Group Of Single Nucleotide Polymorphisms Research Articles

Phenotypic and Genotypic Variation of Cultivated Panax quinquefolius.

American ginseng (Panax quinquefolius) is widely used due to its medicinal properties. Ontario is a major producer of cultivated American ginseng, where seeds were originally collected from the wild without any subsequent scientific selection, and thus the crop is potentially very diverse. A collection of 162 American ginseng plants was harvested from a small area in a commercial garden and phenotyped for morphological traits, such as root grade, stem length, and fresh and dry weights of roots, leaves, stems, and seeds. All of the traits showed a range of values, and correlations were observed between root and stem weights, root dry weight and leaf dry weight, as well as root and leaf fresh weights. The plants were also genotyped using single nucleotide polymorphisms (SNPs) at the PW16 locus. SNP analysis revealed 22 groups based on sequence relatedness with some groups showing no SNPs and others being more diverse. The SNP groups correlated with significant differences in some traits, such as stem length and leaf weight. This study provides insights into the genetic and phenotypic diversity of cultivated American ginseng grown under similar environmental conditions, and the relationship between different phenotypes, as well as genotype and phenotype, will aid in future selection programs to develop American ginseng cultivars with desirable agronomic traits.

Association between gut microbiota and peptic ulcer disease, particularly gastric ulcer and duodenal ulcer: a two-sample Mendelian randomization study.

Recent an observational study has suggested a potential connection between gut microbiota (GM) and peptic ulcer diseases (PUDs), particularly gastric ulcer (GU) and duodenal ulcer (DU). However, the causal connection remains unsure. A two-sample Mendelian randomization (MR) is carried out to explore the connection between the GM and DU or GU. Data on the GM comes from the MiBioGend database, and GU or DU data are based on the FinnGen database. One group of single nucleotide polymorphisms (SNPs) (P < 5 × 10-8) are served as instrumental variables (IVs). To obtain a more comprehensive conclusion, the other SNPs (P < 1 × 10-5) are selected as IVs. Inverse variance weighting (IVW) is used to determine the causal relationship. At the level of P < 1 × 10-5, the IVW analysis suggests that Clostridiaceae1, Butyriccoccus, and Peptcoccus have harmful effects on GU, while LachnospiraceaeUCG004 and MollicutesRF9 have beneficial effects on GU. Then, in the case of DU, the IVW analysis suggested that Lentisphaeria, Negativicutes, Clostridiaceae1, ClostridiumseMnsustricto1, ErysipelotrichaceaeUCG003, LachnospiraceaeNC2004group, Selenomonadale, Victivallales, and Lentisphaerae have harmful effects, while Catenibacterium, Escherichia.Shigella, LachnospiraceaeUCG008, and Sutterella have beneficial effects. When P < 5 × 10-8, IVW analysis suggests that GM has no significant influence on GU or DU. This two-sample MR indicates a causal relationship between GM and GU or DU.

Bacterial SNPs in the human gut microbiome associate with host BMI.

Genome-wide association studies (GWASs) have provided numerous associations between human single-nucleotide polymorphisms (SNPs) and health traits. Likewise, metagenome-wide association studies (MWASs) between bacterial SNPs and human traits can suggest mechanistic links, but very few such studies have been done thus far. In this study, we devised an MWAS framework to detect SNPs and associate them with host phenotypes systematically. We recruited and obtained gut metagenomic samples from a cohort of 7,190 healthy individuals and discovered 1,358 statistically significant associations between a bacterial SNP and host body mass index (BMI), from which we distilled 40 independent associations. Most of these associations were unexplained by diet, medications or physical exercise, and 17 replicated in a geographically independent cohort. We uncovered BMI-associated SNPs in 27 bacterial species, and 12 of them showed no association by standard relative abundance analysis. We revealed a BMI association of an SNP in a potentially inflammatory pathway of Bilophila wadsworthia as well as of a group of SNPs in a region coding for energy metabolism functions in a Faecalibacterium prausnitzii genome. Our results demonstrate the importance of considering nucleotide-level diversity in microbiome studies and pave the way toward improved understanding of interpersonal microbiome differences and their potential health implications.

Evaluation of Density-Based Spatial Clustering for Identifying Genomic Loci Associated with Ischemic Stroke in Genome-Wide Data.

