Detecting high-order interactions of single nucleotide polymorphisms using genetic programming

MOTIVATION: Not individual single nucleotide polymorphisms (SNPs), but high-order interactions of SNPs are assumed to be responsible for complex diseases such as cancer. Therefore, one of the major goals of genetic association studies concerned with such genotype data is the identification of these high-order interactions. This search is additionally impeded by the fact that these interactions often are only explanatory for a relatively small subgroup of patients. Most of the feature selection methods proposed in the literature, unfortunately, fail at this task, since they can either only identify individual variables or interactions of a low order, or try to find rules that are explanatory for a high percentage of the observations. In this article, we present a procedure based on genetic programming and multi-valued logic that enables the identification of high-order interactions of categorical variables such as SNPs. This method called GPAS cannot only be used for feature selection, but can also be employed for discrimination. RESULTS: In an application to the genotype data from the GENICA study, an association study concerned with sporadic breast cancer, GPAS is able to identify high-order interactions of SNPs leading to a considerably increased breast cancer risk for different subsets of patients that are not found by other feature selection methods. As an application to a subset of the HapMap data shows, GPAS is not restricted to association studies comprising several 10 SNPs, but can also be employed to analyze whole-genome data. AVAILABILITY: Software can be downloaded from http://ls2-www.cs.uni-dortmund.de/~nunkesser/#Software     

Computational analysis of simulated SNP interactions between 26 growth factor-related genes in a breast cancer association study

Many association studies analyze the genotype frequencies of case and control data to predict susceptibility to diseases and cancers. Without providing the raw data for genotypes, many association studies cannot be interpreted fully. Often, the interactions of the single nucleotide polymorphisms (SNPs) are not addressed and this limits the potential of such studies. To solve these problems, we propose a novel computational method with source codes to generate a stimulated genotype dataset based on published SNP genotype frequencies. In this study we evaluate the combined effect of 26 SNP combinations related to eight published growth factor-related genes involved in carcinogenesis pathways of breast cancer. The genetic algorithm (GA) was chosen to provide simultaneous analysis of multiple independent SNPs. The GA can perform feature selection from different SNP combinations via their corresponding genotype (called the SNP barcode), and the approach is able to provide a specific SNP barcode with an optimized fitness value effectively. The best SNP barcode with the maximal occurrence difference between groups for the control and breast cancer, together with an odds ratio analysis, is used to evaluate breast cancer susceptibility. When they are compared to their corresponding non-SNP barcodes, the estimated odds ratios for breast cancer are less than 1 (about 0.85 and 0.87; confidence interval: 0.7473～0.9585, p?相似文献   

Novel generating protective single nucleotide polymorphism barcode for breast cancer using particle swarm optimization

Background: High-throughput single nucleotide polymorphism (SNP) genotyping generates a huge amount of SNP data in genome-wide association studies. Simultaneous analyses for multiple SNP interactions associated with many diseases and cancers are essential; however, these analyses are still computationally challenging. Methods: In this study, we propose an odds ratio-based binary particle swarm optimization (OR-BPSO) method to evaluate the risk of breast cancer. Results: BPSO provides the combinational SNPs with their corresponding genotype, called SNP barcodes, with the maximal difference of occurrence between the control and breast cancer groups. A specific SNP barcode with an optimized fitness value was identified among seven SNP combinations within the space of one minute. The identified SNP barcodes with the best performance between control and breast cancer groups were found to be control-dominant, suggesting that these SNP barcodes may prove protective against breast cancer. After statistical analysis, these control-dominant SNP barcodes were processed for odds ratio analysis for quantitative measurement with regard to the risk of breast cancer. Conclusion: This study proposes an effective high-speed method to analyze the SNP–SNP interactions for breast cancer association study.     

Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks

Background

Recent development of high-resolution single nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. There is increasing recognition of the importance of SNPs for medicine and developmental biology. However, SNP data set typically has a large number of SNPs (e.g., 400 thousand SNPs in genome-wide Parkinson disease data set) and a few hundred of samples. Conventional classification methods may not be effective when applied to such genome-wide SNP data.

