Single Nucleotide Polymorphisms Pairs Research Articles

Selection of Genome-Wide SNPs for Pooled Allelotyping Assays Useful for Population Monitoring.

Parasitic worms are serious pests of humans, livestock, and crops worldwide. Multiple management strategies are employed in order to reduce their impact, and some of these may affect their genome and population allelic frequency distribution. The evolution of chemical resistance, ecological changes, and pest dispersal has allowed an increasing number of pests to become difficult to control with current management methods. Their lifestyle limits the use of ecological and individual-based management of populations. There is a need to develop rapid, affordable, and simple diagnostics to assess the efficacy of management strategies and delay the evolution of resistance to these strategies. This study presents a multilocus, equal-representation, whole-genome pooled single nucleotide polymorphisms (SNPs) selection approach as a monitoring tool for the ovine nematode parasite Haemonchus contortus. The SNP selection method used two reference genomes of different quality, then validated these SNPs against a high-quality recent genome assembly. From over 11 million high-quality SNPs identified, 334 SNPs were selected, of which 262 were species-specific, yielded similar allele frequencies when assessed as multiple individuals or as pools of individuals, and suitable to distinguish mixed nematode isolate pools from single isolate pools. As a proof-of-concept, 21 Australian H. contortus populations with various phenotypes and genotypes were screened. This analysis confirmed the overall low level of genetic differentiation between populations collected from the field, but clearly identifying highly inbred populations, and populations showing genetic signatures associated with chemical resistance. The analysis showed that 66% of the SNPs were necessary for stability in assessing population genetic patterns, and SNP pairs did not show linkage according to allelic frequencies across the 21 populations. This method demonstrates that ongoing monitoring of parasite allelic frequencies and genetic changes can be achieved as a management assessment tool to identify drug-treatment failure, population incursions, and inbreeding signatures due to selection. The SNP selection method could also be applied to other parasite species.

Interpretable network-guided epistasis detection.

BackgroundDetecting epistatic interactions at the gene level is essential to understanding the biological mechanisms of complex diseases. Unfortunately, genome-wide interaction association studies involve many statistical challenges that make such detection hard. We propose a multi-step protocol for epistasis detection along the edges of a gene-gene co-function network. Such an approach reduces the number of tests performed and provides interpretable interactions while keeping type I error controlled. Yet, mapping gene interactions into testable single-nucleotide polymorphism (SNP)-interaction hypotheses, as well as computing gene pair association scores from SNP pair ones, is not trivial.ResultsHere we compare 3 SNP-gene mappings (positional overlap, expression quantitative trait loci, and proximity in 3D structure) and use the adaptive truncated product method to compute gene pair scores. This method is non-parametric, does not require a known null distribution, and is fast to compute. We apply multiple variants of this protocol to a genome-wide association study dataset on inflammatory bowel disease. Different configurations produced different results, highlighting that various mechanisms are implicated in inflammatory bowel disease, while at the same time, results overlapped with known disease characteristics. Importantly, the proposed pipeline also differs from a conventional approach where no network is used, showing the potential for additional discoveries when prior biological knowledge is incorporated into epistasis detection.

Association of ADIPOQ Single-Nucleotide Polymorphisms with the Two Clinical Phenotypes Type 2 Diabetes Mellitus and Metabolic Syndrome in a Kinh Vietnamese Population.

