underlying catalase expression could be driven by the DNMT1 enzyme. Finally, modeling analysis showed that the CT/TT genotypes exhibited a lower methylation and a higher CAT expression level, suggesting that the rs1001179 T allele and methylation cooperate in inducing CAT gene expression. Summary/Conclusion: Our data show that SNP as well as methylation of the catalase promoter are involved in controlling catalase expression in CLL. The key advance of this study is to provide new insights into the regulatory mechanisms underlying differential expression of catalase in leukemic cells, which is of clinical relevance in CLL. Moreover, our study may form the basis for future challenges aimed at developing combinatorial therapies targeting catalase regulatory pathways.

Towards Strain-Level Complexity: Sequencing Depth Required for Comprehensive Single-Nucleotide Polymorphism Analysis of the Human Gut Microbiome.

Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.

SNP markers identification by genome wide association study for chemical quality traits of coffee (Coffea spp.) Germplasm.

Coffee quality is an important selection criterion for coffee breeding. Metabolite profiling and Genome-Wide Association Studies (GWAS) effectively dissect the genetic background of complex traits such as metabolites content (caffeine, trigonelline, and 5-caffeoylquinic acid (5-CQA)) in coffee that affect quality. Therefore, it is important to determine the metabolic profiles of Coffea spp. genotypes. This study aimed to identify Single Nucleotide Polymorphisms (SNPs) within Coffea spp. genotypes through GWAS and associate these significant SNPs to the metabolic profiles of the different genotypes. A total of 1,739 SNP markers were obtained from 80 genotypes using the DArTseq™ method. Caffeine, trigonelline, and 5-CQA content were determined in coffee leaves using Ultra-Performance Liquid Chromatography/tandem mass spectrometry (UPLC-MS/MS) analyses. The GWAS was carried out using the Genome Association and Prediction Integrated Tool (GAPIT) software and a compressed mixed linear model. Finally, a total of three significant SNP markers out of ten were identified. One SNP, located in the coffee chromosome (Chr) 8, was significantly associated with caffeine. The two remaining SNPs, located in Chr 4 and 5, were significantly associated with trigonelline and six SNPs markers were associated with 5-CQA in Chr 1, 5 and 10, but these six markers were not significant. These significant SNP sequences were associated with protein ubiquitination, assimilation, and wall receptor kinases. Therefore, these SNPs might be useful hits in subsequent quality coffee breeding programs.

A Novel Quality-Control Procedure to Improve the Accuracy of Rare Variant Calling in SNP Arrays.

Background: Single-nucleotide polymorphism (SNP) arrays are an ideal technology for genotyping genetic variants in mass screening. However, using SNP arrays to detect rare variants [with a minor allele frequency (MAF) of <1%] is still a challenge because of noise signals and batch effects. An approach that improves the genotyping quality is needed for clinical applications. Methods: We developed a quality-control procedure for rare variants which integrates different algorithms, filters, and experiments to increase the accuracy of variant calling. Using data from the TWB 2.0 custom Axiom array, we adopted an advanced normalization adjustment to prevent false calls caused by splitting the cluster and a rare het adjustment which decreases false calls in rare variants. The concordance of allelic frequencies from array data was compared to those from sequencing datasets of Taiwanese. Finally, genotyping results were used to detect familial hypercholesterolemia (FH), thrombophilia (TH), and maturity-onset diabetes of the young (MODY) to assess the performance in disease screening. All heterozygous calls were verified by Sanger sequencing or qPCR. The positive predictive value (PPV) of each step was estimated to evaluate the performance of our procedure. Results: We analyzed SNP array data from 43,433 individuals, which interrogated 267,247 rare variants. The advanced normalization and rare het adjustment methods adjusted genotyping calling of 168,134 variants (96.49%). We further removed 3916 probesets which were discordant in MAFs between the SNP array and sequencing data. The PPV for detecting pathogenic variants with 0.01%<MAF≤1% exceeded 99.37%. PPVs for those with an MAF of ≤0.01% improved from 95% to 100% for FH, 42.11% to 85.19% for TH, and 18.24% to 72.22% for MODY after adopting our rare variant quality-control procedure and experimental verification. Conclusion: Adopting our quality-control procedure, SNP arrays can adequately detect variants with MAF values ranging 0.01%∼0.1%. For variants with MAF values of ≤0.01%, experimental validation is needed unless sequencing data from a homogeneous population of >10,000 are available. The results demonstrated our procedure could perform correct genotype calling of rare variants. It provides a solution of pathogenic variant detection through SNP array. The approach brings tremendous promise for implementing precision medicine in medical practice.

Wavelet Screening: a novel approach to analyzing GWAS data

BackgroundTraditional methods for single-variant genome-wide association study (GWAS) incur a substantial multiple-testing burden because of the need to test for associations with a vast number of single-nucleotide polymorphisms (SNPs) simultaneously. Further, by ignoring more complex joint effects of nearby SNPs within a given region, these methods fail to consider the genomic context of an association with the outcome.ResultsTo address these shortcomings, we present a more powerful method for GWAS, coined ‘Wavelet Screening’ (WS), that greatly reduces the number of tests to be performed. This is achieved through the use of a sliding-window approach based on wavelets to sequentially screen the entire genome for associations. Wavelets are oscillatory functions that are useful for analyzing the local frequency and time behavior of signals. The signals can then be divided into different scale components and analyzed separately. In the current setting, we consider a sequence of SNPs as a genetic signal, and for each screened region, we transform the genetic signal into the wavelet space. The null and alternative hypotheses are modeled using the posterior distribution of the wavelet coefficients. WS is enhanced by using additional information from the regression coefficients and by taking advantage of the pyramidal structure of wavelets. When faced with more complex genetic signals than single-SNP associations, we show via simulations that WS provides a substantial gain in power compared to both the traditional GWAS modeling and another popular regional association test called SNP-set (Sequence) Kernel Association Test (SKAT). To demonstrate feasibility, we applied WS to a large Norwegian cohort (N=8006) with genotypes and information available on gestational duration.ConclusionsWS is a powerful and versatile approach to analyzing whole-genome data and lends itself easily to investigating various omics data types. Given its broader focus on the genomic context of an association, WS may provide additional insight into trait etiology by revealing genes and loci that might have been missed by previous efforts.