Results

In this paper, we use shrunken dissimilarity measure to analyze and select relevant SNPs for classification problems. Examples of HapMap data and Parkinson disease (PD) data are given to demonstrate the effectiveness of the proposed method, and illustrate it has a potential to become a useful analysis tool for SNP data sets. We use Parkinson disease data as an example, and perform a whole genome analysis. For the 367440 SNPs with less than 1% missing percentage from all 22 chromosomes, we can select 357 SNPs from this data set. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and SNPs with significance of P < 0.01 are chosen to identify SNPs networks based on their P values. Here SNPs networks are constructed based on Gene Ontology knowledge, and therefore each SNP network plays a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to Parkinson disease.

Conclusions

Experimental results show that our approach is suitable to handle genetic variations, and provide useful knowledge in a genome-wide SNP study.

     

A Modified Entropy-Based Approach for Identifying Gene-Gene Interactions in Case-Control Study

Gene-gene interactions may play an important role in the genetics of a complex disease. Detection and characterization of gene-gene interactions is a challenging issue that has stimulated the development of various statistical methods to address it. In this study, we introduce a method to measure gene interactions using entropy-based statistics from a contingency table of trait and genotype combinations. We also developed an exploration procedure by using graphs. We propose a standardized relative information gain (RIG) measure to evaluate the interactions between single nucleotide polymorphism (SNP) combinations. To identify the k ^th order interactions, contingency tables of trait and genotype combinations of k SNPs are constructed, with which RIGs are calculated. The RIGs are standardized using the mean and standard deviation from the permuted datasets. SNP combinations yielding high standardized RIG are chosen for gene-gene interactions. Detection of high-order interactions and comparison of interaction strengths between different orders are made possible by using standardized RIG. We have applied the proposed standardized entropy-based method to two types of data sets from a simulation study and a real genetic association study. We have compared our method and the multifactor dimensionality reduction (MDR) method through power analysis of eight different genetic models with varying penetrance rates, number of SNPs, and sample sizes. Our method shows successful identification of genetic associations and gene-gene interactions both in simulation and real genetic data. Simulation results suggest that the proposed entropy-based method is better able to detect high-order interactions and is superior to the MDR method in most cases. The proposed method is well suited for detecting interactions without main effects as well as for models including main effects.     

Polymorphisms in the promoter region of ESR2 gene and breast cancer susceptibility

Genetic variations like single nucleotide polymorphisms (SNPs) in genes involved in estrogen biosynthesis, metabolism and signal transduction have been suggested to affect breast cancer susceptibility. In this study we tested the hypothesis that polymorphisms in the promoter of ESR2 gene may be associated with increased risk for breast cancer. We analyzed three SNPs in the promoter region of human ESR2 gene by means of allele-specific tetra-primer PCR. A total of 318 sporadic breast cancer cases and 318 age-matched controls were included in the study. With regard to homozygous genotypes, women with sporadic breast cancer more frequently carried the CC genotype of ESR2 promoter SNP rs2987983 (OR 1.99, p = 0.005). Calculation of allele positivity demonstrated that presence of T allele of this SNP was more frequent in healthy women. Our data suggest that a SNP in the promoter region of ESR2 gene might be able to affect breast cancer risk. These results further support the emerging hypothesis that ERβ is an important factor in breast cancer development.     

SNP interaction detection with random forests in high-dimensional genetic data

ABSTRACT: BACKGROUND: Identifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional. RESULTS: RF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions. CONCLUSIONS: While RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.     

