Imputation reliability on DNA biallelic markers for drug metabolism studies.

BACKGROUND: Imputation is a statistical process used to predict genotypes of loci not directly assayed in a sample of individuals. Our goal is to measure the performance of imputation in predicting the genotype of the best known gene polymorphisms involved in drug metabolism using a common SNP array genotyping platform generally exploited in genome wide association studies. METHODS: Thirty-nine (39) individuals were genotyped with both Affymetrix Genome Wide Human SNP 6.0 (AFFY) and Affymetrix DMET Plus (DMET) platforms. AFFY and DMET contain nearly 900000 and 1931 markers respectively. We used a 1000 Genomes Pilot + HapMap 3 reference panel. Imputation was performed using the computer program Impute, version 2. SNPs contained in DMET, but not imputed, were analysed studying markers around their chromosome regions. The efficacy of the imputation was measured evaluating the number of successfully imputed SNPs (SSNPs). RESULTS: The imputation predicted the genotypes of 654 SNPs not present in the AFFY array, but contained in the DMET array. Approximately 1000 SNPs were not annotated in the reference panel and therefore they could not be directly imputed. After testing three different imputed genotype calling threshold (IGCT), we observed that imputation performs at its best for IGCT value equal to 50%, with rate of SSNPs (MAF > 0.05) equal to 85%. CONCLUSIONS: Most of the genes involved in drug metabolism can be imputed with high efficacy using standard genome-wide genotyping platforms and imputing procedures.

Fully non-homogeneous hidden Markov model double net: a generative model for haplotype reconstruction and block discovery.

OBJECTIVE: In the last decade, haplotype reconstruction in unrelated individuals and haplotype block discovery have riveted the attention of computer scientists due to the involved strong computational aspects. Such tasks are usually addressed separately, but recently, statistical techniques have permitted them to be solved jointly. Following this trend we propose a generative model that permits researchers to solve the two problems jointly. METHOD: The model inference is based on variational learning, which permits one to estimate quickly the model parameters while remaining robust even to local minima. The model parameters are then used to segment genotypes into blocks by thresholding a quantitative measure of boundary presence. RESULTS: Experiments on real data are presented, and state-of-the-art systems for haplotype reconstruction and strategies for block estimation are considered as comparison. CONCLUSIONS: The proposed method can be used for a fast and reliable estimation of haplotype frequencies and the relative block structure. Moreover, the method can be easily used as part of a more complex system. The threshold used for block discovery can be related to the quality-of-fit reached in the model learning, resulting in an unsupervised strategy for block estimation.

Quality assessment in cytogenetic and molecular genetic testing: the experience of the Italian Project on Standardisation and Quality Assurance.

The first Italian national trial of external quality assessment in genetic testing was organised within the framework of the "Italian National Project for Standardisation and Quality Assurance of Genetic Tests". Sixty-eight Public Health Service laboratories volunteered for the trial, which involved molecular genetic tests (cystic fibrosis, beta-thalassaemia, familial adenomatous polyposis coli and fragile-X syndrome) and cytogenetic tests (prenatal and postnatal, the latter included cancer cytogenetics). The response rate was high (88.2%). The level of analytical accuracy was good, i.e., the percentage of laboratories that correctly genotyped all samples was 89.3% for cystic fibrosis, 90.9% for beta-thalassaemia, 100% for familial adenomatous polyposis coli (despite two laboratories did not complete the analysis because the amount of DNA was considered insufficient), and 90.5% for fragile-X syndrome. Written reports differed widely and were judged "inadequate" in over 50% of cases. Most laboratories from the present study already have experience in previous European external quality assessments for at least one genetic test; this can explain the higher analytical accuracy in the Italian external quality assessment with respect to quality control programmes in other countries. Collaborative networks are strongly suggested to improve the quality of the reports.