SNP Combination Research Articles

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-precision data (SLEM) to explore the SNP combination effects on schizophrenia through intermediate molecular and cellular functions. The SLEM technique utilizes two levels of precision data for learning. It constructs initial backbone networks with more precise but small amount of multilevel assay data. Then, it learns strengths of intermediate interactions with the less precise but massive amount of GWAS data. The learned networks facilitate identifying effective SNP interactions from the intractably large space of all possible SNP combinations. We have shown that the extracted SNP combinations show higher accuracy than any single SNPs and preserve the accuracy in an independent dataset. The learned networks also provide interpretations of molecular and cellular interactions of SNP combinations toward schizophrenia etiology.

HSICCR: A Lightweight Scoring Criterion Based on Measuring the Degree of Causality for the Detection of SNP Interactions

Recently, research on detecting SNP interactions has attracted considerable attention, which is of great significance for exploring complex diseases. The formulation of effective swarm intelligence optimization algorithms is a primary resolution to this issue. To achieve this goal, an important problem needs to be solved in advance; that is, designing and selecting lightweight scoring criteria that can be calculated in O(m) time and can accurately estimate the degree of association between SNP combinations and disease status. In this study, we propose a high-accuracy scoring criterion (HSICCR) by measuring the degree of causality dedicated to assessing the degree. First, we approximate two kinds of dependencies according to the structural equation of the causal relationship between epistasis SNP combination and disease status. Then, inspired by these dependencies, we put forward this scoring criterion that integrates a widely used method of measuring statistical dependencies based on kernel functions (HSIC). However, the computing time complexity of HSIC is O(m2), which is too costly to be an integral part of the scoring criterion. Since the sizes of the sample space of the disease status, SNP loci and SNP combination are small enough, we propose an efficient method of computing HSIC for variables with a small sample in O(m) time. Eventually, HSICCR can be computed in O(m) time in practice. Finally, we compared HSICCR with five representative high-accuracy scoring criteria that detect SNP interactions for 49 simulation disease models. The experimental results show that the accuracy of our proposed scoring criterion is, overall, state-of-the-art.

Existence of viable Mycobacterium leprae in natural environment and its genetic profiling in a leprosy endemic region

IntroductionMolecular epidemiology of leprosy is very important to study leprosy transmission dynamics and to enhance our understanding of leprosy in endemic areas by utilizing the molecular typing method. Nowadays our understanding of leprosy transmission dynamics has been refined by SNP typing and VNTR marker analysis of M. leprae strains.ObjectiveThis study was carried out to find out the presence of viable M. leprae in the soil and water samples from residing areas of leprosy patients staying in different blocks of Purulia district of West Bengal, understanding their genotypes and compared with that of M. leprae present in patients.Material and methodsSlit-skin smear (SSS) samples (n=112) were collected from the active multibacillary leprosy patients from different blocks of leprosy endemic area. Soil samples (n=1060) and water samples (n=620) were collected from residing areas of leprosy patients. SNP subtyping was performed by PCR followed by sequencing. Multiplex PCR was performed using fifteen ML-VNTR loci and results were analysed.ResultsWe observed high PCR positivity in soil samples (344 out of 1060; 32%) and water samples (140 out of 620; 23%). These PCR positive samples when further screened for viability, it was observed that 150 soil samples (44%) and 56 water samples (40%) showed presence of 16S rRNA. SNP typing of M. leprae revealed presence of predominantly type 1. SNP subtype 1D (83%) was most prevalent in all the blocks of Purulia followed by subtype 1C (15%) and subtype 1A (2%). SNP subtype 2F was noted in only one sample. SNP and VNTR combination showed presence of similar strain type in certain pockets of Purulia region which was responsible for transmission.ConclusionPresence of viable M. leprae in the environment, and presence of SNP Type 1 M. leprae in patients and environment suggests both environment and patients play a role in disease transmission.