The genetic architecture of ischemic stroke (IS), which is one of the leading causes of death worldwide, is complex and underexplored. The traditional approach for associative gene mapping is genome-wide association studies (GWASs), testing individual single-nucleotide polymorphisms (SNPs) across the genomes of case and control groups. The purpose of this research is to develop an alternative approach in which groups of SNPs are examined rather than individual ones. We proposed, validated and applied to real data a new workflow consisting of three key stages: grouping SNPs in clusters, inferring the haplotypes in the clusters and testing haplotypes for the association with phenotype. To group SNPs, we applied the clustering algorithms DBSCAN and HDBSCAN to linkage disequilibrium (LD) matrices, representing pairwise r2 values between all genotyped SNPs. These clustering algorithms have never before been applied to genotype data as part of the workflow of associative studies. In total, 883,908 SNPs and insertion/deletion polymorphisms from people of European ancestry (4929 cases and 652 controls) were processed. The subsequent testing for frequencies of haplotypes restored in the clusters of SNPs revealed dozens of genes associated with IS and suggested the complex role that protocadherin molecules play in IS. The developed workflow was validated with the use of a simulated dataset of similar ancestry and the same sample sizes. The results of classic GWASs are also provided and discussed. The considered clustering algorithms can be applied to genotypic data to identify the genomic loci associated with different qualitative traits, using the workflow presented in this research.

Does inflammatory bowel disease promote kidney diseases: a mendelian randomization study with populations of European ancestry

BackgroundThis study aimed to investigate a causal relationship between IBD and multiple kidney diseases using two-sample Mendelian randomization (MR) analyses.MethodsWe selected a group of single nucleotide polymorphisms (SNPs) specific to IBD as instrumental variables from a published genome-wide association study (GWAS) with 86,640 individuals of European ancestry. Summary statistics for multiple kidney diseases were obtained from the publicly available GWAS. Genetic data from one GWAS involving 210 extensive T-cell traits was used to estimate the mediating effect on specific kidney disease. Inverse-variance weighted method were used to evaluate the MR estimates for primary analysis.ResultsGenetic predisposition to IBD was associated with higher risk of IgA nephropathy (IgAN) (OR, 1.78; 95% CI, 1.45–2.19), but not membranous nephropathy, diabetic nephropathy, glomerulonephritis, nephrotic syndrome, chronic kidney disease, and urolithiasis. CD4 expression on CD4 + T cell had a significant genetic association with the risk of IgAN (OR, 2.72; 95% CI, 1.10–6.72). Additionally, consistent results were also observed when IBD was subclassified as ulcerative colitis (OR, 1.38; 95% CI, 1.10–1.71) and Crohn’s disease (OR, 1.37; 95% CI, 1.12–1.68). MR-PRESSO and the MR-Egger intercept did not identify pleiotropic SNPs.ConclusionsThis study provides genetic evidence supporting a positive casual association between IBD, including its subclassification as ulcerative colitis and Crohn’s disease, and the risk of IgAN. However, no casual association was found between IBD and other types of kidney diseases. Further exploration of IBD interventions as potential preventive measures for IgAN is warranted.

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

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

Identification of SNP markers for canine mammary gland tumours in females based on a genome-wide association study - preliminary results.

The development of genetic research over recent decades has enabled the discovery of new genetic markers, such as single nucleotide polymorphisms (SNPs). This, as well as the full sequencing of the dog genome, has enabled genome-wide association studies (GWAS) to be used in the search for genetic causes of canine mammary tumours (CMTs). Genotypic data containing 175,000 SNPs, which had been obtained using the Illumina CanineHD BeadChip microarray technique, were available for analysis in this study. The data concerned 118 bitches, including 36 animals with CMT, representing various breeds and age groups. Statistical analysis was performed in two steps: quality control of genotyping data and genome-wide association analysis based on dominant, recessive, overdominant, codominant, and log-additive models with the single SNP effects. A total of 40 different SNPs significantly associated with CMT appearance were detected. Moreover, twelve SNPs showed statistical significance in more than one model. Of all the significant SNPs, two, namely BICF2G630136001 in the overdominant model and TIGRP2P107898_rs9044787 in the log-additive model, reached the 5-8 significance level. The other SNPs were significant to a 1-5 level. In the group of SNPs indicated as significant in the GWAS analysis, several transpired to be localised within genes that may play an important role in CMT.