Association Association of genetic variant of OXTR (rs53576) and MTNR-1B (rs10830963 and rs1387153) with symptoms of stress in Pakistani women with Gestational Diabetes.

Background: Pregnancy continues to be an event full of risks, complications and adverse outcomes for women despite advanced clinical and diagnostic methods and treatment options available in medicine. Diabetes, as a multifactorial and complex disease, is a possible outcome of a blend of numerous genetic variants of differing scarcities. The exact etiology of gestational diabetes mellitus (GDM) is unclear; however, the interaction of genetic and environmental factors, especially the genetic variants identified as the genetic loci for type 2 diabetes as candidate genes for GDM.&#x0D; Methodology: It will be a case-control study with a genetic analysis of single nucleotide polymorphisms (SNPs) of candidate genes expressed using real-time PCR with sequencing. The study will also evaluate the primer sequence of SNPs.&#x0D; Discussion: We will be able to share the findings after the completion of our research. The results of this study will able helpful in addressing the psycho-physiological needs of women with GDM, which may also be used to devise strategies to benefit the sustainable long-term behavioural change following the affected pregnancy.

Impact of Marker Pruning Strategies Based on Different Measurements of Marker Distance on Genomic Prediction in Dairy Cattle.

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of 'marker density' seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of 'marker density' based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean (d¯) and variance (σd2) of the physical distance between SNPs and the mean (r2¯) and variance (σr22) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d¯ of all panels is basically the same, but the σd2, r2¯ and σr22 are different. Therefore, we only investigated the effects of σd2, r2¯ and σr22 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σd2, but not with r2¯ and σr22. Compared with GenD and RanD, the σd2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large σd2, which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20-30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data.

Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis

This paper introduces a kernel based fuzzy clustering approach to deal with the non-linear separable problems by applying kernel Radial Basis Functions (RBF) which maps the input data space non-linearly into a high-dimensional feature space. Discovering clusters in the high-dimensional genomics data is extremely challenging for the bioinformatics researchers for genome analysis. To support the investigations in bioinformatics, explicitly on genomic clustering, we proposed high-dimensional kernelized fuzzy clustering algorithms based on Apache Spark framework for clustering of Single Nucleotide Polymorphism (SNP) sequences. The paper proposes the Kernelized Scalable Random Sampling with Iterative Optimization Fuzzy c-Means (KSRSIO-FCM) which inherently uses another proposed Kernelized Scalable Literal Fuzzy c-Means (KSLFCM) clustering algorithm. Both the approaches completely adapt the Apache Spark cluster framework by localized sub-clustering Resilient Distributed Dataset (RDD) method. Additionally, we are also proposing a preprocessing approach for generating numeric feature vectors for huge SNP sequences and making it a scalable preprocessing approach by executing it on an Apache Spark cluster, which is applied to real-world SNP datasets taken from open-internet repositories of two different plant species, i.e., soybean and rice. The comparison of the proposed scalable kernelized fuzzy clustering results with similar works shows the significant improvement of the proposed algorithm in terms of time and space complexity, Silhouette index, and Davies-Bouldin index. Exhaustive experiments are performed on various SNP datasets to show the effectiveness of proposed KSRSIO-FCM in comparison with proposed KSLFCM and other scalable clustering algorithms, i.e., SRSIO-FCM, and SLFCM.

Influence of single nucleotide polymorphisms among cigarette smoking and non-smoking patients with coronary artery disease, urinary bladder cancer and lung cancer.

IntroductionCigarette smoke is suggested to be a risk factor for coronary artery disease (CAD), urinary bladder cancer (UBCa) or lung cancer (LCa). However, not all heavy smokers develop these diseases and elevated cancer risk among first-degree relatives suggests an important role of genetic factor.MethodsThree hundred and ten healthy blood donors (controls), 98 CAD, 74 UBCa and 38 LCa patients were included in this pilot study. The influence of 92 single nucleotide polymorphisms (SNPs) and impact of cigarette smoking were analysed.ResultsOut of 92 SNPs tested, differences in distribution of 14 SNPs were detected between controls and patient groups. Only CTLA4 rs3087243 showed difference in both CAD and UBCa patient group compared to control group. Stratified by smoking status, the impact of smoking was associated to frequencies of 8, 3 and 4 SNPs in CAD, UBCa, LCa patients, respectively. None of these 92 SNPs showed a statistically significant difference to more than one type of disease among smoking patients. In non-smoking patients, 7, 3 and 6 SNPs were associated to CAD, UBCa, LCa, respectively. Out of these 92 SNPs, CTLA4 rs3087243 was associated to both non-smoking CAD and UBCa. The XRCC1 rs25487 was associated to both non-smoking UBCa and LCa.ConclusionSNPs might be important risk factors for CAD, UBCa and LCa. Distribution of the SNPs was specific for each patient group, not a random event. Impact of cigarette smoking on the disease was associated to the specific SNP sequences. Thus, smoking individuals with SNPs associated to risk of these serious diseases is an important target group for smoking cessation programs.