Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes.

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.

Hogwash: three methods for genome-wide association studies in bacteria.

Bacterial genome-wide association studies (bGWAS) capture associations between genomic variation and phenotypic variation. Convergence-based bGWAS methods identify genomic mutations that occur independently multiple times on the phylogenetic tree in the presence of phenotypic variation more often than is expected by chance. This work introduces hogwash, an open source R package that implements three algorithms for convergence-based bGWAS. Hogwash additionally contains two burden testing approaches to perform gene or pathway analysis to improve power and increase convergence detection for related but weakly penetrant genotypes. To identify optimal use cases, we applied hogwash to data simulated with a variety of phylogenetic signals and convergence distributions. These simulated data are publicly available and contain the relevant metadata regarding convergence and phylogenetic signal for each phenotype and genotype. Hogwash is available for download from GitHub.

Validation of a Miniaturized Permeability Assay Compatible with CRISPR-Mediated Genome-Wide Screen.

The impermeability of the luminal endothelial cell monolayer is crucial for the normal performance of the vascular and lymphatic systems. A key to this function is the integrity of the monolayer's intercellular junctions. The known repertoire of junction-regulating genes is incomplete. Current permeability assays are incompatible with high-throughput genome-wide screens that could identify these genes. To overcome these limitations, we designed a new permeability assay that consists of cell monolayers grown on ~150 µm microcarriers (MCs). Each MC functions as a miniature individual assay of permeability (MAP). We demonstrate that false-positive results can be minimized, and that MAP sensitivity to thrombin-induced increase in monolayer permeability is similar to the sensitivity of impedance measurement. We validated the assay by showing that the expression of single guide RNAs (sgRNAs) that target genes encoding known thrombin signaling proteins blocks effectively thrombin-induced junction disassembly, and that MAPs carrying such cells can be separated effectively by fluorescence-assisted sorting from those that carry cells expressing non-targeting sgRNAs. These results indicate that MAPs are suitable for high-throughput experimentation and for genome-wide screens for genes that mediate the disruptive effect of thrombin on endothelial cell junctions.

Rare autosomal trisomies: comparison of detection through cell-free DNA analysis and direct chromosome preparation of chorionic villus samples.

OBJECTIVE: Direct chromosome preparations of chorionic villus samples (CVS) and cell-free DNA (cfDNA) testing both involve analysis of the trophoblastic cell lineage. The aim of this study was to compare the spectrum of rare autosomal trisomies (RATs) detected by these two approaches and assess the available information on their clinical significance. METHODS: Data from 10 reports on genome-wide cfDNA testing were pooled to determine which chromosomes were most frequently involved in RAT-positive cases, and pregnancy outcome information was reviewed. CVS information was obtained from an updated database of 76 102 consecutive CVS analyses performed over a period of 18 years at TOMA laboratory, in which trophoblastic and mesenchymal layers were analyzed and amniotic fluid cell analysis was recommended for RAT-positive cases. Chromosomes involved and presence of confined placental mosaicism, true fetal mosaicism and uniparental disomy (UPD) for imprinted chromosomes were assessed. Also evaluated were the frequency and types of RATs in products of conception. RESULTS: RATs were present in 634 of 196 662 (0.32%) cfDNA samples and 237 of 57 539 (0.41%) CVS trophoblast samples (P < 0.01). The frequency of RATs varied over 8-fold between the cfDNA reports. Confirmation of abnormality through amniocentesis was more likely when RATs were ascertained through cfDNA (14 of 151; 9.3%) than through CVS trophoblasts (seven of 237; 3.0%) (P < 0.01). In cfDNA-ascertained cases, trisomies 15, 16 and 22, which are associated with fetal loss, were identified proportionately more often. Of 151 cases with RAT identified by cfDNA and outcome information available, 41.1% resulted in normal live birth; 27.2% in fetal loss; 7.3% had phenotypic abnormality detected through ultrasound or other follow-up evaluation; 2.0% had a clinically significant UPD; and 14.6% had fetal growth restriction or low birth weight. All autosomes were involved in trisomies in products of conception; the most common RATs detected were trisomies 16, 22 and 15 with a frequency of > 9% each. CONCLUSIONS: Although there are strong parallels between RATs ascertained through cfDNA analysis and direct chromosome preparation of CVS, caution is needed in applying conclusions from CVS analysis to cfDNA testing, and vice versa. RATs identified through genome-wide cfDNA tests have uncertain risks for fetal loss, growth restriction or fetal abnormality. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease.

