Dataset Information

A phased SNP-based classification of sickle cell anemia HBB haplotypes.

ABSTRACT: BACKGROUND:Sickle cell anemia causes severe complications and premature death. Five common ?-globin gene cluster haplotypes are each associated with characteristic fetal hemoglobin (HbF) levels. As HbF is the major modulator of disease severity, classifying patients according to haplotype is useful. The first method of haplotype classification used restriction fragment length polymorphisms (RFLPs) to detect single nucleotide polymorphisms (SNPs) in the ?-globin gene cluster. This is labor intensive, and error prone. METHODS:We used genome-wide SNP data imputed to the 1000 Genomes reference panel to obtain phased data distinguishing parental alleles. RESULTS:We successfully haplotyped 813 sickle cell anemia patients previously classified by RFLPs with a concordance >98%. Four SNPs (rs3834466, rs28440105, rs10128556, and rs968857) marking four different restriction enzyme sites unequivocally defined most haplotypes. We were able to assign a haplotype to 86% of samples that were either partially or misclassified using RFLPs. CONCLUSION:Phased data using only four SNPs allowed unequivocal assignment of a haplotype that was not always possible using a larger number of RFLPs. Given the availability of genome-wide SNP data, our method is rapid and does not require high computational resources.

SUBMITTER: Shaikho EM

PROVIDER: S-EPMC5553663 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A phased SNP-based classification of sickle cell anemia HBB haplotypes.

Shaikho Elmutaz M EM Farrell John J JJ Alsultan Abdulrahman A Qutub Hatem H Al-Ali Amein K AK Figueiredo Maria Stella MS Chui David H K DHK Farrer Lindsay A LA Murphy George J GJ Mostoslavsky Gustavo G Sebastiani Paola P Steinberg Martin H MH

BMC genomics 20170811 1

<h4>Background</h4>Sickle cell anemia causes severe complications and premature death. Five common β-globin gene cluster haplotypes are each associated with characteristic fetal hemoglobin (HbF) levels. As HbF is the major modulator of disease severity, classifying patients according to haplotype is useful. The first method of haplotype classification used restriction fragment length polymorphisms (RFLPs) to detect single nucleotide polymorphisms (SNPs) in the β-globin gene cluster. This is labo ...[more]

PMID: 28800727

Similar Datasets

Project description:Sickle cell disease (SCD) is a hematological disorder leading to blood vessel occlusion accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients have diverse shapes that reveal important biomechanical and bio-rheological characteristics, e.g. their density, fragility, adhesive properties, etc. Hence, having an objective and effective way of RBC shape quantification and classification will lead to better insights and eventual better prognosis of the disease. To this end, we have developed an automated, high-throughput, ex-vivo RBC shape classification framework that consists of three stages. First, we present an automatic hierarchical RBC extraction method to detect the RBC region (ROI) from the background, and then separate touching RBCs in the ROI images by applying an improved random walk method based on automatic seed generation. Second, we apply a mask-based RBC patch-size normalization method to normalize the variant size of segmented single RBC patches into uniform size. Third, we employ deep convolutional neural networks (CNNs) to realize RBC classification; the alternating convolution and pooling operations can deal with non-linear and complex patterns. Furthermore, we investigate the specific shape factor quantification for the classified RBC image data in order to develop a general multiscale shape analysis. We perform several experiments on raw microscopy image datasets from 8 SCD patients (over 7,000 single RBC images) through a 5-fold cross validation method both for oxygenated and deoxygenated RBCs. We demonstrate that the proposed framework can successfully classify sickle shape RBCs in an automated manner with high accuracy, and we also provide the corresponding shape factor analysis, which can be used synergistically with the CNN analysis for more robust predictions. Moreover, the trained deep CNN exhibits good performance even for a deoxygenated dataset and distinguishes the subtle differences in texture alteration inside the oxygenated and deoxygenated RBCs.

Dataset Information

A phased SNP-based classification of sickle cell anemia HBB haplotypes.

Publications

A phased SNP-based classification of sickle cell anemia HBB haplotypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets