Single Nucleotide Polymorphism Level Research Articles

Variable parallelism in the genomic basis of age at maturity across spatial scales in Atlantic Salmon.

Complex traits often exhibit complex underlying genetic architectures resulting from a combination of evolution from standing variation, hard and soft sweeps, and alleles of varying effect size. Increasingly, studies implicate both large-effect loci and polygenic patterns underpinning adaptation, but the extent that common genetic architectures are utilized during repeated adaptation is not well understood. Sea age or age at maturation represents a significant life history trait in Atlantic Salmon (Salmo salar), the genetic basis of which has been studied extensively in European Atlantic populations, with repeated identification of large-effect loci. However, the genetic basis of sea age within North American Atlantic Salmon populations remains unclear, as does the potential for a parallel trans-Atlantic genomic basis to sea age. Here, we used a large single-nucleotide polymorphism (SNP) array and low-coverage whole-genome resequencing to explore the genomic basis of sea age variation in North American Atlantic Salmon. We found significant associations at the gene and SNP level with a large-effect locus (vgll3) previously identified in European populations, indicating genetic parallelism, but found that this pattern varied based on both sex and geographic region. We also identified nonrepeated sets of highly predictive loci associated with sea age among populations and sexes within North America, indicating polygenicity and low rates of genomic parallelism. Despite low genome-wide parallelism, we uncovered a set of conserved molecular pathways associated with sea age that were consistently enriched among comparisons, including calcium signaling, MapK signaling, focal adhesion, and phosphatidylinositol signaling. Together, our results indicate parallelism of the molecular basis of sea age in North American Atlantic Salmon across large-effect genes and molecular pathways despite population-specific patterns of polygenicity. These findings reveal roles for both contingency and repeated adaptation at the molecular level in the evolution of life history variation.

Contribution of genetic polymorphism in ABCB1 to individual variations of imatinib plasma levels in patients with gastrointestinal stromal tumor.

Imatinib mesylate (IM) is a first-line treatment option for the majority of patients diagnosed with gastrointestinal stromal tumors (GISTs). Although the clinical benefit is high, interindividual response is variable. This study thus aimed to assess how genetic polymorphisms can affect the blood levels of IM and treatment outcomes in patients with GIST. A total of 31 single-nucleotide polymorphisms (SNPs) in selected cytochrome P450 (P450), ATP-binding cassette transporter (ABC), solute carrier family (SLC), interleukin-4 receptor (IL4R), and vascular endothelial growth factor (VEGF) genes were genotyped using an SNP mass array platform. A total of 192 consecutive patients with GIST who received 400 mg of IM daily were enrolled into the study, with 1,485 blood samples being analyzed. According to genotypes, IM trough concentrations were tested and compared. Progression-free survival (PFS) and overall survival (OS) were also assessed. With a mean follow-up of 75.99 months, trough concentrations of imatinib were examined at average time points of 7.73 for each patient. Polymorphism in ABCB1 rs1045642 was found to be associated with steady-state IM trough plasma levels (P=0.008). Patients with the C genotype (CT + CC) of rs1045642 exhibited higher IM trough concentrations (1,271.09±306.69 ng/mL) compared to those with the TT genotype (1,106.60±206.05 ng/mL). No statistically significant differences in IM plasma concentration were observed for the other SNPs tested. None of the tested SNPs displayed a significant association with patients' survival in this study. This is the largest cohort study evaluating the associations of SNP and imatinib blood trough levels. The ABCB1 rs1045642 genetic polymorphism may exert an effect on the pharmacokinetics of imatinib. The presence of the C allele in ABCB1 rs1045642 is predictive of a higher plasma concentration of IM.

Inflammation and Gut Barrier Function-Related Genes and Colorectal Cancer Risk in Western European Populations.