A Polygenic DNA Damage Repair Pharmacogenomics (DDR_PGx8) Score Predicts GO Response in AML

Gemtuzumab Ozogamicin (GO) is a CD33-directed antibody conjugated to calicheamicin used in AML immunotherapy. Children's Oncology Group led pediatric AML phase II trial COG-AAML03P1 (NCT00070174) established that GO could be safely added to the standard chemotherapy regimen consisting of ara-C, daunorubicin and etoposide (ADE+GO) and improved outcome(Cooper et al., 2012). Subsequent COG-AAML0531 phase III trial (NCT00372593) randomized patients to receive either standard chemotherapy alone (ADE) or with addition of two doses of GO (ADE+GO) and showed that addition of GO improved outcome in newly diagnosed AML patients (Gamis et al., 2014). Our group previously established that CD33 genetic variation impacts GO response in AML patients (Lamba et al., 2017). Given that the antileukemic effect of GO is primarily driven by calicheamicin induced DNA damage, in this study we investigated pharmacogenomic impact of SNPs in DNA damage response (DDR) pathway genes on GO treatment response. We genotyped 132 SNPs in 42 genes involved in DNA damage repair pathway in DNA samples from 470 patients treated with standard chemotherapy in AAML0531 trial (ADE arm) and 755 patients treated with addition of GO to standard therapy in AAML03P1 and AAAML0531 trials (ADE+GO arm). Univariate analysis to test for association between OS, EFS, DFS and RR after induction 1 in both ADE+GO and ADE arms identified 20 SNPs in 16 genes that were significantly associated with at least one of the clinical endpoints tested in the only ADE+GO arm but not in ADE arm of the trials. We tested these 20 SNPs in all possible combinations with a maximum of 3 SNP/model using multivariable Cox proportional hazard models for association with EFS and OS in patients treated with ADE+GO. Drastically increased number of models arising from higher SNP combination numbers was a computational challenge thus we restricted our analysis to a maximum of 3 SNP combinations. We performed 1000 permutation tests per model to determine the likelihood of obtaining them falsely. Models were ordered according to their Bayesian Information Criterion (BIC) and weight in favor of each model. Those withleast BIC and a 1000 permutation p≤0.05 were selected for development of DDR pharmacogenomics score. DDR_PGx8 score was defined by adding the genotype scores of 8 SNPs in 7 genes (AKT1, ATR, DDB2, PARP1, PI3KCA, PTEN and RAD51) accounting for mode of inheritance (additive, dominant or recessive) and direction of their association with outcome (positive for beneficial and negative for detrimental association) Fig 1A shows overall study design. The DDR_PGx8 score ranged from -5 to 3 in patients treated with ADE+GO (n=755) or ADE alone (N=470). Based on the distribution, the scores were stratified into high (score ≥0, n=212 in ADE group and n=357 in ADE+GO group) and low score (score <0, n=241 in ADE group and n=329 in ADE+GO group) groups. The distribution of DDR_PGx8 score groups did not differ by risk groups or MRD1 status. However, it differed significantly within race with 81.81% (108/132) of black or African American patients compared to 43.75% (364/832) of white patients in the low DDR_PGx8 score group. Patients with low-DDR-PGx8 score had significantly worse EFS (HR=1.52, 95%CI (1.22-1.90), p<0.001; Fig 1B), OS (HR=1.62, 95%CI(1.24-2.11), p<0.001), DFS (HR=1.88, 95%CI(1.42-2.50), p<0.00001;), and higher RR1 (HR=1.89, 95%CI(1.40-2.40), p<0.00001) compared to high-DDR-PGx8 score patients when treated with GO (ADE+GO cohort). This impact of DDR_PGx8 was not observed in patients treated with standard chemotherapy alone (ADE arm), with no difference between low and high DDR_PGx8 score groups in EFS (Fig 1B), OS, DFS and RR (all p>0.28). In multivariable Cox proportional hazard models for DDR-PGx8 score groups, initial risk group assignment, WBC at diagnosis and age, low-DDR-PGx8 score remained a significant and independent predictor of inferior EFS (HR=1.6, 95%CI=1.21-2.02, p<0.001; Fig 1C) and OS (HR=1.6, 95%CI=1.17-2.22, p=0.003) in ADE+GO arm but not in ADE arm. We establish a DNA damage repair response-based pharmacogenomics score predicting outcome in patients treated with addition of GO to standard chemotherapy. The score was not predictive of outcome in patients treated with standard chemotherapy alone implying its impact is GO-specific. Our results in conjunction with existing CD33 SNPs provide a rationale for use of pharmacogenomics in personalizing GO treatment. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Pairwise kinship testing with a combination of STR and SNP loci

Individual commercially available kits exhibit limited discrimination power in full-sibling and second-degree kinship analysis, and therefore they are commonly combined with other kits to obtain more loci and a higher efficacy. However, few studies have systematically evaluated the discrimination power of combined loci. In this study, we combined the ForenSeq™ DNA Signature kit (containing 27 short tandem repeats [STRs] + 91 single nucleotide polymorphisms [SNPs]) with the AGCU NC 21 + 1 PCR amplification kit (containing 21 STRs) to obtain a non-overlapping set of 40 STR and 91 SNP markers. The discrimination power was evaluated for 74 full-sibling pairs, 114 uncle/aunt-nephew/niece pairs and 93 grandparent-grandson/granddaughter pairs. The results show that the efficacy of the 40 STR + 91 SNP combination is higher than the efficacy of either 27 STRs + 91 SNPs or 40 STRs alone. Both the sensitivity and specificity of the 40 STR + 91 SNP marker set achieved 100 % in full-sibling testing, with strong power to distinguish second-degree relatives from unrelated pairs. The 40 STR + 91 SNP set could also distinguish most full-sibling relatives from second-degree relatives but was insufficient to distinguish relatives who belong to the same autosomal kinship class. Our results suggest that ignoring linkage can lead to incorrect likelihood ratios for both related and unrelated pairs, while mutation had a relatively lower effect on the likelihood ratios. Moreover, linkage and mutation had a higher impact on full-sibling testing than on second-degree kinship testing. The discrimination power of the 40 STR and 91 SNP marker set could be strengthened by adding an additional relative.

SHEIB-AGM: A Novel Stochastic Approach for Detecting High-Order Epistatic Interactions Using Bioinformation With Automatic Gene Matrix in Genome-Wide Association Studies

Detecting epistatic interactions in GWAS (genome-wide association studies) data is of great significance in studying common and complex diseases; however, the ability to detect high-order epistatic interactions in GWAS data is still insufficient. Existing methods are usually used to identify two-order interactions, and they cannot detect a large number of interactions. In this article, we propose a novel stochastic approach named SHEIB-AGM (stochastic approach for detecting high-order epistatic interactions using bioinformation with automatic gene matrix). SHEIB-AGM utilizes bioinformation to construct a gene matrix. In each iteration, it randomly generate a high-order SNP combination based on the gene matrix. SHEIB-AGM utilizes k2 (the Bayesian network scoring criterion) and G-test to detect epistasis in the generated combination and automatically update the gene matrix. We have compared SHEIB-AGM with six other methods, i.e., DECMDR, SNPHarvester, MACOED, AntEpiSeeker, HS-MMGKG and SEE, on simulated data including 108 epistatic models and 17,600 files. The results demonstrate that SHEIB-AGM greatly outperforms the above methods in terms of F-measure and power. We utilized SHEIB-AGM (with and without bioinformation) to analyze a real GWAS dataset from the Wellcome Trust Case Control Consortium. The results indicate that SHEIB-AGM with bioinformation can detect 33.94~3069.40-times more epistatic interactions. We have found numerous genes and gene pairs that may play an important role in seven complex diseases. Some of them have been found in the CTD database (the Comparative Toxicogenomics Database).

Single Nucleotide Polymorphisms in Starch Biosynthetic Genes Associated With Increased Resistant Starch Concentration in Rice Mutant.