Pathogens have been a strong driving force for natural selection. Therefore, understanding how human genetic differences impact infection-related cellular traits can mechanistically link genetic variation to disease susceptibility. Here we report the Hi-HOST Phenome Project (H2P2): a catalog of cellular genome-wide association studies (GWAS) comprising 79 infection-related phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance for infection-associated phenotypes ranging from pathogen replication to cytokine production. We combined H2P2 with clinical association data from patients to identify a SNP near CXCL10 as a risk factor for inflammatory bowel disease. A SNP in the transcriptional repressor ZBTB20 demonstrated pleiotropy, likely through suppression of multiple target genes, and was associated with viral hepatitis. These data are available on a web portal to facilitate interpreting human genome variation through the lens of cell biology and should serve as a rich resource for the research community.

Novel applications of array comparative genomic hybridization in molecular diagnostics.

INTRODUCTION: In 2004, the implementation of array comparative genomic hybridization (array comparative genome hybridization [CGH]) into clinical practice marked a new milestone for genetic diagnosis. Array CGH and single-nucleotide polymorphism (SNP) arrays enable genome-wide detection of copy number changes in a high resolution, and therefore microarray has been recognized as the first-tier test for patients with intellectual disability or multiple congenital anomalies, and has also been applied prenatally for detection of clinically relevant copy number variations in the fetus. Area covered: In this review, the authors summarize the evolution of array CGH technology from their diagnostic laboratory, highlighting exonic SNP arrays developed in the past decade which detect small intragenic copy number changes as well as large DNA segments for the region of heterozygosity. The applications of array CGH to human diseases with different modes of inheritance with the emphasis on autosomal recessive disorders are discussed. Expert commentary: An exonic array is a powerful and most efficient clinical tool in detecting genome wide small copy number variants in both dominant and recessive disorders. However, whole-genome sequencing may become the single integrated platform for detection of copy number changes, single-nucleotide changes as well as balanced chromosomal rearrangements in the near future.

Using Genomic Data to Find Disease-Modifying Loci in Huntington's Disease (HD).

In this chapter, genetic modifiers are defined, and the rationale for investigating them in HD explained. Issues involved in modeling the phenotype are discussed, using age at motor onset as an example. The statistical methods for analyzing genetic data (linkage and association) are discussed, along with the advantages and disadvantages of each. In particular, the advantage of a genome-wide approach over one based on candidate genes is stressed. Genome-wide association studies (GWAS) are current method of choice to detect genetic modifiers. The power of GWAS is discussed, along with sources of error, and how these might be detected and corrected. Extensions to GWAS, such as gene- and pathway-wide analyses, are discussed, and also how GWAS may be used to estimate genetic risks and trait heritability. Since GWAS are most effective to detect common genetic variants, methods for analyzing rare variation are also discussed. The uses of other types of genomic data (notably, expression) are discussed, and how they might be integrated with genetic data to find causal genes and variants. The chapter ends with a short overview of future prospects for detecting genetic modifiers of HD.

An Overview of Genome-Wide Association Studies.

Genome-wide association study (GWAS) is a powerful study design to identify genetic variants of a trait and, in particular, detect the association between common single-nucleotide polymorphisms (SNPs) and common human diseases such as heart disease, inflammatory bowel disease, type 2 diabetes, and psychiatric disorders. The standard strategy of population-based case-control studies for GWAS is illustrated in this chapter. We provide an overview of the concepts underlying GWAS, as well as provide guidelines for statistical methods performed in GWAS.

Functional mapping and annotation of genetic associations with FUMA.

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

SparRec: An effective matrix completion framework of missing data imputation for GWAS.