A genome-wide screen of gene–gene interactions for rheumatoid arthritis susceptibility

The objective of the study was to identify interacting genes contributing to rheumatoid arthritis (RA) susceptibility and identify SNPs that discriminate between RA patients who were anti-cyclic citrullinated protein positive and healthy controls. We analyzed two independent cohorts from the North American Rheumatoid Arthritis Consortium. A cohort of 908 RA cases and 1,260 controls was used to discover pairwise interactions among SNPs and to identify a set of single nucleotide polymorphisms (SNPs) that predict RA status, and a second cohort of 952 cases and 1,760 controls was used to validate the findings. After adjusting for HLA-shared epitope alleles, we identified and replicated seven SNP pairs within the HLA class II locus with significant interaction effects. We failed to replicate significant pairwise interactions among non-HLA SNPs. The machine learning approach “random forest” applied to a set of SNPs selected from single-SNP and pairwise interaction tests identified 93 SNPs that distinguish RA cases from controls with 70% accuracy. HLA SNPs provide the most classification information, and inclusion of non-HLA SNPs improved classification. While specific gene–gene interactions are difficult to validate using genome-wide SNP data, a stepwise approach combining association and classification methods identifies candidate interacting SNPs that distinguish RA cases from healthy controls.     

Identification of novel SNPs in glioblastoma using targeted resequencing

High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other "omics" approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens.     

dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data

MOTIVATION: Oligonucleotide microarrays allow genotyping of thousands of single-nucleotide polymorphisms (SNPs) in parallel. Recently, this technology has been applied to loss-of-heterozygosity (LOH) analysis of paired normal and tumor samples. However, methods and software for analyzing such data are not fully developed. RESULT: Here, we report automated methods for pooling SNP array replicates to make LOH calls, visualizing SNP and LOH data along chromosomes in the context of genes and cytobands, making statistical inference to identify shared LOH regions, clustering samples based on LOH profiles and correlating the clustering results to clinical variables. Application of these methods to prostate and breast cancer datasets generates biologically important results. AVAILABILITY: The software module dChipSNP implementing these methods is available at http://biosun1.harvard.edu/complab/dchip/snp/ SUPPLEMENTARY INFORMATION: The breast cancer data are provided by Andrea L. Richardson, Zhigang C. Wang and James D. Iglehart.     

GPR30 gene polymorphisms are associated with progesterone receptor status and histopathological characteristics of breast cancer patients

G-protein coupled receptor GPR30 has been demonstrated to mediate estrogenic effects on essential features of human breast cancer cells. Polymorphisms in GPR30 gene might therefore affect breast cancer susceptibility or tumor characteristics. This is the first study examining allele and genotype frequencies of GPR30 single nucleotide polymorphisms (SNPs) in breast cancer patients. A total of 257 sporadic breast cancer cases and 247 age-matched controls were genotyped for three GPR30 polymorphisms by means of allele-specific tetra-primer PCR. Comparison of the breast cancer case and the control group with regard to the SNP allele, genotype and haplotype frequencies did not show significant differences. In contrast, the GPR30 SNPs tested were significantly associated with tumor size, histological grading, nodal status and progesterone receptor (PR) status. The A allele of SNP rs3808351 was significantly less frequent in patients with large or G3 tumors, T allele of SNP rs11544331 less frequently occurred in patients with positive nodal status, suggesting that both SNPs might exert protective effects regarding aggressive breast cancer entities. Both homozygous GG genotype of promoter SNP rs3808350 and T allele of missense SNP rs11544331 were inversely associated with PR-negativity, suggesting that they might exert protective effects regarding development of PR-negative cancer. In conclusion, the results of this study support the important role of GPR30 in breast cancer and encourage functional studies on the molecular mechanisms underlying the association of GPR30 polymorphisms with PR status and tumor growth.     