PurposeGenetic factors play an important role in the development of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS). However, few genetic association studies related to these disorders have been performed with Vietnamese subjects. In this study, the potential associations of ADIPOQ single nucleotide polymorphisms (SNPs) with T2DM and MetS in a Kinh Vietnamese population were investigated.Patients and MethodsA study with 768 subjects was conducted to examine the associations of four ADIPOQ SNPs (rs266729, rs1501299, rs3774261, and rs822393) primarily with T2DM and secondarily with MetS. The TaqMan SNP genotyping assay was used to determine genotypes from subjects’ DNA samples.ResultsAfter statistical adjustment for age, sex, and body mass index, the ADIPOQ SNP rs266729 was found to be associated with increased risk of T2DM under multiple inheritance models: codominant (OR = 2.30, 95% CI = 1.16–4.58), recessive (OR = 2.17, 95% CI = 1.11–4.26), and log-additive (OR = 1.32, 95% CI = 1.02–1.70). However, rs1501299, rs3774261, and rs822393 were not associated with risk for T2DM. Additionally, rs266729, rs3774261, and rs822393 were statistically associated with MetS, while rs1501299 was not. Haplotype analysis showed a strong linkage disequilibrium between the SNP pairs rs266729/rs822393 and rs1501299/rs3774261, and the haplotype rs266729(G)/rs822393(T) was not statistically associated with MetS.ConclusionThe results show that rs266729 is a lead candidate SNP associated with increased risk of developing T2DM and MetS in a Kinh Vietnamese population, while rs3774261 is associated with MetS only. Further functional characterization is needed to uncover the mechanism underlying the potential genotype–phenotype associations.

The pattern of linkage disequilibrium in Dual-Purpose Belgian Blue cattle.

Quantifying the level of linkage disequilibrium (LD), non-random association of alleles at two or more loci, is important to determine the number of markers needed for genomic selection. The aims of this study were to evaluate the extent of LD in Dual-Purpose Belgian Blue (DPBB) and to compare the level of LD in DPBB with that of Walloon Holstein. Data of 28,427 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes(BTA), of 639 DPBB and 398 Holstein bulls were used. The level of LD between pairwise SNPs separated by up to 10Mb was evaluated, separately for each breed, using the squared correlation of the alleles at two loci. The analysis of molecular variance showed that the percentage of variation within populations (85.48%) was higher than between populations (14.52%). However, permutation tests showed a significant genetic differentiation between the two studied populations (p<.01). The average LD found between adjacent SNP pairs in DPBB (0.16 (SD=0.22)) was generally lower than in Holstein (0.23 (SD=0.27)). The proportion of SNPs in useful LD (r2 >0.30) within a genomic distance of ≤0.10Mb between SNPs was 18.58% and 28.23% in DPBB and Holstein bulls, respectively. In both breeds, the effective population size decreased over generations; however, the decline was greater in DPBB than that in Holstein. Based on results, it can be concluded that at least 68,000 SNPs are needed for implementing genomic selection in DPBB cattle with enough accuracy.

The ε4‐bearing TOMM40‐APOE‐APOC1 haplotype but not the ε4 allele confers an exceptionally high risk of Alzheimer’s disease

The APOE gene harboring the ε2 and ε4 alleles encoded by minor alleles of rs7412 and rs429358 single nucleotide polymorphisms (SNPs), respectively, is one of the most studied genes in humans. Despite that, the problem of whether the associations between these alleles and the late-onset Alzheimer's disease (referred to as AD) are due to these alleles themselves remains controversial. This problem is complicated by an inherent heterogeneity in genetic predisposition to AD, which is common for complex diseases in late life.We used a comprehensive, two-stage strategy to gain insights into a more homogeneous genetic architecture of AD in subjects of European ancestry in the TOMM40-APOE-APOC1 region, emphasizing the role of the ε4 allele. First, we examined differences in linkage disequilibrium (LD) patterns in AD-affected and unaffected subjects who carry or do not carry the ε4 or ε2 allele to prioritize SNP pairs according to the LD differences. Second, rs429358 and SNPs from the top pair were selected to construct compound genotypes and associate them with AD. The discovery analyses used a mega-sample of 2,673 AD-affected and 16,246 cognitively normal subjects from four independent studies. The landmark Alzheimer's Disease Genetics Consortium initiative cohort (3,662 AD-cases and 1,541 controls) was used for validation.The top LD difference between the most unfavorable AD-affected ε2-free sample and the most favorable AD- and ε4-free sample was for rs2075650 (TOMM40) and rs12721046 (APOC1) SNPs, Δr=77.6% (p<10-100 ). The meta-analysis results showed the exceptionally high risk of AD for carriers of the full minor allele homozygote comprised of rs429358, rs2075650, and rs12721046 SNPs, odds ratio=18.1, p=1.96×10-77 . This risk was 4.37-fold (p=1.34×10-3 ) higher than that for carriers of two copies of the ε4 alleles who do not have minor alleles of rs2075650 and rs12721046.This study provides compelling evidence that the strongest genetic predisposition to AD in the APOE region is credited to a haplotype comprised of the ε4 allele and TOMM40 (rs2075650) and APOC1 (rs12721046) minor alleles, but not to the ε4 allele itself. This more homogeneous polygenic profile is promising for assessing subjects at an exceptionally high risk of AD.