Bayesian bi-level variable selection for genome-wide survival study.

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

Bi-level structured functional analysis for genome-wide association studies.

Genome-wide association studies (GWAS) have led to great successes in identifying genotype-phenotype associations for complex human diseases. In such studies, the high dimensionality of single nucleotide polymorphisms (SNPs) often makes analysis difficult. Functional analysis, which interprets SNPs densely distributed in a chromosomal region as a continuous process rather than discrete observations, has emerged as a promising avenue for overcoming the high dimensionality challenges. However, the majority of the existing functional studies continue to be individual SNP based and are unable to sufficiently account for the intricate underpinning structures of SNP data. SNPs are often found in groups (e.g., genes or pathways) and have a natural group structure. Additionally, these SNP groups can be highly correlated with coordinated biological functions and interact in a network. Motivated by these unique characteristics of SNP data, we develop a novel bi-level structured functional analysis method and investigate disease-associated genetic variants at the SNP level and SNP group level simultaneously. The penalization technique is adopted for bi-level selection and also to accommodate the group-level network structure. Both the estimation and selection consistency properties are rigorously established. The superiority of the proposed method over alternatives is shown through extensive simulation studies. A type 2 diabetes SNP data application yields some biologically intriguingresults.

Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network.

A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach. Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes. Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively. These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.

Alignment of human aquaporin 4 and ß-amyloid proteins may indicate involvement of ß-amyloid in brain water homeostasis and prevention of brain edema.

Alignment of human aquaporin 4 and ß-amyloid proteins may indicate involvement of ß-amyloid in brain water homeostasis and prevention of brain edema.

FOXO3A acts as immune response modulator in human virus-negative inflammatory cardiomyopathy

ObjectiveInflammatory cardiomyopathy is characterised by inflammatory infiltrates leading to cardiac injury, left ventricular (LV) dilatation and reduced LV ejection fraction (LVEF). Several viral pathogens and autoimmune phenomena may cause cardiac...

New genetic markers for &lt;i&gt;Burkholderia pseudomallei&lt;/i&gt; strains typing

Burkholderia pseudomallei is the causative agent of melioidosis, a serious infectious disease in humans and animals that may be asymptomatic long-term and quickly develop to pneumonia and septicemia in immunocompromised state and due to predisposing factors. Melioidosis is endemic in countries with tropical and subtropical climates, where B. pseudomallei is a part of the soil and water microbiota of stagnant water bodies, as well as the plant rhizosphere. The recording of imported melioidosis cases in countries with temperate climate zone, along with the continued threat of using this pathogen as a bioterrorism agent, indicate the relevance of research aimed at developing modern methods for its diagnosing and typing. Current research in the intraspecific differentiation of pathogenic strains of infectious diseases tends to use two or more types of molecular markers. A promising direction for the B. pseudomallei strains genotyping is based on using a combination of VNTR loci, which provides a high discriminating ability of the method of multi-locus variable tandem repeat number analysis (MLVA), and slowly evolving single nucleotide polymorphisms (SNPs). The aim of this work was to identify new VNTR and SNP loci suitable for use as genetic markers in molecular typing of the melioidosis causative agent. 20 strains of B. pseudomallei from the collection of the Volgograd Plague Control Research Institute and whole genome sequences of 85 B. pseudomallei strains from the GenBank NCBI database were analyzed. While typing melioidosis causative agent strains from the GenBank NCBI database for 4 VNTR loci, 74 genotypes were identified, of which 64 were unique (HunterGaston index 0.997). It was found that the high allelic polymorphism of VNTR loci limited a potential to determine the geographical and phylogenetic relationships of B. pseudomallei isolates by using the MLVA-4 scheme, and the identified null alleles increased the risk of homoplasia. In this regard, the MLVA-4 scheme was supplemented with a VNTR marker in the BPSS1974 locus encoding a collagen-like protein as well as with SNP markers in the genes of lipid A biosynthesis lauroyl acyltransferase, RNA polymerase sigma factor RpoH, and glutamine amidotransferase. For amplification of the select loci, primers and probes were designed as part of the developed scheme, which were tested in the typing of B. pseudomallei collection strains. According to the results of a cluster analysis for 105 strains of the melioidosis causative agent, an integrated approach turned out to be optimal, allowing differentiation of strains within SNP groups based on VNTR profiles. The methodological approach developed for B. pseudomallei genetic typing, based on a comprehensive analysis of 4 SNP- and 5 MLVA-markers in our modification, allowed to differentiate strains according to the geographical regions of their origin and establish the clonal origin of isolates upon revealing cases of melioidosis.