Resistant Starch (RS), plays a crucial role in human health and nutrition by controlling glucose metabolism. RS or dietary fibre content in rice is low because it goes through a variety of process before it is ready for cooking and consumption. Hence, this study was carried out to develop a rice mutant with increased RS. The rice mutant (γ278) with increased RS was developed by utilizing gamma (γ) rays as a mutagen. Mutant γ278 was characterized for mutations in the starch biosynthetic genes viz., GBSSI, SSI, SSIIa, SSIIIa, SBEIa, and SBEIIb to reveal the functional mutations/variations led to high RS content in rice. A total of 31 sequence variants/mutations in six genes were identified. We report the discovery of three deleterious mutation/variants each in GBSSI, SSIIa, and SSIIIa with the potential to increase RS content in rice. Further, wild × mutant crosses were made to develop an F2 population to study the effect of combination of deleterious mutations. The SNP (GBSSI:ssIIa:ssIIIa) combination responsible for high RS content in F2 population was identified and recorded highest amylose content (AC) (26.18%) and RS (8.68%) content. In conclusion, this marker combination will be highly useful to develop a rice variety with increased RS.

Significant role of gene–gene interactions of clock genes in mood disorder

BackgroundThe genetic interactions in the circadian rhythm biological system are promising as a source of pathophysiology in mood disorder. We examined the role of the gene–gene interactions of clock genes in mood disorder. MethodsWe included 413 patients with mood disorder and 1294 controls. The clock genes investigated were BHLHB2, CLOCK, CSNK1E, NR1D1, PER2, PER3, and TIMELESS. Allele, genotype, and haplotype associations were tested. Gene–-gene interactions were analyzed using the non-parametric model-free multifactor-dimensionality reduction (MDR) method. ResultsTIMELESS rs4630333 and CSNK1E rs135745 were significantly associated with both major depressive disorder and bipolar disorder. The CLOCK haplotype was also strongly associated. The genetic roles of these SNPs were consistent from the allele and genotypic associations to the MDR interaction results. In MDR analysis, the combination of TIMELESS rs4630333 and CSNK1E rs135745 exhibited the most significant association with mood disorders in the two-locus model. BHLHB2 rs2137947 for major depressive disorder and CLOCK rs12649507 for bipolar disorder were the most significant third loci in the three-locus combination model. The four-locus SNP combination model showed the best balanced accuracy (BA), but its cross-validation consistency (CVC) was unsatisfactory. LimitationsWe included only 17 SNPs for seven circadian genes due to our limited resources; all subjects were ethnically Korean. ConclusionsOur results suggest significant single-gene associations and gene–gene interactions of circadian genes with mood disorder. Gene–gene interactions play a crucial role in mood disorder, even when individual clock genes do not have significant roles.

1447-P: Epistatic Interactions of Known Gene Variants for Glycated Hemoglobin: Evidence from the Long Life Family Study

HbA1c, an index of chronic glycemic status relevant to IR and T2D, is associated with aging, cognitive performance and mortality. To identify epistatic interactions, we assessed 65 published SNPs associated with levels of HbA1c (n=4,090) and change in HbA1c levels over time (n=2,054) among nondiabetic participants of European ancestry who had complete data from 2 exams collected 7 years apart in the LLFS, a multicenter family study with a clustering of healthy aging and exceptional survival. Both traits were adjusted for age, sex, BMI, field center and principal components, with correction for familial membership. HbA1c change over time was derived using growth curve modeling, taking into account baseline HbA1c. A knowingly more powerful Bayesian approach was used to test SNP association searching for significant (Bayes Factor, BF&amp;gt;3) single SNPs and best (top 1) combinations of interacting SNPs followed by MIXED regression reconfirmation (p&amp;lt;0.05 for main and interaction terms). HK1-rs17476364 (BF=7.8), GCK-rs4607517 (BF=5.7), HK1-rs16926246 (BF=4.8), SPTA1-rs12041363 (BF=4.5), G6PC2-rs560887 (BF=3.6) and SPTA1-rs857691 (BF=3.0) were individually associated with baseline HbA1c. Their best and significant 2-SNP association was found between HK1-rs17476364 and GCK (BF=13.5; p=0.000015, 0.0038 and 0.0093 for main and interaction terms, respectively). The emerged best 3 and 4 SNP combinations were HK1-GCK-SPTA1 (BF=18.1) and HK1-GCK-SPTA1-G6PC2 (BF=22.0), respectively. For HbA1c change over time, no single SNPs were significant; their best SNP combination involved G6PC2, ADCY5, SLC30A8, HK1, PHB2 and FN3KRP. Glucokinase (GCK product) and hexokinase (HK1 product) are isozymes that interactively and uniquely phosphorylate glucose to make G6P, an important intermediate enabling the coordination of the rate of glycolysis. Together, HK1-GCK interaction for HbA1c was observed first in a large family data with familial clustering of exceptional longevity. Disclosure P. An: None. B. Thyagarajan: None. J.H. Lee: None. N. Schupf: None. J.M. Zmuda: None. M. Province: None. Funding National Institutes of Health