Genome-wide approaches delineate the additive, epistatic, and pleiotropic nature of variants controlling fatty acid composition in peanut (Arachis hypogaea L.).

The fatty acid composition of seed oil is a major determinant of the flavor, shelf-life, and nutritional quality of peanuts. Major QTLs controlling high oil content, high oleic content, and low linoleic content have been characterized in several seed oil crop species. Here, we employ genome-wide association approaches on a recently genotyped collection of 787 plant introduction accessions in the USDA peanut core collection, plus selected improved cultivars, to discover markers associated with the natural variation in fatty acid composition, and to explain the genetic control of fatty acid composition in seed oils. Overall, 251 single nucleotide polymorphisms (SNPs) had significant trait associations with the measured fatty acid components. Twelve SNPs were associated with two or three different traits. Of these loci with apparent pleiotropic effects, 10 were associated with both oleic (C18:1) and linoleic acid (C18:2) content at different positions in the genome. In all 10 cases, the favorable allele had an opposite effect-increasing and lowering the concentration, respectively, of oleic and linoleic acid. The other traits with pleiotropic variant control were palmitic (C16:0), behenic (C22:0), lignoceric (C24:0), gadoleic (C20:1), total saturated, and total unsaturated fatty acid content. One hundred (100) of the significantly associated SNPs were located within 1000 kbp of 55 genes with fatty acid biosynthesis functional annotations. These genes encoded, among others: ACCase carboxyl transferase subunits, and several fatty acid synthase II enzymes. With the exception of gadoleic (C20:1) and lignoceric (C24:0) acid content, which occur at relatively low abundance in cultivated peanuts, all traits had significant SNP interactions exceeding a stringent Bonferroni threshold (α = 1%). We detected 7682 pairwise SNP interactions affecting the relative abundance of fatty acid components in the seed oil. Of these, 627 SNP pairs had at least one SNP within 1000 kbp of a gene with fatty acid biosynthesis functional annotation. We evaluated 168 candidate genes underlying these SNP interactions. Functional enrichment and protein-to-protein interactions supported significant interactions (P-value < 1.0E-16) among the genes evaluated. These results show the complex nature of the biology and genes underlying the variation in seed oil fatty acid composition and contribute to an improved genotype-to-phenotype map for fatty acid variation in peanut seed oil.

KLK3 SNP\u2013SNP interactions for prediction of prostate cancer aggressiveness

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP–SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10–9) and 3145 (P < 1 × 10–5) SNP–SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene–gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP–SNP interactions were supported by gene expression and protein–protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.

A comparative study on the unified model based multifactor dimensionality reduction methods for identifying gene-gene interactions associated with the survival phenotype