Gut barrier dysfunction and related inflammation are known to be associated with the development and progression of colorectal cancer (CRC). We investigated associations of 292 single-nucleotide polymorphisms (SNPs) from 27 genes related to endotoxins/lipopolysaccharide (LPS) sensing and tolerance, mucin synthesis, inflammation, and Crohn's disease with colon and rectal cancer risks. Incident CRC cases (N=1,374; colon=871, rectum=503) were matched 1:1 to controls nested within the European Prospective Investigation into Cancer and Nutrition cohort. Previously measured serum concentrations of gut barrier function and inflammation biomarkers (flagellin/LPS-specific immunoglobulins and C-reactive protein [CRP]) were available for a sub-set of participants (Ncases=1,001; Ncontrols=667). Forty-two unique SNPs from 19 different genes were associated with serum biomarkers at Punadjusted≤0.05 among controls. Among SNPs associated with a gut permeability score, 24 SNPs were in genes related to LPS sensing and mucin synthesis. Nine out of 12 SNPs associated with CRP were in genes related to inflammation or Crohn's disease. TLR4 was associated with colon cancer at the SNP level (nine SNPs, all Punadjusted≤0.04) and at the gene level (Punadjusted≤0.01). TLR4 rs10759934 was associated with rectal cancer but not colon cancer. Similarly, IL10 was associated with rectal cancer risk at a SNP and gene level (both Punadjusted ≤ 0.01), but not colon cancer. Genes and SNPs were selected a priori therefore we present unadjusted P-values. However, no association was statistically significant after multiple testing correction. This large and comprehensive study has identified gut barrier function and inflammation-related genes possibly contributing to CRC risk in European populations and is consistent with potential etiological links between host genetic background, gut barrier permeability, microbial endotoxemia and CRC development.

Expected values for the accuracy of predicted breeding values accounting for genetic differences between reference and target populations

BackgroundGenetic merit, or breeding values as referred to in livestock and crop breeding programs, is one of the keys to the successful selection of animals in commercial farming systems. The developments in statistical methods during the twentieth century and single nucleotide polymorphism (SNP) chip technologies in the twenty-first century have revolutionized agricultural production, by allowing highly accurate predictions of breeding values for selection candidates at a very early age. Nonetheless, for many breeding populations, realized accuracies of predicted breeding values (PBV) remain below the theoretical maximum, even when the reference population is sufficiently large, and SNPs included in the model are in sufficient linkage disequilibrium (LD) with the quantitative trait locus (QTL). This is particularly noticeable over generations, as we observe the so-called erosion of the effects of SNPs due to recombinations, accompanied by the erosion of the accuracy of prediction. While accurately quantifying the erosion at the individual SNP level is a difficult and unresolved task, quantifying the erosion of the accuracy of prediction is a more tractable problem. In this paper, we describe a method that uses the relationship between reference and target populations to calculate expected values for the accuracies of predicted breeding values for non-phenotyped individuals accounting for erosion. The accuracy of the expected values was evaluated through simulations, and a further evaluation was performed on real data.ResultsUsing simulations, we empirically confirmed that our expected values for the accuracy of PBV accounting for erosion were able to correctly determine the prediction accuracy of breeding values for non-phenotyped individuals. When comparing the expected to the realized accuracies of PBV with real data, only one out of the four traits evaluated presented accuracies that were significantly higher than the expected, approaching h2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{\ ext{h}}}^{2}}$$\\end{document}.ConclusionsWe defined an index of genetic correlation between reference and target populations, which summarizes the expected overall erosion due to differences in allele frequencies and LD patterns between populations. We used this correlation along with a trait’s heritability to derive expected values for the accuracy (R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}$$\\end{document}) of PBV accounting for the erosion, and demonstrated that our derived ER|erosion\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{E}}\\left[{\ ext{R}}|{\ ext{erosion}}\\right]$$\\end{document} is a reliable metric.

SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

Functionally Informed Polygenic Risk Scores for Alzheimer’s Disease

AbstractBackgroundAlzheimer’s Disease (AD) is a heritable, neurodegenerative disease. Most AD cases can be categorized as Late Onset Alzheimer’s Disease (LOAD) cases, which are polygenic. Genome Wide Association Studies (GWAS) of LOAD have identified several associated single nucleotide polymorphisms (SNPs). These variants are in 75 genetic loci, and have implicated a diverse array of genes, cell types, and biological pathways in LOAD. Additionally, many associated variants are not in protein coding regions of the genome. However, it is difficult to determine if all associated variants are functionally relevant and contribute to the etiology or progression of the disease. Here, we present CoRE‐BED, a variant annotation tool that can categorize SNPs into functional classes, such as promoters or enhancers, for a given cell type. These functional variants in each cell type are subsequently used to determine functional variant contributions to LOAD heritability and risk.MethodWe annotated a recently published GWAS of AD (Bellenguez et al 2022) using CoRE‐BED. CoRE‐BED leverages 19 functional assays across 33 distinct cell types from EpiMap to classify variants into functional categories for each supported cell type. Annotated variants were used to estimate cell type specific partitioned heritability using LD Score Regression. Finally, annotated variants were used to build cell type specific Polygenic Risk Scores (PRS). PRS was tested using multiple PRS software and utilized the annotated GWAS as a training dataset and independent cases and controls derived from Vanderbilt University’s biobank, BioVU.ResultCoRE‐BED provided functional classifications for all variants in the GWAS for individual tissue types. Variants that pass genome wide significance (P < 5×10−8) are enriched for functional annotations. Immune related cell types contribute the greatest proportion of partitioned heritability. Standard PRS methods outperform cell type informed PRS measures.ConclusionCoRE‐BED is a robust annotation tool that can determine functional annotations at the SNP level. AD GWAS significant associations overlap with functionally annotated SNPs in multiple cell types. Partitioned heritability indicates that immune related cell types may play an important role in the heritability of AD. However, conventional methods for PRS still provide the most robust means to predict AD risk.

Cognitively Healthy Centenarians are genetically protected against Alzheimer’s disease

AbstractBackgroundThe prevalence of Alzheimer’s Disease (AD) increases with age, yet a small fraction of the population reaches ages beyond 100 years maintaining cognitive abilities, so‐called cognitively‐healthy‐centenarians. We aimed to uncover the genetic factors associated with such protection against AD, and to highlight the effects of AD‐associated variants during advanced aging.MethodPrevious Genome‐Wide‐Association‐Studies (GWAS) identified 86 single‐nucleotide‐polymorphisms (SNPs) associated with AD‐risk. We investigated the frequency of each SNP in 346 cognitively‐healthy‐centenarians (mean age 101.05±2.51), compared to 2,281 AD cases (mean age 67.96±9.84), 3,165 middle‐aged healthy controls (mean age 62.57±8.66), and we combined the SNPs into Polygenic Risk Scores (PRS) for each individual. Finally, we characterized the functional properties of the SNPs enriched/depleted the most in the centenarians.ResultAt the SNP level, centenarians were depleted with risk‐increasing SNPs and enriched with protective SNPs. The AD‐related PRS (excluding APOE SNPs) was significantly lower in centenarians; not only compared to AD (OR = 1.96, p = 4.07×10−26), but also to middle‐aged controls (OR = 1.29, p = 2.61×10−5). When including APOE SNPs these effects were even stronger (OR = 5.07, p = 6.15×10−71, and OR = 1.87, p = 4.67×10−17, for comparisons with AD and normal controls, respectively). The most depleted/enriched SNPs in centenarians functionally mapped to activation/regulation of immune response and endocytosis/phagocytosis. These SNPs were located near ANKH, TMEM106B, CLNK, EPDR1, WDR12, PLCG2, RIN3, CD2AP, MS4A, TREM2, ABCA7 and MAF.ConclusionThe ability to reach advanced age without cognitive decline is influenced by AD‐associated genetic variants. An advantageous regulation of the brain’s immune system, intracellular protein trafficking, and endocytosis/phagocytosis may confer the strongest protection against AD and potentially other age‐related diseases.

Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis.

Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.

The genomic response to urbanization in the damselfly Ischnura elegans.