Screening large-scale association study data: exploiting interactions using random forests

Background  

Genome-wide association studies for complex diseases will produce genotypes on hundreds of thousands of single nucleotide polymorphisms (SNPs). A logical first approach to dealing with massive numbers of SNPs is to use some test to screen the SNPs, retaining only those that meet some criterion for futher study. For example, SNPs can be ranked by p-value, and those with the lowest p-values retained. When SNPs have large interaction effects but small marginal effects in a population, they are unlikely to be retained when univariate tests are used for screening. However, model-based screens that pre-specify interactions are impractical for data sets with thousands of SNPs. Random forest analysis is an alternative method that produces a single measure of importance for each predictor variable that takes into account interactions among variables without requiring model specification. Interactions increase the importance for the individual interacting variables, making them more likely to be given high importance relative to other variables. We test the performance of random forests as a screening procedure to identify small numbers of risk-associated SNPs from among large numbers of unassociated SNPs using complex disease models with up to 32 loci, incorporating both genetic heterogeneity and multi-locus interaction.     

A supervised approach for identifying discriminating genotype patterns and its application to breast cancer data

MOTIVATION: Large-scale association studies, investigating the genetic determinants of a phenotype of interest, are producing increasing amounts of genomic variation data on human cohorts. A fundamental challenge in these studies is the detection of genotypic patterns that discriminate individuals exhibiting the phenotype under study from individuals that do not possess it. The difficulty stems from the large number of single nucleotide polymorphism (SNP) combinations that have to be tested. The discrimination problem becomes even more involved when additional high-throughput data, such as gene expression data, are available for the same cohort. RESULTS: We have developed a graph theoretic approach for identifying discriminating patterns (DPs) for a given phenotype in a genotyped population. The method is based on representing the SNP data as a bipartite graph of individuals and their SNP states, and identifying fully connected subgraphs of this graph that relate individuals enriched for a given phenotypic group. The method can handle additional data types such as expression profiles of the genotyped population. It is reminiscent of biclustering approaches with the crucial difference that its search process is guided by the phenotype under consideration in a supervised manner. We tested our approach in simulations and on real data. In simulations, our method was able to retrieve planted patterns with high success rate. We then applied our approach to a dataset of 72 breast cancer patients with available gene expression profiles, genotyped over 695 SNPs. We detected several DPs that were highly significant with respect to various clinical phenotypes, and investigated the groups of patients and the groups of genes they defined. We found the patient groups to be highly enriched for other phenotypes and to display expression coherency among their profiles. The gene groups displayed functional coherency and involved genes with known role in cancer, providing additional support to their involvement. AVAILABILITY: The program is available upon request.     

A novel survival multifactor dimensionality reduction method for detecting gene–gene interactions with application to bladder cancer prognosis

The widespread use of high-throughput methods of single nucleotide polymorphism (SNP) genotyping has created a number of computational and statistical challenges. The problem of identifying SNP–SNP interactions in case–control studies has been studied extensively, and a number of new techniques have been developed. Little progress has been made, however, in the analysis of SNP–SNP interactions in relation to time-to-event data, such as patient survival time or time to cancer relapse. We present an extension of the two class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP–SNP interactions in the context of survival analysis. The proposed Survival MDR (Surv-MDR) method handles survival data by modifying MDR’s constructive induction algorithm to use the log-rank test. Surv-MDR replaces balanced accuracy with log-rank test statistics as the score to determine the best models. We simulated datasets with a survival outcome related to two loci in the absence of any marginal effects. We compared Surv-MDR with Cox-regression for their ability to identify the true predictive loci in these simulated data. We also used this simulation to construct the empirical distribution of Surv-MDR’s testing score. We then applied Surv-MDR to genetic data from a population-based epidemiologic study to find prognostic markers of survival time following a bladder cancer diagnosis. We identified several two-loci SNP combinations that have strong associations with patients’ survival outcome. Surv-MDR is capable of detecting interaction models with weak main effects. These epistatic models tend to be dropped by traditional Cox regression approaches to evaluating interactions. With improved efficiency to handle genome wide datasets, Surv-MDR will play an important role in a research strategy that embraces the complexity of the genotype–phenotype mapping relationship since epistatic interactions are an important component of the genetic basis of disease.     