BackgroundFor gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely employed to reduce multi-levels of gene-gene interactions into high- or low-risk groups using a binary attribute. For the survival phenotype, the Cox-MDR method has been proposed using a martingale residual of a Cox model since Surv-MDR was first proposed using a log-rank test statistic. Recently, the KM-MDR method was proposed using the Kaplan-Meier median survival time as a classifier. All three methods used the cross-validation procedure to identify single nucleotide polymorphism (SNP) using SNP interactions among all possible SNP pairs. Furthermore, these methods require the permutation test to verify the significance of the selected SNP pairs. However, the unified model-based multifactor dimensionality reduction method (UM-MDR) overcomes this shortcoming of MDR by unifying the significance testing with the MDR algorithm within the framework of the regression model. Neither cross-validation nor permutation testing is required to identify SNP by SNP interactions in the UM-MDR method. The UM-MDR method comprises two steps: in the first step, multi-level genotypes are classified into high- or low-risk groups, and an indicator variable for the high-risk group is defined. In the second step, the significance of the indicator variable of the high-risk group is tested in the regression model included with other adjusting covariates. The Cox-UMMDR method was recently proposed by combining Cox-MDR with UM-MDR to identify gene-gene interactions associated with the survival phenotype. In this study, we propose two simple methods either by combining KM-MDR with UM-MDR, called KM-UMMDR or by modifying Cox-UMMDR by adjusting for the covariate effect in step 1, rather than in step 2, a process called Cox2-UMMDR. The KM-UMMDR method allows the covariate effect to be adjusted for in the regression model of step 2, although KM-MDR cannot adjust for the covariate effect in the classification procedure of step 1. In contrast, Cox2-UMMDR differs from Cox-UMMDR in the sense that the martingale residuals are obtained from a Cox model by adjusting for the covariate effect in step 1 of Cox2-UMMDR whereas Cox-UMMDR adjusts for the covariate effect in the regression model in step 2. We performed simulation studies to compare the power of several methods such as KM-UMMDR, Cox-UMMDR, Cox2-UMMDR, Cox-MDR, and KM-MDR by considering the effect of covariates and the marginal effect of SNPs. We also analyzed a real example of Korean leukemia patient data for illustration and a short discussion is provided.ResultsIn the simulation study, two different scenarios are considered: the first scenario compares the power of the cases with and without the covariate effect. The second scenario is to compare the power of cases with the main effect of SNPs versus without the main effect of SNPs. From the simulation results, Cox-UMMDR performs the best across all scenarios among KM-UMMDR, Cox2-UMMDR, Cox-MDR and KM-MDR. As expected, both Cox-UMMDR and Cox-MDR perform better than KM-UMMDR and KM-MDR when a covariate effect exists because the former adjusts for the covariate effect but the latter cannot. However, Cox2-UMMDR behaves similarly to KM-UMMDR and KM-MDR even though there is a covariate effect. This implies that the covariate effect would be more efficiently adjusted for in the regression model of the second step rather than under the classification procedure of the first step. When there is a main effect of any SNP, Cox-UMMDR, Cox2-UMMDR and KM-UMMDR perform better than Cox-MDR and KM-MDR if the main effects of SNPs are properly adjusted for in the regression model. From the simulation results of two different scenarios, Cox-UMMDR seems to be the most robust when there is either any covariate effect adjusting for or any SNP that has a main effect on the survival phenotype. In addition, the power of all methods decreased as the censoring fraction increased from 0.1 to 0.3, as heritability increased. The power of all methods seems to be greater under MAF = 0.2 than under MAF = 0.4. For illustration, both KM-UMMDR and Cox2-UMMDR were applied to identify SNP by SNP interactions with the survival phenotype to a real dataset of Korean leukemia patients.ConclusionBoth KM-UMMDR and Cox2-UMMDR were easily implemented by combining KM-MDR and Cox-MDR with UM-MDR, respectively, to detect significant gene-gene interactions associated with survival time without cross-validation and permutation testing. The simulation results demonstrate the utility of KM-UMMDR, Cox2-UMMDR and Cox-UMMDR, which outperforms Cox-MDR and KM-MDR when some SNPs with only marginal effects might mask the detection of causal epistasis. In addition, Cox-UMMDR, Cox2-UMMDR and Cox-MDR performed better than KM-UMMDR and KM-MDR when there were potentially confounding covariate effects.

Chimerism Assay Using Single Nucleotide Polymorphisms Adjacent and in Linkage-Disequilibrium Enables Sensitive Disease Relapse Monitoring after Hematopoietic Stem-Cell Transplantation.