The complex and rapid environmental changes brought about by urbanization pose significant challenges to organisms. The multifaceted effects of urbanization often make it difficult to define and pinpoint the very nature of adaptive urban phenotypes. In such situations, scanning genomes for regions differentiated between urban and non-urban populations may be an attractive approach. Here, we investigated the genomic signatures of adaptation to urbanization in the damselfly Ischnura elegans sampled from 31 rural and urban localities in three geographic regions: southern and northern Poland, and southern Sweden. Genome-wide variation was assessed using more than 370,000 single nucleotide polymorphisms (SNPs) genotyped by ddRADseq. Associations between SNPs and the level of urbanization were tested using two genetic environment association methods: Latent Factors Mixed Models and BayPass. While we found numerous candidate SNPs and a highly significant overlap between candidates identified by the two methods within the geographic regions, there was a distinctive lack of repeatability between the geographic regions both at the level of individual SNPs and of genomic regions. However, we found "synapse organization" at the top of the functional categories enriched among the genes located in the proximity of the candidate urbanization SNPs. Interestingly, the overall significance of "synapse organization" was built up by the accretion of different genes associated with candidate SNPs in different geographic regions. This finding is consistent with the highly polygenic nature of adaptation, where the response may be achieved through a subtle adjustment of allele frequencies in different genes that contribute to adaptive phenotypes. Taken together, our results point to a polygenic adaptive response in the nervous system, specifically implicating genes involved in synapse organization, which mirrors the findings from several genomic and behavioral studies of adaptation to urbanization in other taxa.

Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers.

Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism(SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based(aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case-control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels. -values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortiumwhole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.

Relationship between the methylenetetrahydrofolate reductase (MTHFR) rs1801133 SNP and serum homocysteine levels of Zhuang hypertensive patients in the central region of Guangxi

BackgroundThe relationship between the methylenetetrahydrofolate reductase (MTHFR) single nucleotide polymorphism (SNP) and serum homocysteine (Hcy) levels or H-type hypertension in different populations is inconsistent. This study aimed to explore the association between the MTHFR rs1801133 SNP and serum Hcy levels of Zhuang hypertensive patients in the central region of Guangxi.MethodsA total of 606 Zhuang inpatients with essential hypertension were recruited in our hospital from August 2016 to December 2018. The patients were divided into H-type hypertension (Hcy > 10 µmol/L, n = 528) and non–H-type hypertension (Hcy ≤ 10 µmol/L, n = 78) groups. At the same time, an age- and sex-matched group of 379 subjects with normal physical examination in our hospital were selected as the control group. Blood biochemical measurements and genotyping of the MTHFR rs1801133 SNP were performed.ResultsThe prevalence of H-type hypertension was 87.13%. The levels of serum Hcy in patients with hypertension were higher than those in control group (14.20 ± 5.78 μmol/L vs. 11.97 ± 5.39 μmol/L, P < 0.001), especially in patients with H-type hypertension (15.08 ± 5.65 μmol/L, P < 0.001). The frequencies of TT genotype (22.73%) and T allele (46.21%) in patients with H-type hypertension were significantly higher than those in control group (11.35% and 30.47%, respectively) and non–H-type hypertension group (10.26% and 28.85%, respectively; P < 0.001 for all). Multivariate linear regression analysis showed that serum Hcy levels were significantly correlated with creatinine, low-density lipoprotein cholesterol, endogenous creatinine clearance rate, and the MTHFR rs1801133 genotypes in control group, while serum Hcy levels were significantly correlated with creatinine, triglyceride, low-density lipoprotein cholesterol, endogenous creatinine clearance rate, glycosylated hemoglobin, and the MTHFR rs1801133 genotypes in H-type hypertension group (P < 0.05–0.001). Serum Hcy levels in the T allele carriers were higher than those in the T allele noncarriers in both H-type hypertension and control groups.ConclusionsThere was closely related between the MTHFR rs1801133 SNP and serum Hcy levels in Zhuang patients with H-type hypertension in the central region of Guangxi. The MTHFR SNP may be an important reason for the increase of serum Hcy levels in Zhuang patients with H-type hypertension in this region.

Association of a haplotype in the NRG1 gene with schizophrenia: a case-control study