Genome-wide association studies present computational challenges for missing data imputation, while the advances of genotype technologies are generating datasets of large sample sizes with sample sets genotyped on multiple SNP chips. We present a new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optimization models of SparRec, based on low-rank and low number of co-clusters of matrices, are different from current statistics methods. While our low-rank matrix completion (LRMC) model is similar to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new. (2) SparRec, as other matrix completion methods, is flexible to be applied to missing data imputation for large meta-analysis with different cohorts genotyped on different sets of SNPs, even when there is no reference panel. This kind of meta-analysis is very challenging for current statistics based methods. (3) SparRec has consistent performance and achieves high recovery accuracy even when the missing data rate is as high as 90%. Compared with Mendel-Impute, our low-rank based method achieves similar accuracy and efficiency, while the co-clustering based method has advantages in running time. The testing results show that SparRec has significant advantages and competitive performance over other state-of-the-art existing statistics methods including Beagle and fastPhase.

Altools: a user friendly NGS data analyser.

BACKGROUND: Genotyping by re-sequencing has become a standard approach to estimate single nucleotide polymorphism (SNP) diversity, haplotype structure and the biodiversity and has been defined as an efficient approach to address geographical population genomics of several model species. To access core SNPs and insertion/deletion polymorphisms (indels), and to infer the phyletic patterns of speciation, most such approaches map short reads to the reference genome. Variant calling is important to establish patterns of genome-wide association studies (GWAS) for quantitative trait loci (QTLs), and to determine the population and haplotype structure based on SNPs, thus allowing content-dependent trait and evolutionary analysis. Several tools have been developed to investigate such polymorphisms as well as more complex genomic rearrangements such as copy number variations, presence/absence variations and large deletions. The programs available for this purpose have different strengths (e.g. accuracy, sensitivity and specificity) and weaknesses (e.g. low computation speed, complex installation procedure and absence of a user-friendly interface). Here we introduce Altools, a software package that is easy to install and use, which allows the precise detection of polymorphisms and structural variations. RESULTS: Altools uses the BWA/SAMtools/VarScan pipeline to call SNPs and indels, and the dnaCopy algorithm to achieve genome segmentation according to local coverage differences in order to identify copy number variations. It also uses insert size information from the alignment of paired-end reads and detects potential large deletions. A double mapping approach (BWA/BLASTn) identifies precise breakpoints while ensuring rapid elaboration. Finally, Altools implements several processes that yield deeper insight into the genes affected by the detected polymorphisms. Altools was used to analyse both simulated and real next-generation sequencing (NGS) data and performed satisfactorily in terms of positive predictive values, sensitivity, the identification of large deletion breakpoints and copy number detection. CONCLUSIONS: Altools is fast, reliable and easy to use for the mining of NGS data. The software package also attempts to link identified polymorphisms and structural variants to their biological functions thus providing more valuable information than similar tools.

Genetic Predictors of Depressive Symptoms in the Look AHEAD Trial.

OBJECTIVES: Numerous studies have found elevated depressive symptoms among individuals with Type 2 diabetes, yet the mechanisms remain unclear. We examined whether genetic loci previously associated with depressive symptoms predict depressive symptoms among overweight/obese individuals with Type 2 diabetes or change in depressive symptoms during behavioral weight loss. METHODS: The Illumina CARe iSelect (IBC) chip and Cardiometabochip were characterized in 2118 overweight or obese participants with Type 2 diabetes from Look AHEAD (Action for Health in Diabetes), a randomized trial to determine the effects of intensive life-style intervention and diabetes support and education on cardiovascular morbidity and mortality. Primary analyses focused on baseline Beck Depression Inventory (BDI) scores and depressive symptom change at 1 year. RESULTS: Of eight single nucleotide polymorphisms (SNPs) in six loci, three a priori SNPs in two loci (chromosome 5: rs60271; LBR: rs2230419, rs1011319) were associated with baseline BDI scores, but in the opposite direction of prior research. In joint analysis of 90,003 IBC and Cardiometabochip SNPs, rs1543654 in the region of KCNE1 predicted change in BDI scores at Year 1 in diabetes support and education (ß = -1.05, standard error [SE] = 0.21, p = 6.9 × 10(-7)) at the level of chip-wide significance, while also showing a nominal association with baseline BDI (ß = 0.35, SE = 0.16, p = .026). Adjustment for antidepressant medication and/or limiting analyses to non-Hispanic white individuals did not meaningfully alter results. CONCLUSIONS: Previously reported genetic associations with depressive symptoms did not replicate in this cohort of overweight/obese individuals with Type 2 diabetes. We identified KCNE1 as a potential novel locus associated with depressive symptoms.

