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MassARRAY-based single nucleotide polymorphism analysis in breast cancer of north Indian population.

ABSTRACT: BACKGROUND:Breast Cancer (BC) is associated with inherited gene mutations. High throughput genotyping of BC samples has led to the identification and characterization of biomarkers for the diagnosis of BC. The most common genetic variants studied are SNPs (Single Nucleotide Polymorphisms) that determine susceptibility to an array of diseases thus serving as a potential tool for identifying the underlying causes of breast carcinogenesis. METHODS:SNP genotyping employing the Agena MassARRAY offers a robust, sensitive, cost-effective method to assess multiple SNPs and samples simultaneously. In this present study, we analyzed 15 SNPs of 14 genes in 550 samples (150 cases and 400 controls). We identified four SNPs of genes TCF21, SLC19A1, DCC, and ERCC1 showing significant association with BC in the population under study. RESULTS:The SNPs were rs12190287 (TCF21) having OR 1.713 (1.08-2.716 at 95% CI) p-value 0.022 (dominant), rs1051266 (SLC19A1) having OR 3.461 (2.136-5.609 at 95% CI) p-value 0.000000466 (dominant), rs2229080 (DCC) having OR 0.6867 (0.5123-0.9205 at 95% CI) p-value 0.0116 (allelic) and rs2298881 (ERCC1) having OR 0.669 (0.46-0.973 at 95% CI), p-value 0.035 (additive) respectively. The in-silico analysis was further used to fortify the above findings. CONCLUSION:It is further anticipated that the variants should be evaluated in other population groups that may aid in understanding the genetic complexity and bridge the missing heritability.

SUBMITTER: Bakshi D

PROVIDER: S-EPMC7487711 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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MassARRAY-based single nucleotide polymorphism analysis in breast cancer of north Indian population.

Bakshi Divya D Nagpal Ashna A Sharma Varun V Sharma Indu I Shah Ruchi R Sharma Bhanu B Bhat Amrita A Verma Sonali S Bhat Gh Rasool GR Abrol Deepak D Sharma Rahul R Vaishnavi Samantha S Kumar Rakesh R

BMC cancer 20200907 1

<h4>Background</h4>Breast Cancer (BC) is associated with inherited gene mutations. High throughput genotyping of BC samples has led to the identification and characterization of biomarkers for the diagnosis of BC. The most common genetic variants studied are SNPs (Single Nucleotide Polymorphisms) that determine susceptibility to an array of diseases thus serving as a potential tool for identifying the underlying causes of breast carcinogenesis.<h4>Methods</h4>SNP genotyping employing the Agena M ...[more]

PMID: 32894086

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Project description:BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

Project description:BackgroundCurrent breeding approaches in potato rely almost entirely on phenotypic evaluations; molecular markers, with the exception of a few linked to disease resistance traits, are not widely used. Large-scale sequence datasets generated primarily through Sanger Expressed Sequence Tag projects are available from a limited number of potato cultivars and access to next generation sequencing technologies permits rapid generation of sequence data for additional cultivars. When coupled with the advent of high throughput genotyping methods, an opportunity now exists for potato breeders to incorporate considerably more genotypic data into their decision-making.ResultsTo identify a large number of Single Nucleotide Polymorphisms (SNPs) in elite potato germplasm, we sequenced normalized cDNA prepared from three commercial potato cultivars: 'Atlantic', 'Premier Russet' and 'Snowden'. For each cultivar, we generated 2 Gb of sequence which was assembled into a representative transcriptome of ~28-29 Mb for each cultivar. Using the Maq SNP filter that filters read depth, density, and quality, 575,340 SNPs were identified within these three cultivars. In parallel, 2,358 SNPs were identified within existing Sanger sequences for three additional cultivars, 'Bintje', 'Kennebec', and 'Shepody'. Using a stringent set of filters in conjunction with the potato reference genome, we identified 69,011 high confidence SNPs from these six cultivars for use in genotyping with the Infinium platform. Ninety-six of these SNPs were used with a BeadXpress assay to assess allelic diversity in a germplasm panel of 248 lines; 82 of the SNPs proved sufficiently informative for subsequent analyses. Within diverse North American germplasm, the chip processing market class was most distinct, clearly separated from all other market classes. The round white and russet market classes both include fresh market and processing cultivars. Nevertheless, the russet and round white market classes are more distant from each other than processing are from fresh market types within these two groups.ConclusionsThe genotype data generated in this study, albeit limited in number, has revealed distinct relationships among the market classes of potato. The SNPs identified in this study will enable high-throughput genotyping of germplasm and populations, which in turn will enable more efficient marker-assisted breeding efforts in potato.

Project description:INTRODUCTION:Several Single Nucleotide Polymorphisms (SNPs) in lipid transport genes have been shown to be associated with Coronary Artery Disease (CAD). The Hepatic Lipase (HL)glycoprotein is a key component that catalyzes the hydrolysis of triglycerides and phospholipids in all major classes of lipoproteins. AIM:We studied whether the HL gene-250G/A polymorphism affect blood lipid level and the CAD in a North Indian population. MATERIALS AND METHODS:A total number of 477 subjects were enrolled in the study after approval of the Institutional Ethics Committee. Out of 477 subjects, 233 were with coronary artery disease as study group and 244 subjects without coronary artery disease as control group. All subjects recruited with matched ethnicity in age group of 40-70 years. Blood samples were collected in EDTA vials and genomic DNA was extracted from blood using the phenol-chloroform method. Lipid profile was estimated by using a commercially available kit. Polymorphisms in the HL (-250 G/A) gene were analysed by using restriction fragment length polymorphism-polymerase chain reaction (PCR-RFLP) method. The effect of this polymorphism on plasma lipids, lipoproteins and coronary artery disease was determined. RESULTS:In Human Hepatic Lipase (LIPC)-250G/A genotype, the frequencies of GG, GA and AA genotype in CAD group was 80.69%, 15.45% and 3.86%, respectively; in the control group, the corresponding frequencies were 90.16%, 9.02% and 0.82%, respectively. A significant difference was found in the genotype (LIPC-250G/A) distribution between the two groups. Further logistic regression analysis indicated that the GA and AA genotypes in SNP-250G/A were significantly associated with CAD in all genetic models (In codominant model- GA vs. GG, OR=1.91, 95% CI=1. 09-3.37, p=0. 03 and AA vs. GG, OR= 5.26, 95% CI= 1.10-24.60, p=0.04; in dominant model- GA+AA vs. GG, OR=2.19, p=0.004 and in recessive model- AA vs. GG+GA, OR=5.26, p=0.04 whereas, A allele at nucleotide -250G/A in the LIPC gene had an association with increased risk of CAD (OR=2.33, p=<0.008). CONCLUSION:Our findings indicated that the higher frequency of a dominant model (GA+AA) as well as mutant allele A of LIPC-250 G/A polymorphism is significantly associated with risk of CAD and the lipid profile can be used as a predictor of CAD.

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MassARRAY-based single nucleotide polymorphism analysis in breast cancer of north Indian population.

Publications

MassARRAY-based single nucleotide polymorphism analysis in breast cancer of north Indian population.

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