Multigraph Cluster Analysis of AD Candidate Genes via Human Brain Mapping

AbstractBackgroundBrain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s diseases (AD). Given the high dimensionality, the individual level SNP‐QT signals typically have small effect sizes and are hard to be detected and replicated. To overcome this limitation, this work proposes a new approach to identify high‐level imaging genetic associations through applying multi‐graph clustering to SNP‐QT association maps.MethodsParticipants include 255 cognitive normal (CN) and 218 late mild cognitive impairment (LMCI) subjects from the ADNI data. Linear regression is performed to regress regional AV‐45 measures on each of the 54 AD SNPs to obtain a brain association map across 116 AAL‐ROIs. To quantify the similarity between each SNP pair, 5 scoring functions are used to calculate 5 different similarity measures between the respective brain maps of the two SNPs. Each scoring function yields a similarity graph of all 54 SNPs. Multigraph min‐max clustering is applied to 5 different graphs simultaneously to group similar SNPs together. Functional annotation is performed on each SNP cluster and the corresponding average brain association map to provide high level interpretations.ResultsFigure 1 shows the genetic effect on regional AV‐45 measures for each pair of SNP and ROI. The multi‐graph clustering result is shown in Figure 2, where 54 AD‐related SNPs are clustered into two groups. Figure 3a shows the results of Enrichr Elsevier pathway analysis. SNP group 1 is associated with pathways such as amyloid beta clearance, while SNP group 2 is associated with pathways including amyloid beta formation and APP processing in AD. Figure 3b shows the NeuroSynth annotation of the average brain effect maps associated with the two SNP clusters, respectively.ConclusionWe proposed an approach that transforms individual level imaging genetic associations (SNP vs ROI) to high‐level associations (i.e., SNP cluster vs brain association map), and applied it to an LMCI study. These high‐level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.

Vitamin D levels and Vitamin D-related gene polymorphisms in Chinese children with type 1 diabetes.

Low vitamin D levels may play a role in type 1 diabetes (T1D) susceptibility. Since 25(OH)D synthesis is genetically regulated, single nucleotide polymorphisms (SNPs) of important genes have also been shown to modulate the risk of T1D, so this study aimed to investigate the relationship between five SNPs in CYP2R1, DHCR7, CYP24A1, VDR genes, serum 25(OH)D levels and T1D in Chinese children. This case-control study included 141 T1D patients and 200 age-matched healthy children.25 (OH) D concentration was determined, genotyping was performed by High resolution melting (HRM). There was a significant difference in the prevalence of vitamin D deficiency, insufficiency, and sufficiency between T1D and healthy controls. (χ2 = 10.86, p = 0.004), however no evidence of the association between any group of SNPs and circulating 25(OH) D levels was observed. The allele distribution of CYP2R1(rs1993116) was significantly different between T1D and control group (p = 0.040), and the C allele carriers of rs1993116 had a higher risk of T1D than the T allele carriers, Carriers of the CC and CT genotypes of rs1993116 have higher T1D risk than those carrying the TT genotype. GMDR analysis revealed a significant interaction between CYP2R1(rs12794714) and CYP2R1(rs1993116) in the risk of T1D with a maximum testing balance accuracy of 60.39%.

Associated Effect of SLC40A1 and TMPRSS6 Polymorphisms on Iron Overload.

