Haplotype analysis on correlation between transcription factor 7-like 2 gene polymorphism and breast cancer risk

Background Up to now, limited researches focused on the association between transcription factor 7-like 2 gene (TF7L2) gene single nucleotide polymorphisms (SNPs) and breast cancer (BC) risk. The aim of this study was to evaluate the associations between TF7L2 and BC risk in Chinese Han population. Methods Logistic regression model was used to test the correlation between polymorphisms and BC risk. Strength of association was evaluated by odds ratio (OR) and 95% confidence interval (CI). Generalized multifactor dimensionality reduction (GMDR) was applied to analyze the SNP-SNP and gene-environment interaction. Results Logistic regression analysis indicated that the BC risk was obviously higher in carriers of rs1225404 polymorphism C allele than that in TT genotype carriers (TC or CC versus TT), adjusted OR (95%CI) =1.40 (1.09–1.72). Additionally, we also discovered that people with rs7903146- T allele had an obviously higher risk of BC than people with CC allele (CT or TT versus CC), adjusted OR (95%CI) =1.44 (1.09–1.82). GMDR model was used to research the effect of interaction among 4 SNPs and environmental factors on BC risk. We discovered an important two-locus model (p = 0.0100) including rs1225404 and abdominal obesity, suggesting a potential gene–environment correlation between rs1225404 and abdominal obesity. In general, the cross-validation consistency of two-locus model was 10 of 10, and the testing accuracy was 0.632. Compared with subjects with normal waist circumference (WC) value and rs1225404 TT genotype, abdominal obese subjects with rs1225404 TC or CC genotype had the highest BC risk. After covariate adjustment, OR (95%CI) was 2.23 (1.62–2.89). Haplotype analysis indicated that haplotype containing rs1225404-T and rs7903146-C alleles were associated with higher BC risk. Conclusions C allele of rs1225404 and T allele of rs7903146, interaction between rs1225404 and abdominal obesity, rs1225404-T and rs7903146-C haplotype were all related to increased BC risk.

Association analysis of maternal MTHFR gene polymorphisms and the occurrence of congenital heart disease in offspring

Background Although many studies showed that the risk of congenital heart disease (CHD) was closely related to genetic factors, the exact pathogenesis is still unknown. Our study aimed to comprehensively assess the association of single nucleotide polymorphisms (SNPs) of maternal MTHFR gene with risk of CHD and its three subtypes in offspring. Methods A case–control study involving 569 mothers of CHD cases and 652 health controls was conducted. Thirteen SNPs were detected and analyzed. Results Our study showed that genetic polymorphisms of maternal MTHFR gene at rs4846052 and rs1801131 were significantly associated with risk of CHD in the homozygote comparisons (TT vs. CC at rs4846052: OR = 7.62 [95%CI 2.95–19.65]; GG vs. TT at rs1801131: OR = 5.18 [95%CI 2.77–9.71]). And six haplotypes of G–C (involving rs4846048 and rs2274976), A–C (involving rs1801133 and rs4846052), G–T (involving rs1801133 and rs4846052), G–T–G (involving rs2066470, rs3737964 and rs535107), A–C–G (involving rs2066470, rs3737964 and rs535107) and G–C–G (involving rs2066470, rs3737964 and rs535107) were identified to be significantly associated with risk of CHD. Additionally, we observed that a two-locus model involving rs2066470 and rs1801131 as well as a three-locus model involving rs227497, rs1801133 and rs1801131 were significantly associated with risk of CHD in the gene–gene interaction analyses. For three subtypes including atrial septal defect, ventricular septal defect and patent ductus arteriosus, similar results were observed. Conclusions Our study indicated genetic polymorphisms of maternal MTHFR gene were significantly associated with risk of fetal CHD in the Chinese population. Additionally, there were significantly interactions among different SNPs on risk of CHD. However, how these SNPs affect the development of fetal heart remains unknown, and more studies in different ethnic populations and with a larger sample are required to confirm these findings.