Association of Single Nucleotide Polymorphisms in Wnt Signaling Pathway Genes with Breast Cancer in Saudi Patients

Breast cancer is a complex heterogeneous disease involving genetic and epigenetic alterations in genes encoding proteins that are components of various signaling pathways. Candidate gene approach have identified association of genetic variants in the Wnt signaling pathway genes and increased susceptibility to several diseases including breast cancer. Due to the rarity of somatic mutations in key genes of Wnt pathway, we investigated the association of genetic variants in these genes with predisposition to breast cancers. We performed a case-control study to identify risk variants by examining 15 SNPs located in 8 genes associated with Wnt signaling. Genotypic analysis of individual locus showed statistically significant association of five SNPs located in β-catenin, AXIN2, DKK3, SFRP3 and TCF7L2 with breast cancers. Increased risk was observed only with the SNP in β-catenin while the other four SNPs conferred protection against breast cancers. Majority of these associations persisted after stratification of the cases based on estrogen receptor status and age of on-set of breast cancer. The rs7775 SNP in exon 6 of SFRP3 gene that codes for either arginine or glycine exhibited very strong association with breast cancer, even after Bonferroni''s correction. Apart from these five variants, rs3923086 in AXIN2 and rs3763511 in DKK4 that did not show any association in the overall population were significantly associated with early on-set and estrogen receptor negative breast cancers, respectively. This is the first study to utilize pathway based approach to identify association of risk variants in the Wnt signaling pathway genes with breast cancers. Confirmation of our findings in larger populations of different ethnicities would provide evidence for the role of Wnt pathway as well as screening markers for early detection of breast carcinomas.     

Combinations of SNPs related to signal transduction in bipolar disorder

Any given single nucleotide polymorphism (SNP) in a genome may have little or no functional impact. A biologically significant effect may possibly emerge only when a number of key SNP-related genotypes occur together in a single organism. Thus, in analysis of many SNPs in association studies of complex diseases, it may be useful to look at combinations of genotypes. Genes related to signal transmission, e.g., ion channel genes, may be of interest in this respect in the context of bipolar disorder. In the present study, we analysed 803 SNPs in 55 genes related to aspects of signal transmission and calculated all combinations of three genotypes from the 3×803 SNP genotypes for 1355 controls and 607 patients with bipolar disorder. Four clusters of patient-specific combinations were identified. Permutation tests indicated that some of these combinations might be related to bipolar disorder. The WTCCC bipolar dataset were use for replication, 469 of the 803 SNP were present in the WTCCC dataset either directly (n = 132) or by imputation (n = 337) covering 51 of our selected genes. We found three clusters of patient-specific 3×SNP combinations in the WTCCC dataset. Different SNPs were involved in the clusters in the two datasets. The present analyses of the combinations of SNP genotypes support a role for both genetic heterogeneity and interactions in the genetic architecture of bipolar disorder.     

Determining and interpreting new predictive rules for breast cancer familial inheritance

DNA copy number alterations have been discovered to be key genetic events in development and progression of cancer. No clear data of familial and sporadic breast cancer are available. We focused on looking for an independent platform as a tool to identify the chromosomal profile in familial versus sporadic breast cancer patients. A total of 124 breast cancer patients were studied utilizing aCGH. The dataset was analyzed using Gaussian Mixture Models to determine the thresholds in order to assess gene copy number changes and to minimize the impact of noise on further data analyses. The identification of regions of consistent aberration across samples was carried out with statistical approaches and machine learning tools to draw profiles for familial and sporadic groups. Familial and sporadic cases resulted with a chromosome imbalance of 15% [false discovery rate (FDR): q=718E-5] and 18% (FDR: q=632E-13), respectively. The differential map evidenced two cytogenetic bands (8p23 and 11q13-11q14) significantly altered in familial versus sporadic cases (FDR: q=7E-4). The application of a new bioinformatics tool that discovers fuzzy classification rules (IFRAIS) let to individualize association of genes alterations that identify familial or sporadic cases. These results are comparable to those of the other systems used and are consistent from the biological point of view.     