Short tandem repeat (STR)-based chimerism analysis has been widely used for chimerism monitoring after hematopoietic stem-cell transplantation (HSCT), but technical artifacts can be problematic. We designed a chimerism assay using single nucleotide polymorphisms (SNPs) adjacent and in linkage-disequilibrium (CASAL), which doubly checked for SNP pairs, and thus could reduce background errors and increase analytical sensitivity. CASAL targeted 84 SNP pairs within 10 bp distance and in perfect linkage-disequilibrium. Using undiluted and serially diluted samples, baseline error rates, and linearity was calculated. Clinical performance of CASAL was evaluated in comparison with a conventional STR assay, using 191 posttransplant samples from 42 patients with HSCT. CASAL had ∼10 times lower baseline error rates compared to that of ordinary next-generation sequencing. Limit of detection and quantification of CASAL were estimated to be 0.09 and 0.39%, respectively, with a linear range of 0.1-100%. CASAL correlated well with STR assay (r2 = 0.99) and the higher sensitivity enabled detection of low-level recipient chimerism and earlier prediction of relapse. CASAL is a simple, analytically sensitive and accurate assay that can be used in clinical samples after HSCT with a higher performance compared to that of traditional assays. It should also be useful in other forensic and archeological testing.

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores.

Some organizations such as 23andMe and the UK Biobank have large genomic databases that they re-use for multiple different genome-wide association studies. Even research studies that compile smaller genomic databases often utilize these databases to investigate many related traits. It is common for the study to report a genetic risk score (GRS) model for each trait within the publication. Here, we show that under some circumstances, these GRS models can be used to recover the genetic variants of individuals in these genomic databases—a reconstruction attack. In particular, if two GRS models are trained by using a largely overlapping set of participants, it is often possible to determine the genotype for each of the individuals who were used to train one GRS model, but not the other. We demonstrate this theoretically and experimentally by analyzing the Cornell Dog Genome database. The accuracy of our reconstruction attack depends on how accurately we can estimate the rate of co-occurrence of pairs of single nucleotide polymorphisms within the private database, so if this aggregate information is ever released, it would drastically reduce the security of a private genomic database. Caution should be applied when using the same database for multiple analysis, especially when a small number of individuals are included or excluded from one part of the study.

A genome-wide association study (GWAS) for pH value in the meat of Berkshire pigs.

The purpose of this study is to estimate the single nucleotide polymorphism (SNP) effect for pH values affecting Berkshire meat quality. A total of 39,603 SNPs from 1,978 heads after quality control and 882 pH values were used estimate SNP effect by single step genomic best linear unbiased prediction (ssGBLUP) method. The average physical distance between adjacent SNP pairs was 61.7kbp and the number and proportion of SNPs whose minor allele frequency was below 10% were 9,573 and 24.2%, respectively. The average of observed heterozygosity and polymorphic information content was 0.32 ± 0.16 and 0.26 ± 0.11, respectively and the estimate for average linkage disequilibrium was 0.40. The heritability of pH45m and pH24h were 0.10 and 0.15 respectively. SNPs with an absolute value more than 4 standard deviations from the mean were selected as threshold markers, among the selected SNPs, protein-coding genes of pH45m and pH24h were detected in 6 and 4 SNPs, respectively. The distribution of coding genes <RFX8, CREG2, TBC1D8, EXOC6B> were detected at pH45m and <C12orf49, LOC106506010, BICC1, ANK3> were detected at pH24h.

Roles of interacting stress related genes in lifespan regulation: insights for translating experimental findings to humans