Influence of Rapid Urbanization on Thyroid Autoimmune Disease in China

Background. The prevalence of autoimmune thyroid diseases (AITDs), especially Hashimoto’s thyroiditis (HT), has increased dramatically in China. Moreover, China is experiencing the largest scale of urbanization in the world. We intended to explore the relationship between rapid urbanization and HT. Methods. A total of 2946 subjects in Zhejiang Shangyu (SY) (n = 1546) and Jiangsu Nanjing (NJ) (n = 1400) were enrolled in this study. Serum TPOAb, TGAb, and thyrotropin (TSH) were measured, and ultrasonography of the thyroid was performed in all subjects. DNA was extracted from all subjects, and four SNPs were selected for genotyping. Generalized multifactor dimensionality reduction (GMDR) was used to screen the best interaction between genetic factors and environment factors. Results. TPOAb and TGAb concentrations were higher in NJ than in SY (34.60 vs. 14.00 IU/ml and 21.05 vs. 7.50 IU/ml). People in NJ also had higher TPOAb and TGAb positivity rates than those in SY (7.8% vs. 12.7% and 8.7% vs. 16.3%). Logistic regression analysis indicated that rapid urbanization was an independent risk factor for TPOAb (OR = 1.473) and TGAb (OR = 1.689). Genotype TT in rs11675434 was associated with an increased risk of TPOAb positivity both in SY (OR = 2.955) and in NJ (OR = 1.819). GMDR analysis showed a two-locus model (SNP2 × urbanization) and a three-locus model (SNP2 × SNP3 × urbanization), which had testing accuracies of 56.88% and 57.25%, respectively ( P values were 0.001 and 0.001). Conclusion. Rapid urbanization influences the incidence of TPOAb and TGAb positivity. We should pay more attention to thyroid autoimmune disease in areas of China experiencing rapid urbanization.

Vitamin D receptor gene polymorphisms and its interactions with environmental factors on renal cell carcinoma risk

Aims We designed a case-control study to investigate the effect of vitamin D receptor gene (VDR) gene single nucleotide polymorphisms (SNPs) and possible gene- environment interaction on the susceptibility of renal cell carcinoma (RCC). Methods Generalized multifactor dimensionality reduction (GMDR) was used to find out the interaction combinations between SNPs and environmental factors, including gene- gene synergy and gene environment synergy effect. Logistic regression was used to analyze the correlation between the four SNPs in VDR gene and RCC, and the significant interaction combinations found by GMDR model were analyzed by hierarchical analysis. Results The genotype distribution of the control group was in accordance with Hardy- Weinberg equilibrium. Logistic regression analysis showed that the risk of RCC in VDR-rs7975232 A allele carriers was significantly higher than that of CC genotype carriers (CA + AA vs. CC), adjusted OR (95 % CI) = 1.75 (1.26–2.28). We used GMDR model to screen the best synergistic model between the four SNPs of VDR gene and smoking and drinking. We found a significant two locus model (P = 0.0010) involving rs7975232 and smoking. The cross- validation consistency of the two- locus model was 10/ 10, and the accuracy was 60.72 %. Compared with non-smokers with rs7975232 -CA or AA genotype, smokers with rs7975232 -CC genotype had the highest risk of RCC, or (95 % CI) = 2.23 (1.42–3.09), after adjustment for covariates. Conclusions We found that the A allele of rs7975232 within VDR gene, interaction between rs7975232 and smoking were all associated with increased RCC risk.

The value of genomic relationship matrices to estimate levels of inbreeding

Background Genomic relationship matrices are used to obtain genomic inbreeding coefficients. However, there are several methodologies to compute these matrices and there is still an unresolved debate on which one provides the best estimate of inbreeding. In this study, we investigated measures of inbreeding obtained from five genomic matrices, including the Nejati-Javaremi allelic relationship matrix (FNEJ), the Li and Horvitz matrix based on excess of homozygosity (FL&H), and the VanRaden (methods 1, FVR1, and 2, FVR2) and Yang (FYAN) genomic relationship matrices. We derived expectations for each inbreeding coefficient, assuming a single locus model, and used these expectations to explain the patterns of the coefficients that were computed from thousands of single nucleotide polymorphism genotypes in a population of Iberian pigs. Results Except for FNEJ, the evaluated measures of inbreeding do not match with the original definitions of inbreeding coefficient of Wright (correlation) or Malécot (probability). When inbreeding coefficients are interpreted as indicators of variability (heterozygosity) that was gained or lost relative to a base population, both FNEJ and FL&H led to sensible results but this was not the case for FVR1, FVR2 and FYAN. When variability has increased relative to the base, FVR1, FVR2 and FYAN can indicate that it decreased. In fact, based on FYAN, variability is not expected to increase. When variability has decreased, FVR1 and FVR2 can indicate that it has increased. Finally, these three coefficients can indicate that more variability than that present in the base population can be lost, which is also unreasonable. The patterns for these coefficients observed in the pig population were very different, following the derived expectations. As a consequence, the rate of inbreeding depression estimated based on these inbreeding coefficients differed not only in magnitude but also in sign. Conclusions Genomic inbreeding coefficients obtained from the diagonal elements of genomic matrices can lead to inconsistent results in terms of gain and loss of genetic variability and inbreeding depression estimates, and thus to misleading interpretations. Although these matrices have proven to be very efficient in increasing the accuracy of genomic predictions, they do not always provide a useful measure of inbreeding.