Abstract Objectives Schizophrenia (SZ) is a severe multifactorial disease. NRG1 is a gene acting in the development of SZ. A number of NRG1 single nucleotide polymorphisms (SNPs) and their haplotypes are associated with SZ. In the present study, we investigated the association of a NRG1 haplotype (G-C in rs6988339-rs3757930 frame) which was reported to be associated with SZ, and two other SNPs in the same gene (rs74942016, rs80127039) whose rare missense alleles were found in SZ patients. Also, we analyzed disease associations of potential new haplotypes constructed by the variants of these SNPs. Methods We genotyped 4 SNPs in a sample consisting of 302 SZ patients and 333 controls from a local Turkish population. We tested the disease associations of these variants at single SNP, haplotype and diplotype levels in case-control design. Results At single SNP level, the CC genotype of rs3757930 was associated with SZ (p=0.038). The previously reported association of G-C haplotype in rs6988339-rs3757930 frame was absent (p=0.416), but we found another haplotype (C-G in rs3757930-rs74942016, p=0.018) and three diplotypes (A-C/G-C diplotype of rs6988339-rs3757930 frame, C-G/C-G diplotype of rs3757930-rs74942016 frame, and A-C-G/G-C-G diplotype of rs6988339-rs3757930-rs74942016 frame) associated with schizophrenia in our sample. Conclusions Our study indicated the associations of a SNP, a haplotype, and a diplotype of NRG1 with schizophrenia and supported the involvement of NRG1 gene in the development of the disease. Since our sample was collected from a limited geographic area, the associations we have reported need to be supported by further studies in different populations.

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.

Genetic impacts on nigral iron deposition in Parkinson's disease: A preliminary quantitative susceptibility mapping study.

Dysfunction of iron metabolism, especially in substantia nigra (SN), is widely acknowledged in Parkinson's disease (PD), but the genetic influence on iron deposition remains largely unknown. Thus, in this study, we aimed to investigate potential genetic impacts on iron deposition in PD. Seventy-four subjects, including 38 patients with PD and 36 age-matched normal controls, participated in this study. Imaging genetic association analysis was used to identify the specific influence of single nucleotide polymorphism (SNP) on iron-related quantitative traits (QT). Genetic effects on iron deposition at the disease level, SNP level, and their interactive effect were highlighted. Four strong SNP-QT associations were detected: rs602201-susceptibility of bilateral SN, rs198440-susceptibility of left SN, and rs7895403-susceptibility of left caudate head. Detailed analyses showed that: (1) significant iron deposition was exclusively found in bilateral SN in PD; (2) altered polymorphisms of the A allele/A- genotype of rs602201 and G allele/G- genotype of rs198440 and rs7895403 were more frequently observed in PD; (3) for rs602201, among all subjects, A- genotype carriers showed significantly increased iron content than TT genotype in bilateral SN; for rs198440 and rs7895403, G- carriers showed increased iron content than AA genotype in left SN and left caudate head, respectively; and (4) rs602201 exhibited significant SNP-by-disease interaction in bilateral SN. This study shows that rs602201 and rs198440 have a stimulative impact on nigral iron deposition in PD, which provides improved understanding of iron-related pathogenesis in PD, and specifically, that vulnerability to iron deposition in SN is genetic-based.

LMAS: evaluating metagenomic short de novo assembly methods through defined communities.

The de novo assembly of raw sequence data is key in metagenomic analysis. It allows recovering draft genomes from a pool of mixed raw reads, yielding longer sequences that offer contextual information and provide a more complete picture of the microbial community. To better compare de novo assemblers for metagenomic analysis, LMAS (Last Metagenomic Assembler Standing) was developed as a flexible platform allowing users to evaluate assembler performance given known standard communities. Overall, in our test datasets, k-mer De Bruijn graph assemblers outperformed the alternative approaches but came with a greater computational cost. Furthermore, assemblers branded as metagenomic specific did not consistently outperform other genomic assemblers in metagenomic samples. Some assemblers still in use, such as ABySS, MetaHipmer2, minia, and VelvetOptimiser, perform relatively poorly and should be used with caution when assembling complex samples. Meaningful strain resolution at the single-nucleotide polymorphism level was not achieved, even by the best assemblers tested. The choice of a de novo assembler depends on the computational resources available, the replicon of interest, and the major goals of the analysis. No single assembler appeared an ideal choice for short-read metagenomic prokaryote replicon assembly, each showing specific strengths. The choice of metagenomic assembler should be guided by user requirements and characteristics of the sample of interest, and LMAS provides an interactive evaluation platform for this purpose. LMAS is open source, and the workflow and its documentation are available at https://github.com/B-UMMI/LMAS and https://lmas.readthedocs.io/, respectively.

Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles.