Experimental studies provided numerous evidence that caloric/dietary restriction may improve health and increase the lifespan of laboratory animals, and that the interplay among molecules that sense cellular stress signals and those regulating cell survival can play a crucial role in cell response to nutritional stressors. However, it is unclear whether the interplay among corresponding genes also plays a role in human health and lifespan. Literature about roles of cellular stressors have been reviewed, such as amino acid deprivation, and the integrated stress response (ISR) pathway in health and aging. Single nucleotide polymorphisms (SNPs) in two candidate genes (GCN2/EIF2AK4 and CHOP/DDIT3) that are closely involved in the cellular stress response to amino acid starvation, have been selected using information from experimental studies. Associations of these SNPs and their interactions with human survival in the Health and Retirement Study data have been estimated. The impact of collective associations of multiple interacting SNP pairs on survival has been evaluated, using a recently developed composite index: the SNP-specific Interaction Polygenic Risk Score (SIPRS). Significant interactions have been found between SNPs from GCN2/EIF2AK4 and CHOP/DDI3T genes that were associated with survival 85+ compared to survival between ages 75 and 85 in the total sample (males and females combined) and in females only. This may reflect sex differences in genetic regulation of the human lifespan. Highly statistically significant associations of SIPRS [constructed for the rs16970024 (GCN2/EIF2AK4) and rs697221 (CHOP/DDIT3)] with survival in both sexes also been found in this study. Identifying associations of the genetic interactions with human survival is an important step in translating the knowledge from experimental to human aging research. Significant associations of multiple SNPxSNP interactions in ISR genes with survival to the oldest old age that have been found in this study, can help uncover mechanisms of multifactorial regulation of human lifespan and its heterogeneity.

A Tool for Detecting Complementary Single Nucleotide Polymorphism Pairs in Genome-Wide Association Studies for Epistasis Testing.

Detecting interacting loci pairs has been instrumental to understand disease etiology when single locus associations do not fully account for the underlying heritability. However, the number of loci to test is prohibitively large. Epistasis test prioritization algorithms rank likely epistatic single nucleotide polymorphism (SNP) pairs to limit the number of statistical tests. Potpourri detects epistatic SNP pairs by diversifying the selected SNPs' genomic regions and investigating their co-occurrence patterns over the case cohort. It can also input and further prioritize SNPs in regulatory or coding regions. The program identifies and returns a list of prioritized SNP pairs for epistasis testing. This article describes how to use the program and the details of the input and output data.

Investigation of gene-gene interactions of clock genes for chronotype in a healthy Korean population.

Chronotype is an important moderator of psychiatric illnesses, which seems to be controlled in some part by genetic factors. Clock genes are the most relevant genes for chronotype. In addition to the roles of individual genes, gene-gene interactions of clock genes substantially contribute to chronotype. We investigated genetic associations and gene-gene interactions of the clock genes BHLHB2, CLOCK, CSNK1E, NR1D1, PER1, PER2, PER3, and TIMELESS for chronotype in 1,293 healthy Korean individuals. Regression analysis was conducted to find associations between single nucleotide polymorphism (SNP) and chronotype. For gene-gene interaction analyses, the quantitative multifactor dimensionality reduction (QMDR) method, a nonparametric model-free method for quantitative phenotypes, were performed. No individual SNP or haplotype showed a significant association with chronotype by both regression analysis and single-locus model of QMDR. QMDR analysis identified NR1D1 rs2314339 and TIMELESS rs4630333 as the best SNP pairs among two-locus interaction models associated with chronotype (cross-validation consistency [CVC] = 8/10, p = 0.041). For the three-locus interaction model, the SNP combination of NR1D1 rs2314339, TIMELESS rs4630333, and PER3 rs228669 showed the best results (CVC = 4/10, p < 0.001). However, because the mean differences between genotype combinations were minor, the clinical roles of clock gene interactions are unlikely to be critical.

Potpourri: An Epistasis Test Prioritization Algorithm via Diverse SNP Selection.

Genome-wide association studies (GWAS) explain a fraction of the underlying heritability of genetic diseases. Investigating epistatic interactions between two or more loci help to close this gap. Unfortunately, the sheer number of loci combinations to process and hypotheses prohibit the process both computationally and statistically. Epistasis test prioritization algorithms rank likely epistatic single nucleotide polymorphism (SNP) pairs to limit the number of tests. However, they still suffer from very low precision. It was shown in the literature that selecting SNPs that are individually correlated with the phenotype and also diverse with respect to genomic location leads to better phenotype prediction due to genetic complementation. Here, we propose that an algorithm that pairs SNPs from such diverse regions and ranks them can improve prediction power. We propose an epistasis test prioritization algorithm that optimizes a submodular set function to select a diverse and complementary set of genomic regions that span the underlying genome. The SNP pairs from these regions are then further ranked w.r.t. their co-coverage of the case cohort. We compare our algorithm with the state of the art on three GWAS and show that (1) we substantially improve precision (from 0.003 to 0.652) while maintaining the significance of selected pairs, (2) decrease the number of tests by 25-fold, and (3) decrease the runtime by 4-fold. We also show that promoting SNPs from regulatory/coding regions improves the performance (up to 0.8). Potpourri is available at http:/ciceklab.cs.bilkent.edu.tr/potpourri.

