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Haplotypes at ATM identify coding-sequence variation and indicate a region of extensive linkage disequilibrium.

ABSTRACT: Genetic variation in the human population may lead to functional variants of genes that contribute to risk for common chronic diseases such as cancer. In an effort to detect such possible predisposing variants, we constructed haplotypes for a candidate gene and tested their efficacy in association studies. We developed haplotypes consisting of 14 biallelic neutral-sequence variants that span 142 kb of the ATM locus. ATM is the gene responsible for the autosomal recessive disease ataxia-telangiectasia (AT). These ATM noncoding single-nucleotide polymorphisms (SNPs) were genotyped in nine CEPH families (89 individuals) and in 260 DNA samples from four different ethnic origins. Analysis of these data with an expectation-maximization algorithm revealed 22 haplotypes at this locus, with three major haplotypes having frequencies > or = .10. Tests for recombination and linkage disequilibrium (LD) show reduced recombination and extensive LD at the ATM locus, in all four ethnic groups studied. The most striking example was found in the study population of European ancestry, in which no evidence for recombination could be discerned. The potential of ATM haplotypes for detection of genetic variants through association studies was tested by analysis of 84 individuals carrying one of three ATM coding SNPs. Each coding SNP was detected by association with an ATM haplotype. We demonstrate that association studies with haplotypes for candidate genes have significant potential for the detection of genetic backgrounds that contribute to disease.

SUBMITTER: Bonnen PE

PROVIDER: S-EPMC1287921 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe ATM protein is activated as a result of ionizing radiation, and genetic variants of the ATM gene may therefore affect the level of radiation-induced damage. Individuals heterozygous for ATM mutations have been reported to have an increased risk of malignancy, especially breast cancer.Materials and methodsNorwegian breast cancer patients (272) treated with radiation (252 of which were evaluated for radiation-induced adverse side effects), 95 Norwegian women with no known history of cancer and 95 American breast cancer patients treated with radiation (44 of which developed ipsilateral breast tumour recurrence, IBTR) were screened for sequence variations in all exons of the ATM gene as well as known intronic variants by denaturating high performance liquid chromatography (dHPLC) followed by sequencing to determine the nature of the variant.Results and conclusionA total of 56 variants were identified in the three materials combined. A borderline significant association with breast cancer risk was found for the 1229 T>C (Val>Ala) substitution in exon 11 (P-value 0.055) between the Norwegian controls and breast cancer patients as well as a borderline significant difference in haplotype distribution (P-value 0.06). Adverse side effects, such as: development of costal fractures and telangiectasias, subcutaneous and lung fibrosis, pleural thickening and atrophy were evaluated in the Norwegian patients. Significant associations were found for several of the identified variants such as rs1800058 (Leu > Phe) where a decrease in minor allele frequency was found with increasing level of adverse side effects for the clinical end-points pleural thickening and lung fibrosis, thus giving a protective effect. Overall our results indicate a role for variation in the ATM gene both for risk of developing breast cancer, and in radiation induced adverse side effects. No association could be found between risk of developing ipsilateral breast tumour recurrence and any of the sequence variants found in the American patient material.

Project description:Genomic prediction is an effective way to estimate the genomic breeding values from genetic information based on statistical methods such as best linear unbiased prediction (BLUP). The used of haplotype, clusters of linked single nucleotide polymorphism (SNP) as markers instead of individual SNPs can improve the accuracy of genomic prediction. Since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with a cluster of markers is higher compared to an individual marker. To make haplotypes efficient in genomic prediction, finding optimal ways to define haplotypes is essential. In this study, 770K or 50K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 3,498 cattle. Using SNP chip data, haplotype was defined in three different ways based on 1) the number of SNPs included, 2) length of haplotypes (bp), and 3) agglomerative hierarchical clustering based on LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; 5, 10, 20 or 50 SNPs on average per haplotype. A linear mixed model using haplotype to calculated the covariance matrix was applied for testing the prediction accuracy of each haplotype size. Also, conventional SNP-based linear mixed model was tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight (CWT), eye muscle area (EMA) and backfat thickness (BFT) were used as the phenotypes. This study reveals that using haplotypes generally showed increased accuracy compared to conventional SNP-based model for CWT and EMA, but found to be small or no increase in accuracy for BFT. LD clustering-based haplotypes specifically the five SNPs size showed the highest prediction accuracy for CWT and EMA. Meanwhile, the highest accuracy was obtained when length-based haplotypes with five SNPs were used for BFT. The maximum gain in accuracy was 1.3% from cross-validation and 4.6% from forward validation for EMA, suggesting that genomic prediction accuracy can be increased by using haplotypes. However, the improvement from using haplotypes may depend on the trait of interest. In addition, when the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles, thereby reducing computational costs. Therefore, finding optimal ways to define haplotypes and using the haplotype alleles as markers can improve the accuracy of genomic prediction.

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Haplotypes at ATM identify coding-sequence variation and indicate a region of extensive linkage disequilibrium.

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