Genetic variation in the ABCB1 gene associated with post treatment lyme disease syndrome status

Post Treatment Lyme Disease Syndrome (PTLDS) poses a difficult to understand health issue. This multi-centered, randomized control trial studied the possible correlation between ABCB1 (MDR1) gene variants and the incidence of PTLDS in affected patients. Genomic DNA was isolated and analyzed for four ABCB1 gene SNPs (rs1128503, rs1045642, rs2235067, and rs4148740). Significant findings include the association of rs1128503 TC variant with PTLDS status. Additionally, the rs1128503+ rs1045642+ rs2235067 SNP combination increased rs1128503 genotype TC significance to 3.83 times the rs1128503 genotype CC. The TT variant of rs4148740 in conjunction with rs1128503 reduced the odds ratio and appeared to convey a PTLDS protective status to the rs1128503 TC variant.

Nitric oxide alters nitrogen metabolism and PIN gene expressions by playing protective role in arsenic challenged Brassica juncea L.

Plants have ability to adapt themselves through altering their growth process. In the present study, we examined exogenous application of nitric oxide (NO) on nitrogen metabolism and auxin (PIN) gene expression, and its possible role in alleviation of arsenic (As) toxicity in Brassica juncea seedlings. Seven days old hydroponically grown B. juncea seedlings were exposed to AsIII (150 μM), Sodium nitroprusside (NO donor, 100 μM), AsIII + SNP and control (without metal)for 48 h. Experimental results revealed that AsIII stress: enhanced the level of nitrite, NiR activity, NO3− and NH4+content as well as NADH-GOGAT activity; but GDH level decreased; enhanced content of amino acids; upregulated gene expression level of N metabolism and downregulated polar auxin transporter genes (PIN); inhibited plant growth and morphological parameters; increased MDA, H2O2, cysteine, proline content, enzymatic antioxidants (SOD, CAT, APX; GSH, TT, NPT); and decreased nutrient content. AsIII + SNP combination reduced the accumulation of As; improved growth; chlorophyll, protein and mineral nutrient content by scavenging ROS generation; maintained amino acids content; downregulated expression of N metabolism genes and upregulated expression of auxin transporter (PIN) genes . Additional biochemical data depicts reduction in the level of nitrogen related enzymatic activities, and other stress related parameters. Overall, this study provides an integrated view that exogenous SNP (NO donor) supplementation alleviated the inhibitory role of AsIII in B. juncea seedlings by altering nutrients, amino acids and auxin redistribution via expression of nitrogen and PIN gene profiling.

Superior single nucleotide polymorphisms that contribute to two main routes of the fatty acid synthesis pathway in Korean cattle.

The fatty acid composition of adipose tissue in livestock has been recognized as an important carcass trait that affects meat quality. To determine the overall positive effect on fatty acid synthesis, we investigated PPARγ, SREBPs, FABP4, SCD, and FASN genes because SNP and/or SNP combinations were strongly affecting each fatty acid synthesis pathway. We screened their SNPs, SNP combinations, and genotype. Furthermore, we studied the economic traits C18:1, monounsaturated fatty acid (MUFA), and marbling score (MS). To enhance the accuracy of the predictive genetic effect, we applied a statistical adjustment model excluding environmental factors. We also selected superior genes, gene combinations, and genotypes for each economic trait by using multifactor dimensionality reduction. Based on the results, g.3977-325 T>C (CC) was selected as the best genotype of the SNP and the combination (g.10153 A>G, g.3977-325 T>C) (AACC, AGCC, GGCC), (g.3690 G>A, g.3977-325 T>C) (GGCC, GACC), (g.3977-325 T>C, g.25670 C>T) (CCCC, CCCT, CCTT) (genotypes of the best SNP combination) was selected as the best gene combination for C18:1 and MUFA. In addition, g.3977-325 T>C (CC) was selected as the best genotype of the SNP and the combination (g.3977-325 T>C, g.6974 G>A) (TTAA, CTAA, CCAA, CCGA, CCGG) as the best SNP combination for MS. We integrated genes that were selected as excellent SNP and SNP combinations affecting two main routes of the fatty acid synthesis pathway and identified the best and final SNP, SNP combination, and superior genotype.