Statistical methods with exhaustive search in the identification of gene–gene interactions for colorectal cancer

Though additive forms of heritability are primarily studied in genetics, nonlinear, non-additive gene-gene interactions, that is, epistasis, could explain a portion of the missing heritability in complex human diseases including cancer. In recent years, powerful computational methods have been introduced to understand multivariable genetic factors of these complex human diseases in extremely high-dimensional genome-wide data. In this study, we investigated the performance of three powerful methods, BOolean Operation-based Screening and Testing (BOOST), FastEpistasis, and Tree-based Epistasis Association Mapping (TEAM) to identify interacting genetic risk factors of colorectal cancer (CRC) for genome-wide association studies (GWAS). After quality-control based data preprocessing, we applied these three algorithms to a CRC GWAS data set, and selected the top-ranked 100 single-nucleotide polymorphism (SNP) pairs identified by each method (251 SNPs in total), among which 74 pairs were common between FastEpistasis and BOOST. The identified SNPs by BOOST, FastEpistasis, and TEAM mapped to 58, 57, and 62 genes, respectively. Some genes highlighted by our study, including MACF1, USP49, SMAD2, SMAD3, TGFBR1, and RHOA, have been detected in previous CRC-related research. We also identified some new genes with potential biological relevance to CRC such as CCDC32. Furthermore, we constructed the network of these top SNP pairs for three methods, and the patterns identified in the networks show that some SNPs including rs2412531, rs349699, and rs17142011 play a crucial role in the classification of disease status in our study.

Case-only analysis of gene–gene interactions in inflammatory bowel disease

Background Gene–gene interactions (G × G) potentially play a role in the etiology of complex human diseases, including inflammatory bowel disease (IBD), and may partially explain their ‘missing heritability’. Methods Using the largest genotype dataset available for IBD (16,636 Crohn’s disease (CD) and 12,888 ulcerative colitis (UC) cases) we analyzed G × G with the powerful case-only (CO) design. We studied 169 single nucleotide polymorphisms (SNPs) for CD (156 for UC), previously shown to be associated with the respective diseases. To ensure the validity of the CO design, we confined our analysis to pairs of unlinked SNPs. We used principal component analysis at the center level to adjust for possible causes of genotypic association other than G × G, such as population stratification and genotyping batch effects. Results from center-wise logistic regression analyses were combined by a random effects meta-analysis. Results A number of nominally significant (p < .05) G × G interactions were observed, but none of these withstood the Bonferroni multiple testing correction. However, one SNP pair, comprising rs26528 in the IL27 gene and rs9297145 in the KPNA7 gene region was characterized by an interaction odds ratio of 1.18 (95% CI: 1.10–1.27) for CD and a p-value of 7.75 × 10−6. Owing to the concurrent role of the IL27 and KPNA7 genes in NF-κB signaling, a master regulator of pro- and anti-inflammatory processes in IBD, the observed interaction also has biological plausibility. Conclusions We were able to exemplify the utility of the CO design for analyzing G × G, but had to recognize that such interactions are probably scarce for IBD.

Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus.