Using high-dimensional genetic variants such as single nucleotide polymorphisms (SNP) to predict complex diseases and traits has important applications in basic research and other clinical settings. For example, predicting gene expression is a necessary first step to identify (putative) causal genes in transcriptome-wide association studies. Due to weak signals, high-dimensionality, and linkage disequilibrium (correlation) among SNPs, building such a prediction model is challenging. However, functional annotations at the SNP level (e.g., as epigenomic data across multiple cell- or tissue-types) are available and could be used to inform predictor importance and aid in outcome prediction. Existing approaches to incorporate annotations have been based mainly on (generalized) linear models. Bayesian additive regression trees (BART), in contrast, is a reliable method to obtain high-quality nonlinear out of sample predictions without overfitting. Unfortunately, the default prior from BART may be too inflexible to handle sparse situations where the number of predictors approaches or surpasses the number of observations. Motivated by our real data application, this article proposes an alternative prior based on the logit normal distribution because it provides a framework that is adaptive to sparsity and can model informative functional annotations. It also provides a framework to incorporate prior information about the between SNP correlations. Computational details for carrying out inference are presented along with the results from a simulation study and a genome-wide prediction analysis of the Alzheimer's Disease Neuroimaging Initiative data.

Effect of schizophrenia risk gene polymorphisms on cognitive and neural plasticity

A recent Chinese genome-wide association study found evidence for 58 out of the 128 schizophrenia-associated variants previously discovered in Western samples by the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC). However, the functional impact of these trans-ancestry genome-wide single-nucleotide polymorphisms (SNPs) is not clear. In the current study, we examined the roles of trans-ancestry SNPs in cognitive and neural plasticity. We first performed a behavioral study of 547 healthy volunteers, who received month-long working memory training, and working memory capability assessment both before and after the training. A separate sample of 101 subjects received the same training and received fMRI scans during a working memory task, both before and after the training. The behavioral study found a significant association between the polygenic risk score (PRS) and behavioral plasticity, with higher schizophrenia risk scores being linked to less plasticity. At the SNP level, rs36068923 showed a significant signal, with the risk allele being associated with less plasticity. The fMRI study further found that the PRS and rs36068923 polymorphism were associated with training-induced changes in striatal activation, with higher PRS and the risk allele of rs36068923 being linked to less brain plasticity. In sum, this study found that a high genetic risk for schizophrenia was associated with less plasticity at both behavioral and neural levels. These results provide new insights into the neural and cognitive mechanisms linking genes to schizophrenia.

Whole genome sequencing of &lt;i&gt;Acinetobacter baumannii&lt;/i&gt; strains isolated from hospital patients in the northern territories of the Tyumen region

Introduction. is to The analysis of the genetic relatedness of isolates aiming to find the source of infection is an important task of nosocomial infection control. The most common causative agent of healthcare-associated infections is Acinetobacter baumannii.Objective. To evaluate the results of whole genome sequencing of A. baumannii bacteria isolated from clinical samples of patients undergoing inpatient treatment in the northern territories of the Tyumen region.Materials and methods. Nine isolates of A. baumannii from the clinical material of patients were studied. Bacterial cultures were identified by mass spectrometry. Whole genome sequencing, multilocus sequence typing and search for markers of antibiotic resistance were performed.Results. The studied strains belonged to sequence types ST2 and ST187, and to the international clonal complex CC2. All A. baumannii isolates were found to have beta-lactamase genes, as well as genes for resistance to aminoglycosides, to the MLS group of antibiotics, and to tetracyclines. The presence of a cluster of genes associated with virulence was detected: those responsible for the synthesis of acinetobactin and iron binding, surface antigen 1 and porin.Conclusion. Based on data of a single nucleotide polymorphism (SNP) analysis, A. baumannii isolates from the clinical material of patients of healthcare institution #1 belong mainly to one bacterial strain. Isolates of A. baumannii from the clinical material of patients of healthcare institution #2 are closely related. The ability to distinguish clinical isolates of A. baumannii at the level of several SNPs per genome will improve the identification of the source of infection, and whole genome sequencing data can contribute to the rational prescription of antibiotic therapy and the correction of disinfection and antiseptic measures.