Particle swarm optimization algorithm for analyzing SNP–SNP interaction of renin-angiotensin system genes against hypertension

Most non-significant individual single nucleotide polymorphisms (SNPs) were undiscovered in hypertension association studies. Their possible SNP–SNP interactions were usually ignored and leaded to missing heritability. In present study, we proposed a particle swarm optimization (PSO) algorithm to analyze the SNP–SNP interaction associated with hypertension. Genotype dataset of eight SNPs of renin-angiotensin system genes for 130 non-hypertension and 313 hypertension subjects were included. Without SNP–SNP interaction, most individual SNPs were non-significant difference between the hypertension and non-hypertension groups. For SNP–SNP interaction, PSO can select the SNP combinations involving different SNP numbers, namely the best SNP barcodes, to show the maximum frequency difference between non-hypertension and hypertension groups. After computation, the best PSO-generated SNP barcodes were dominant in non-hypertension in terms of the occurrences of frequency differences between non-hypertension and hypertension groups. The OR values of the best SNP barcodes involving 2–8 SNPs were 0.705–0.334, suggesting that these SNP barcodes were protective against hypertension. In conclusion, this study demonstrated that non-significant SNPs may generate the joint effect in association study. Our proposed PSO algorithm is effective to identify the best protective SNP barcodes against hypertension.     

Gene-centric characteristics of genome-wide association studies

Background

The high-throughput genotyping chips have contributed greatly to genome-wide association (GWA) studies to identify novel disease susceptibility single nucleotide polymorphisms (SNPs). The high-density chips are designed using two different SNP selection approaches, the direct gene-centric approach, and the indirect quasi-random SNPs or linkage disequilibrium (LD)-based tagSNPs approaches. Although all these approaches can provide high genome coverage and ascertain variants in genes, it is not clear to which extent these approaches could capture the common genic variants. It is also important to characterize and compare the differences between these approaches.

Methodology/Principal Findings

In our study, by using both the Phase II HapMap data and the disease variants extracted from OMIM, a gene-centric evaluation was first performed to evaluate the ability of the approaches in capturing the disease variants in Caucasian population. Then the distribution patterns of SNPs were also characterized in genic regions, evolutionarily conserved introns and nongenic regions, ontologies and pathways. The results show that, no mater which SNP selection approach is used, the current high-density SNP chips provide very high coverage in genic regions and can capture most of known common disease variants under HapMap frame. The results also show that the differences between the direct and the indirect approaches are relatively small. Both have similar SNP distribution patterns in these gene-centric characteristics.

Conclusions/Significance

This study suggests that the indirect approaches not only have the advantage of high coverage but also are useful for studies focusing on various functional SNPs either in genes or in the conserved regions that the direct approach supports. The study and the annotation of characteristics will be helpful for designing and analyzing GWA studies that aim to identify genetic risk factors involved in common diseases, especially variants in genes and conserved regions.     

Investigation on BRCA1 SNPs and its effects on mastitis in Chinese commercial cattle

The main objective of this study was to investigate whether the bovine breast cancer 1 (BRCA1) gene was associated with mastitis resistance in Chinese commercial cattle. A total of 51 SNPs were screened from public data resources and DNA sequencing. Three SNPs (c.5682G>C,c.26198C>T and c.46126G>T) were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and created restriction site PCR (CRS-PCR) methods and 21 combinations of these SNPs were observed. The single SNP and their genetic effects on somatic cell score (SCS) were evaluated and a significant association with SCS was found in c.46126G>T. The mean SCS of individuals with genotype KK was significantly lower than those of genotypes KL and LL. The results of combined genotypes analysis of three SNPs showed that HHLLNN genotype with the highest SCS was easily for the mastitis susceptibility, whereas GGKKMM genotype with the lowest SCS was favorable for the mastitis resistance. The information provided in the present study will be very useful for improving mastitis resistance in dairy cattle by marker-assisted selection (MAS).