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems.

High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g., processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderately-sized datasets and to a few hours for large-scale datasets.

Epigenetics for anthropologists: An introduction to methods.

The study of epigenetics, or chemical modifications to the genome that may alter gene expression, is a growing area of interest for social scientists. Anthropologists and human biologists are interested in epigenetics specifically, as it provides a potential link between the environment and the genome, as well as a new layer of complexity for the study of human biological variation. In pace with the rapid increase in interest in epigenetic research, the range of methods has greatly expanded over the past decade. The primary objective of this article is to provide an overview of the current methods for assaying DNA methylation, the most commonly studied epigenetic modification. We will address considerations for all steps required to plan and conduct an analysis of DNA methylation, from appropriate sample collection, to the most commonly used methods for laboratory analyses of locus-specific and genome-wide approaches, and recommendations for statistical analyses. Key challenges in the study of DNA methylation are also discussed, including tissue specificity, the stability of measures, timing of sample collection, statistical considerations, batch effects, and challenges related to analysis and interpretation of data. Our hope is that this review serves as a primer for anthropologists and human biologists interested in incorporating epigenetic data into their research programs.

Methylome-wide association study of schizophrenia: identifying blood biomarker signatures of environmental insults.

IMPORTANCE: Epigenetic studies present unique opportunities to advance schizophrenia research because they can potentially account for many of its clinical features and suggest novel strategies to improve disease management. OBJECTIVE: To identify schizophrenia DNA methylation biomarkers in blood. DESIGN, SETTING, AND PARTICIPANTS: The sample consisted of 759 schizophrenia cases and 738 controls (N = 1497) collected in Sweden. We used methyl-CpG-binding domain protein-enriched genome sequencing of the methylated genomic fraction, followed by next-generation DNA sequencing. We obtained a mean (SD) number of 68 (26.8) million reads per sample. This massive data set was processed using a specifically designed data analysis pipeline. Critical top findings from our methylome-wide association study (MWAS) were replicated in independent case-control participants using targeted pyrosequencing of bisulfite-converted DNA. MAIN OUTCOMES AND MEASURES: Status of schizophrenia cases and controls. RESULTS: Our MWAS suggested a considerable number of effects, with 25 sites passing the highly conservative Bonferroni correction and 139 sites significant at a false discovery rate of 0.01. Our top MWAS finding, which was located in FAM63B, replicated with P = 2.3 × 10-10. It was part of the networks regulated by microRNA that can be linked to neuronal differentiation and dopaminergic gene expression. Many other top MWAS results could be linked to hypoxia and, to a lesser extent, infection, suggesting that a record of pathogenic events may be preserved in the methylome. Our findings also implicated a site in RELN, one of the most frequently studied candidates in methylation studies of schizophrenia. CONCLUSIONS AND RELEVANCE: To our knowledge, the present study is one of the first MWASs of disease with a large sample size using a technology that provides good coverage of methylation sites across the genome. Our results demonstrated one of the unique features of methylation studies that can capture signatures of environmental insults in peripheral tissues. Our MWAS suggested testable hypotheses about disease mechanisms and yielded biomarkers that can potentially be used to improve disease management.

Balony: a software package for analysis of data generated by synthetic genetic array experiments.

BACKGROUND: Synthetic Genetic Array (SGA) analysis is a procedure which has been developed to allow the systematic examination of large numbers of double mutants in the yeast Saccharomyces cerevisiae. The aim of these experiments is to identify genetic interactions between pairs of genes. These experiments generate a number of images of ordered arrays of yeast colonies which must be analyzed in order to quantify the extent of the genetic interactions. We have designed software that is able to analyze virtually any image of regularly arrayed colonies and allows the user significant flexibility over the analysis procedure. RESULTS: "Balony" is freely available software which enables the extraction of quantitative data from array-based genetic screens. The program follows a multi-step process, beginning with the optional preparation of plate images from single or composite images. Next, the colonies are identified on a plate and the pixel area of each is measured. This is followed by a scoring module which normalizes data and pairs control and experimental data files. The final step is analysis of the scored data, where the strength and reproducibility of genetic interactions can be visualized and cross-referenced with information on each gene to provide biological insights into the results of the screen. CONCLUSIONS: Analysis of SGA screens with Balony can be either automated or highly interactive, enabling the user to customize the process to their specific needs. Quantitative data can be extracted at each stage for external analysis if required. Beyond SGA, this software can be used for analyzing many types of plate-based high-throughput screens.