EFFICIENCY BASED ON MARKER ASSISTED SELECTION FOR F2 POPULATION WITH ONE LOCUS MODEL: A SIMULATION STUDY

Simulation study was done to evaluate QTL mapping and selection efficiency of molecular markers utilisation in the F2 population. The simulation study started with formulating genetic configuration which consists of chromosome maps and genetic models. Genetic model for diploid individuals is a model which consists two alleles for each locus. Genetic model that used is a mathematical model consists additive, dominance, and interactions with different effects at each locus, with maximum interaction occurs between two loci (digenic). QTL mapping was constructed by using single locus model, two loci model and multiple loci model. the effect of sample size, heritability, and marker density was observed. Three model was used to analyse QTL position, i.e. marker regression, interval mapping (IM) and composite interval mapping (CIM). Several parameters were specified in this study: genetic variability coefficient (GVC=15%), population mean (μ=10), epistasis and genetic variance ratio (f=0.1), dominance and additive variance ratio (r=0.25), the ratio of AA:AD:DD is 3:2:1 with additive and dominance gene action, and its interaction. The first and last marker were located at each edge of 150 cM chromosome for each chromosome. The interval distance between markers were equal. Haldane’s map function was used in this simulation. The simulation was performed by using the QTL Package on “R” software. With a heritability 0.2, the required sample size to indicate the interval markers associated with QTL were 50 for single locus model. The level of selection efficiency using molecular markers was higher than the phenotypic screening on. Efficiency level of selection based on molecular markers (Em) is a function of the distance between the markers to QTL (d) which follows “reciprocal quadratic” function. Efficiency level of selection based on phenotype (Ef) is a function of heritability favourable traits which follows “reciprocal quadratic” function.Keywords: efficiency, markers, QTL, simulation

Revisiting a GWAS peak in Arabidopsis thaliana reveals possible confounding by genetic heterogeneity

Genome-wide association studies (GWAS) have become a standard approach for exploring the genetic basis of phenotypic variation. However, correlation is not causation, and only a tiny fraction of all associations have been experimentally confirmed. One practical problem is that a peak of association does not always pinpoint a causal gene, but may instead be tagging multiple causal variants. In this study, we reanalyze a previously reported peak associated with flowering time traits in Swedish in Arabidopsis thaliana. The peak appeared to pinpoint the AOP2/AOP3 cluster of glucosinolate biosynthesis genes, which is known to be responsible for natural variation in herbivore resistance. Here we propose an alternative hypothesis, by demonstrating that the AOP2/AOP3 flowering association can be wholly accounted for by allelic variation in two flanking genes with clear roles in regulating flowering: NDX1, a regulator of the main flowering time controller FLC, and GA1, which plays a central role in gibberellin synthesis and is required for flowering under some conditions. In other words, we propose that the AOP2/AOP3 flowering-time association is yet another example of a spurious, “synthetic” association, arising from trying to fit a single-locus model in the presence of two statistically associated causative loci.

Diagnosis of Diabetic Retinopathy Using Machine Learning

Diabetic retinopathy is an eye condition that can cause vision loss and blindness in people who have diabetics. It affects blood vessels in the retina. Initially, Diabetic retinopathy may not have any symptoms, but finding it early can help us to take steps to protect our vision. Some people notice changes in their vision, like trouble in reading or seeing faraway objects, these changes may come and go. In later stages of diseases, blood vessels in the retina starts to bleed into the vitreous. If this happens, you may see dark, floating spots or streaks that look like lobwels. Sometimes the spots clear up on their own, but it is important to start the treatment, otherwise it may get worse and the bleeding can happen again. There are various stages, it includes blurred vision, impairment of color vision, floaters, patches or streaks. Hence in our project, we came up with an idea of identifying diabetic retinopathy in early stages, to classify a given set of images into four classes, we are using supervised learning methods. For this task, we use deep learning technique with inception v3module along with skin locus model in order to achieve better results and for easy classification of images