BackgroundGenetic interactions are known to play an important role in the missing heritability problem for type 2 diabetes mellitus (T2DM). Interactions between enhancers and their target genes play important roles in gene regulation and disease pathogenesis. In the present study, we aimed to identify genetic interactions between enhancers and their target genes associated with T2DM.MethodsWe performed genetic interaction analyses of enhancers and protein-coding genes for T2DM in 2,696 T2DM patients and 3,548 controls of European ancestry. A linear regression model was used to identify single nucleotide polymorphism (SNP) pairs that could affect the expression of the protein-coding genes. Differential expression analyses were used to identify differentially expressed susceptibility genes in diabetic and nondiabetic subjects.ResultsWe identified one SNP pair, rs4947941×rs7785013, significantly associated with T2DM (combined P=4.84×10−10). The SNP rs4947941 was annotated as an enhancer, and rs7785013 was located in the epidermal growth factor receptor (EGFR) gene. This SNP pair was significantly associated with EGFR expression in the pancreas (P=0.033), and the minor allele “A” of rs7785013 decreased EGFR gene expression and the risk of T2DM with an increase in the dosage of “T” of rs4947941. EGFR expression was significantly upregulated in T2DM patients, which was consistent with the effect of rs4947941×rs7785013 on T2DM and EGFR expression. A functional validation study using the Mouse Genome Informatics (MGI) database showed that EGFR was associated with diabetes-relevant phenotypes.ConclusionGenetic interaction analyses of enhancers and protein-coding genes suggested that EGFR may be a novel susceptibility gene for T2DM.

Mitochondria, sex and variation in routine metabolic rate.

Variation in the metabolic costs associated with organismal maintenance may play a key role in determining fitness, and thus these differences among individuals are likely to be subject to natural selection. Although the evolvability of maintenance metabolism depends on its underlying genetic architecture, relatively little is known about the nature of genetic variation that underlies this trait. To address this, we measured variation in routine metabolic rate (ṀO2routine ), an index of maintenance metabolism, within and among three populations of Atlantic killifish, Fundulus heteroclitus, including a population from a region of genetic admixture between two subspecies. Polygenic association tests among individuals from the admixed population identified 54 single nucleotide polymorphisms (SNPs) that were associated with ṀO2routine , and these SNPs accounted for 43% of interindividual variation in this trait. However, genetic associations with ṀO2routine involved different SNPs if females and males were analysed separately, and there was a sex-dependent effect of mitochondrial genotype on variation in routine metabolism. These results imply that there are sex-specific genetic mechanisms, and potential mitonuclear interactions, that underlie variation in ṀO2routine . Additionally, there was evidence for epistatic interactions between 17% of the possible pairs of trait-associated SNPs, suggesting that epistatic effects on ṀO2routine are common. These data demonstrate not only that phenotypic variation in this ecologically important trait has a polygenic basis with considerable epistasis among loci, but also that these underlying genetic mechanisms, and particularly the role of mitochondrial genotype, may be sex-specific.

Genetic interactions between ANLN and KDR are prognostic for breast cancer survival.

Single nucleotide polymorphisms (SNPs) are the most common genetic variation in mammalian cells with prognostic potential. Anillin-actin binding protein (ANLN) has been identified as being involved in PI3K/PTEN signaling, which is critical in cell life/death control, and kinase insert domain receptor (KDR) encodes a key receptor mediating the cancer angiogenesis/metastasis switch. Knowledge of the intrinsic connections between PI3K/PTEN and KDR signaling, which represent two critical transitions in carcinogenesis, led the present study to investigate the effects of the potential synergy between ANLN and KDR on breast cancer outcome and identify relevant SNPs driving such a synergy at the genetic level. The survival associations of SNPs from KDR and ANLN were assessed through pairwise interaction survival analysis, quantitative trait loci analysis, pathway enrichment analysis and network construction, and the interactions between ANLN and KDR were validated in vitro. It was found that both rare homozygotes in the ANLN:rs12535394 and KDR:rs11133360 SNP pair are prognostic of favorable breast cancer survival and underpin the prominent roles of the immune response in cancer state control. This study contributes to breast cancer prognosis and therapeutic design by providing genetic evidence of interactions between ANLN and KDR, and suggesting the prominent role of the immune response in driving the synergies between the cancer cell life/death and angiogenesis/metastasis transitions during carcinogenesis.