Mutations in the ferroportin (FPN) gene SLC40A1 alter iron recycling and cause disturbances in iron homeostasis. The variants of TMPRSS6 contribute to the development of iron deficiencies. In this study, we determined the role of FPN and TMPRSS6 gene polymorphisms in the modulation of iron homeostasis based on biochemical parameters. PCR analysis and sequencing were performed to determine the single nucleotide polymorphisms (SNPs) SLC40A1 c.44–24G>C (rs1439816), SLC40A1 c.663T>C (rs2304704), and TMPRSS6 c.2207T>C (rs855791). Hemoglobin concentration and iron status were determined by standard procedures. We studied 79 iron-loaded individuals for SLC40A1 polymorphisms. Interestingly, 35/79 individuals with SLC40A1 SNPs also carried a TMPRSS6 c.2207T>C polymorphism. The biochemical values of the iron overloaded individuals were compared to those of the individuals carrying TMPRSS6 SNPs and the healthy individuals (wild-type group). The ferritin concentration, transferrin saturation % (TS%), and hemoglobin concentration were significantly higher in the participants with FPN SNPs than in the other three groups. The ferritin concentration and TS% were higher in participants with both SLC40A1 and TMPRSS6 SNPs than in the TMPRSS6 and wild-type groups, while hemoglobin concentration was significantly higher than that in the TMPRSS6 SNP group only. The participants with TMPRSS6 SNPs had significantly lower ferritin concentration, TS%, and hemoglobin concentration than all the other groups. SLC40A1 and TMPRSS6 SNPs might act in the opposite direction, preventing the development of severe iron overload, and the modulation of the iron status by TMPRSS6 SNPs might provide protection.

Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.

Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for higher-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The proposed method is fitted on a group of single nucleotide polymorphisms (SNPs) potentially associated with the phenotype and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify higher-order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture underlying complex phenotypes.

Single nucleotide polymorphism within chromosome 8q24 is associated with prostate cancer development in Saudi Arabia

ObjectiveGenome-wide association studies have demonstrated that single nucleotide polymorphisms (SNPs) are important risk factors for the development of prostate cancer (PCa). Preliminary studies have suggested that the incidence of PCa in Saudi males is low but is probably familial or genetically related. MethodsTo identify any possible association of SNP with PCa development in Saudi patients, we investigated a group of SNPs in Saudi PCa patients (n=85) and compared the outcomes to healthy normal controls (n=115) and nodular hyperplasia patients (n=120). DNA was extracted from paraffin-embedded formalin fixed tissue or whole blood from both patients’ groups and healthy control group. A total of thirteen SNPs were genotyped using TaqMan® minor groove binder polymerase chain reaction assay. ResultsThe rs16901979A, s629242T and rs1447295A alleles were found at significantly higher frequency in PCa patients than controls (p<0.05). The rs16901979 CA genotype was found at significantly greater frequency in PCa patients than in healthy controls (43% vs. 14%, odds ratio=4.6, p=0.0001) and benign hyperplasia group (43% vs. 25%, odds ratio=2.2, p=0.009). ConclusionOur study has highlighted the association of rs16901979 SNP with PCa in Saudi males. Such findings have important implications in the PCa diagnosis and in screening unaffected family members of Saudi patients.

Polymorphisms of genes associated with psycho-emotional deviations in a sample of healthy male conscripts

The prevalence of candidate gene polymorphic alleles associated with personality disorders was studied in a sample of healthy male Military Innovation Technopolis ERA recruits, science mouth operators of Caucasian race aged 2327 (median age 24). Genotyping of 10 studied polymorphic variants of genes was carried out using polymerase chain reaction in real time. According to the results, the distribution of genotypes in the studied group of single nucleotide polymorphisms corresponded to HardyWeinberg equilibrium, except for the C677T MTHFR variant (rs1801133). The occurrence of minor alleles was as follows: 0.500 for A1438G and 0.310 for C102T (HTRA2); 0.175 for G-6A (CNTF); 0.508 for C T (UCP2); 0.270 for A1298C and 0.325C677T (MTHFR); 0.278A2756G (MTR); 0.571A66G (MTRR); 0.381Alu Ins/Del (ACE); 0.294G1444A (PPARGC1A). In the studied sample, the frequency of occurrence of minor alleles of 10 genetic markers correlated with data collected on people of European descent. The greatest statistical deviation was observed by comparison with data on people of African descent. Statistically significant concordances with different populations and studies conducted in them are the basis for including the selected polymorphisms in a candidate list of psycho-emotional and neurological pathologies influencing the occupational fitness of servicepersons. However, reliable data on the association between mental disorders and the development of degenerative disorders are not available simultaneously in all populations; therefore, additional research is required on this subject.

Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum.

We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.