Accuracy of genotype imputation to whole genome sequencing level using different populations of Nile tilapia

A cost-effective strategy to obtain ultra-dense genomic information is to sequence part of population and perform imputation from lower density genotypes to sequence level for the remaining animals. The aims of this study were to evaluate the feasibility of genotype imputation from medium density to sequence level in Nile tilapia and to investigate the impacts of size and origin of reference population in the accuracy of imputation. Genomic DNA was extracted from fin-clip samples of 326 animals from 3 different populations (PA, PB and PC). After sequencing, alignment, variant calling and quality control of genotypes, approximately 4.6 million of single-nucleotide polymorphisms (SNPs) in common to all populations were retained and used for further imputation analyses. Four scenarios were evaluated to assess imputation accuracy on each population, including: two reference sizes (10 or 90% of animals of each reference population) and two reference origins (two different populations only or all three populations used as reference). The animals in the validation set had part of their genotypes masked keeping only 49,216 SNPs available and the accuracy of imputation was assessed using the correlation between the imputed and observed genotypes (R2). Imputation was carried out using FImpute3 software. At individual level, the R2 showed intermediate values ranging from 0.37 ± 0.04 to 0.56 ± 0.07 for PA, 0.43 ± 0.05 to 0.58 ± 0.08 for PB and 0.43 ± 0.05 to 0.58 ± 0.07 for PC. An increase in the R2 was observed when 90% of animals from the same population were used as reference in comparison to only 10% (0.37 ± 0.04 to 0.54 ± 0.07 for PA, 0.43 ± 0.05 to 0.57 ± 0.07 for PB and 0.43 ± 0.05 to 0.58 ± 0.07 for PC). At SNP level, the use of all three populations as reference yielded the best results in terms of number of SNPs imputed with accuracy greater than 0.8. On average, 676,233 ± 142,291, 666,559 ± 52,648 and 592,187 ± 89,663 SNPs were imputed with accuracy >0.8 for PA, PB and PC, respectively. Considering only these highly accurate imputed SNPs, the average imputation accuracy of samples was equal to 0.95 ± 0.06 for PA and 0.92 ± 0.07 for PB and PC, for scenarios that included more animals as reference (90% of same population as reference, two and three populations). There were no significant differences for R2 between scenarios that used 90% of animals from the same population and used animals from the three population as reference showing that the strategy of using information from other population to increase the reference population had minor effect on accuracy of imputation. In conclusion, it was feasible to impute from 50 K to approximately 700 K with high accuracy using tilapia sequence data. We also expect that the use of more animals from these populations or animals from ascending lines as reference could help in the imputation process to obtain millions of imputed SNPs with high accuracy.

Evidence of gene-gene interaction in hidradenitis suppurativa: a nationwide registry study of Danish twins.

Hidradenitis suppurativa (HS) is a recurrent inflammatory skin disease that, apart from rare causative loss-of-function mutations, has a widely unknown genetic aetiology. To estimate the relative importance of genetic and environmental factors underlying susceptibility to HS. Via the Danish Twin Registry and the Danish National Patient Registry we pulled together information on zygosity with that of HS status. Cases of HS were identified by the International Classification of Diseases (ICD)-8 (705·91) and ICD-10 (L73·2). Heritability was assessed by the classic biometric model and the possibility of gene-gene interaction via the multilocus modelling approach. Among 100 044 registered twins, we found 170 twins (from 163 pairs) diagnosed with HS. The seven concordant pairs were all monozygotic. Monozygotic twins had a case-wise concordance rate of 28% [95% confidence interval (CI) 7-49], corresponding to a familial risk of 73 (95% CI 13-133) times that of the background population. The biometrical modelling suggested a heritability of 0·80 (95% CI 0·67-0·93), and the multilocus index estimate was 230 (95% CI 60-400). This is highly indicative of gene-gene interactions, with the possibility of up to six interacting loci. This twin study was substantially larger and employed a more valid phenotype than previous studies. Genetics account for the majority of HS susceptibility, and HS is most likely caused by gene-gene interactions rather than monogenetic mutations or solely additive genetic factors. New approaches aimed at assessing potential interactions at a single-nucleotide polymorphism (SNP)-SNP level should be implemented in future HS genome-wide association studies.