Microfluidic genome-wide profiling of intrinsic electrical properties in Saccharomyces cerevisiae.

Methods to analyze the intrinsic physical properties of cells - for example, size, density, rigidity, or electrical properties - are an active area of interest in the microfluidics community. Although the physical properties of cells are determined at a fundamental level by gene expression, the relationship between the two remains exceptionally complex and poorly characterized, limiting the adoption of intrinsic separation technologies. To improve our current understanding of how a cell's genotype maps to a measurable physical characteristic and quantitatively investigate the potential of using these characteristics as biomarkers, we have developed a novel screen that combines microfluidic cell sorting with high-throughput sequencing and the haploid yeast deletion library to identify genes whose functions modulate one such characteristic - intrinsic electrical properties. Using this screen, we are able to establish a high-content electrical profile of the haploid yeast gene deletion strains. We find that individual genetic deletions can appreciably alter the electrical properties of cells, affecting ~10% of the 4432 gene deletion strains screened. Additionally, we find that gene deletions affecting electrical properties in specific ways (i.e. increasing or decreasing effective conductivity at higher or lower electric field frequencies) are strongly associated with an enriched subset of fundamental biological processes that can be traced to specific pathways and complexes. The screening approach demonstrated here and the attendant results are immediately applicable to the intrinsic separations community.

Genetics of serum BDNF: meta-analysis of the Val66Met and genome-wide association study.

OBJECTIVES: Lower levels of serum brain derived neurotrophic factor (BDNF) is one of the best known biomarkers of depression. To identify genetic variants associated with serum BDNF, we tested the Val66Met (rs6265) functional variant and conducted a genome-wide association scan (GWAS). METHODS: In a community-based sample (N = 2054; aged 19-101, M = 51, SD = 15) from Sardinia, Italy, we measured serum BDNF concentration and conducted a GWAS. RESULTS: We estimated the heritability of serum BDNF to be 0.48 from sib-pairs. There was no association between serum BDNF and Val66Met in the SardiNIA sample and in a meta-analysis of published studies (k = 13 studies, total n = 4727, P = 0.92). Although no genome-wide significant associations were identified, some evidence of association was found in the BDNF gene (rs11030102, P = 0.001) and at two loci (rs7170215, P = 4.8 × 10⁻5 and rs11073742 P = 1.2 × 10⁻5) near and within NTRK3 gene, a neurotrophic tyrosine kinase receptor. CONCLUSIONS: Our study and meta-analysis of the literature indicate that the BDNF Val66Met variant is not associated with serum BDNF, but other variants in the BDNF and NTRK3 genes might regulate the level of serum BDNF.

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies.

BACKGROUND: The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate. RESULTS: We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures. CONCLUSION: T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially.

METU-SNP: an integrated software system for SNP-complex disease association analysis.

Recently, there has been increasing research to discover genomic biomarkers, haplotypes, and potentially other variables that together contribute to the development of diseases. Single Nucleotide Polymorphisms (SNPs) are the most common form of genomic variations and they can represent an individual’s genetic variability in greatest detail. Genome-wide association studies (GWAS) of SNPs, high-dimensional case-control studies, are among the most promising approaches for identifying disease causing variants. METU-SNP software is a Java based integrated desktop application specifically designed for the prioritization of SNP biomarkers and the discovery of genes and pathways related to diseases via analysis of the GWAS case-control data. Outputs of METU-SNP can easily be utilized for the downstream biomarkers research to allow the prediction and the diagnosis of diseases and other personalized medical approaches. Here, we introduce and describe the system functionality and architecture of the METU-SNP. We believe that the METU-SNP will help researchers with the reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting the development of personalized medicine approaches and targeted drug discoveries.