The gene encoding the inwardly rectifying potassium channel Kir4.1 may be involved in sudden infant death syndrome.

Disturbances in brain function and development may play a role in sudden infant death syndrome (SIDS). This Norwegian study aimed to test the hypothesis that specific variants of genes involved in water transport and potassium homeostasis would be predisposing factors for SIDS. Genetic variation in the genes encoding aquaporin-4 (AQP4), Kir4.1 (KCNJ10) and α-syntrophin was analysed in 171 SIDS cases (62.6% male) with a median age of 15.5 (2-52) weeks and 398 adult controls (70.6% male) with a median age of 44 (11-91) years. All the subjects were Caucasians who were autopsied from 1988 to 2013. The CC genotype of rs72878794 in the AQP4 gene and a combination of the CC genotype in rs17375748, rs1130183, rs12133079 and rs1186688 in KCNJ10 (4xCC) were found to be associated with SIDS. The SIDS cases with the 4xCC SNP combination were younger than the SIDS cases with other genotype combinations (p = 0.006). This study indicates that genetic variations in KCNJ10 and AQP4 may be predisposing factors for SIDS. Alterations in the expression of the AQP4/Kir4.1 complex can disrupt water and ion homeostasis, which may influence brain development and facilitate brain oedema formation This may be especially unfavourable during the first weeks of life.

A survey about methods dedicated to epistasis detection.

During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).

Global distribution of polymorphisms associated with delayed Plasmodium falciparum parasite clearance following artemisinin treatment: Genotyping of archive blood samples

The recent emergence and spread of artemisinin-resistant Plasmodium falciparum isolates is a growing concern for global malaria-control efforts. A recent genome-wide analysis study identified two SNPs at genomic positions MAL10-688956 and MAL13-1718319, which are linked to delayed clearance of parasites following artemisinin combination therapy (ACT). It is expected that continuous artemisinin pressure will affect the distribution of these SNPs. Here, we investigate the worldwide distribution of these SNPs using a large number of archived samples in order to generate baseline data from the period before the emergence of ACT resistance. The presence of SNPs in MAL10-688956 and MAL13-1718319 was assessed by nested PCR RFLP and direct DNA sequencing using 653 global P. falciparum samples obtained before the reported emergence of ACT resistance. SNPs at MAL10-688956 and MAL13-1718319 associated with delayed parasite clearance following ACT administration were observed in 8% and 3% of parasites, respectively, mostly in Cambodia and Thailand. Parasites harbouring both SNPs were found in only eight (1%) isolates, all of which were from Cambodia and Thailand. Linkage disequilibrium was detected between MAL10-688956 and MAL13-1718319, suggesting that this SNP combination may have been selected by ACT drug pressure. Neither of the SNPs associated with delayed parasite clearance were observed in samples from Africa or South America. Baseline information of the geographical difference of MAL10-688956 and MAL13-1718319 SNPs provides a solid basis for assessing whether these SNPs are selected by artemisinin-based combination therapies.

Detection of exonic variants within the melanocortin 1 receptor (MC1R) gene in Black Silky, White Leghorn and Golden duckwing Araucana chicken.

The melanocortin 1 receptor (MC1R) gene can be considered a candidate functional gene for the pigmentation of plumage color. The aim of this study was to investigate the association between the genotype frequencies of g.69 T>C, g.376 G>A and g.427 A>G SNPs within the MC1R gene in Black silky (O), Golden duckwing Araucana (GA) and White Leghorn (W). The CC and AA genotype frequencies of g.69 T>C and g.427 A>G SNPs in White Leghorn (W) were both 1.000, and the TT genotype frequency of the g.69 T>C SNP in Golden duckwing Araucana (GA) was also 1.000. The GG and AA genotype frequencies of g.376 G>A and g.427 A>G SNPs in Black silky (O) were both 0.100. When a haplotype is observed using a combination of markers, a Golden duckwing Araucana (GA) can especially be distinguished when it is a TAG, TGG and TAA type in the SNP combination of the MC1R gene. In case of the CAA types, only White Leghorn (W) could specifically be distinguished. Therefore, three SNPs in MC1R may provide identification in chicken breeds.

Combined analysis of three lynch syndrome cohorts confirms the modifying effects of 8q23.3 and 11q23.1 in MLH1 mutation carriers

Two colorectal cancer (CRC) susceptibility loci have been found to be significantly associated with an increased risk of CRC in Dutch Lynch syndrome (LS) patients. Recently, in a combined study of Australian and Polish LS patients, only MLH1 mutation carriers were found to be at increased risk of disease. A combined analysis of the three data-sets was performed to better define this association. This cohort-study includes three sample populations combined totaling 1,352 individuals from 424 families with a molecular diagnosis of LS. Seven SNPs, from six different CRC susceptibility loci, were genotyped by both research groups and the data analyzed collectively. We identified associations at two of the six CRC susceptibility loci in MLH1 mutation carriers from the combined LS cohort: 11q23.1 (rs3802842, HR = 2.68, p ≤ 0.0001) increasing risk of CRC, and rs3802842 in a pair-wise combination with 8q23.3 (rs16892766) affecting age of diagnosis of CRC (log-rank test; p ≤ 0.0001). A significant difference in the age of diagnosis of CRC of 28 years was observed in individuals carrying three risk alleles compared to those with 0 risk alleles for the pair-wise SNP combination. A trend (due to significance threshold of p ≤ 0.0010) was observed in MLH1 mutation carriers towards an increased risk of CRC for the pair-wise combination (p = 0.002). This study confirms the role of modifier loci in LS. We consider that LS patients with MLH1 mutations would greatly benefit from additional genotyping of SNPs rs3802842 and rs16892766 for personalized risk assessment and a tailored surveillance program.

GCH1-Polymorphism and Pain Sensitivity among Women with Provoked Vestibulodynia

BackgroundProvoked vestibulodynia (PVD) is a pain disorder localized in the vestibular mucosa. It is the most common cause of dyspareunia among young women and it is associated with general pain hypersensitivity and other chronic pain conditions. Polymorphism in the guanosine triphosphate cyclohydrolase (GCH1) gene has been found to influence general pain sensitivity and the risk of developing a longstanding pain condition. The aim of this study was to investigate GCH1-polymorphism in women with PVD and healthy controls, in correlation to pain sensitivity.ResultsWe found no correlation between the previously defined pain-protective GCH1-SNP combination and the diagnosis of PVD. Nor any correlation with pain sensitivity measured as pressure pain thresholds on the arm, leg and in the vestibule, coital pain scored on a visual analog scale and prevalence of other bodily pain conditions among women with PVD (n = 98) and healthy controls (n = 102). However, among patients with current treatment (n = 36), there was a significant interaction effect of GCH1-gene polymorphism and hormonal contraceptive (HC) therapy on coital pain (p = 0.04) as well as on pressure pain thresholds on the arm (p = 0.04). PVD patients carrying the specified SNP combination and using HCs had higher pain sensitivity compared to non-carriers. In non-HC-users, carriers had lower pain sensitivity.ConclusionsThe results of this study gave no support to the hypothesis that polymorphism in the GCH1-gene contributes to the etiology of PVD. However, among patients currently receiving treatment an interaction effect of the defined SNP combination and use of hormonal contraceptives on pain sensitivity was found. This finding offers a possible explanation to the clinically known fact that some PVD patients improve after cessation of hormonal contraceptives, indicating that PVD patients carrying the defined SNP combination of GCH1 would benefit from this intervention.

Next-generation DNA sequencing-based assay for measuring allelic expression imbalance (AEI) of candidate neuropsychiatric disorder genes in human brain

BackgroundCommon genetic variants that regulate gene expression are widely suspected to contribute to the etiology and phenotypic variability of complex diseases. Although high-throughput, microarray-based assays have been developed to measure differences in mRNA expression among independent samples, these assays often lack the sensitivity to detect rare mRNAs and the reproducibility to quantify small changes in mRNA expression. By contrast, PCR-based allelic expression imbalance (AEI) assays, which use a "marker" single nucleotide polymorphism (mSNP) in the mRNA to distinguish expression from pairs of genetic alleles in individual samples, have high sensitivity and accuracy, allowing differences in mRNA expression greater than 1.2-fold to be quantified with high reproducibility. In this paper, we describe the use of an efficient PCR/next-generation DNA sequencing-based assay to analyze allele-specific differences in mRNA expression for candidate neuropsychiatric disorder genes in human brain.ResultsUsing our assay, we successfully analyzed AEI for 70 candidate neuropsychiatric disorder genes in 52 independent human brain samples. Among these genes, 62/70 (89%) showed AEI ratios greater than 1 ± 0.2 in at least one sample and 8/70 (11%) showed no AEI. Arranging log2AEI ratios in increasing order from negative-to-positive values revealed highly reproducible distributions of log2AEI ratios that are distinct for each gene/marker SNP combination. Mathematical modeling suggests that these log2AEI distributions can provide important clues concerning the number, location and contributions of cis-acting regulatory variants to mRNA expression.ConclusionsWe have developed a highly sensitive and reproducible method for quantifying AEI of mRNA expressed in human brain. Importantly, this assay allowed quantification of differential mRNA expression for many candidate disease genes entirely missed in previously published microarray-based studies of mRNA expression in human brain. Given the ability of next-generation sequencing technology to generate large numbers of independent sequencing reads, our method should be suitable for analyzing from 100- to 200-candidate genes in 100 samples in a single experiment. We believe that this is the appropriate scale for investigating variation in mRNA expression for defined sets candidate disorder genes, allowing, for example, comprehensive coverage of genes that function within biological pathways implicated in specific disorders. The combination of AEI measurements and mathematical modeling described in this study can assist in identifying SNPs that correlate with mRNA expression. Alleles of these SNPs (individually or as sets) that accurately predict high- or low-mRNA expression should be useful as markers in genetic association studies aimed at linking candidate genes to specific neuropsychiatric disorders.

Replication and extension of association of choline acetyltransferase with nicotine dependence in European and African American smokers

Choline acetyltransferase is critical in the synthesis of acetylcholine and regulation of cholinergic neuron functions. We recently reported association of the encoding gene ChAT with both smoking cessation and nicotine dependence (ND) in two independent European American (EA) samples; however, in the replication sample, only limited SNPs partially covering the gene were examined. In this study, we examined the association of 14 SNPs, which cover the entire gene, with ND, assessed by smoking quantity (SQ), heaviness of smoking index (HSI), and Fagerström Test for ND (FTND), in 2,037 subjects from 602 families of African American (AA) or EA origin. Individual SNP-based association analysis revealed that five SNPs showed nominal association with at least one ND measure in one of the samples (P = 0.022-0.042); none remained significant after correction for multiple testing. Haplotype-based association analysis revealed that haplotypes G-G-A-C, formed by rs1880676-rs3810950-rs10082479-rs8178990 (P = 0.005-0.0178), and G-G-T-C-G-C, formed by rs1880676-rs3810950-rs10082479-rs8178990-rs3793790-rs12266458 (P = 0.00247-0.00468), displayed significant association with all three ND measures in the AA sample, as did haplotype T-C-G-A-T, formed by rs12266458-rs11101191-rs8178991-rs4838544-rs4838547 (P = 0.00741-0.0103), in the EA sample. All these detected haplotype-based associations remained significant after correction for all major haplotypes for a given SNP combination. Together, our findings, in conjunction with the previous report of the association, warrant further investigation of ChAT in ND.