BOOGIE: Predicting Blood Groups from High Throughput Sequencing Data.

Manuel Giollo,Carlo Ferrari,Emanuela Leonardi,Silvio C E Tosatto,Giovanni Minervini,Marta Scalzotto,Jeong-Sun Seo

doi:10.1371/journal.pone.0124579

Abstract

Over the last decade, we have witnessed an incredible growth in the amount of available genotype data due to high throughput sequencing (HTS) techniques. This information may be used to predict phenotypes of medical relevance, and pave the way towards personalized medicine. Blood phenotypes (e.g. ABO and Rh) are a purely genetic trait that has been extensively studied for decades, with currently over thirty known blood groups. Given the public availability of blood group data, it is of interest to predict these phenotypes from HTS data which may translate into more accurate blood typing in clinical practice. Here we propose BOOGIE, a fast predictor for the inference of blood groups from single nucleotide variant (SNV) databases. We focus on the prediction of thirty blood groups ranging from the well known ABO and Rh, to the less studied Junior or Diego. BOOGIE correctly predicted the blood group with 94% accuracy for the Personal Genome Project whole genome profiles where good quality SNV annotation was available. Additionally, our tool produces a high quality haplotype phase, which is of interest in the context of ethnicity-specific polymorphisms or traits. The versatility and simplicity of the analysis make it easily interpretable and allow easy extension of the protocol towards other phenotypes. BOOGIE can be downloaded from URL http://protein.bio.unipd.it/download/.

Highlights

Advances in genome sequencing due to high throughput sequencing (HTS) over the last years have detected a huge amount of new Single Nucleotide Variants (SNVs) [1], producing a tremendous growth of variation databases
BOOGIE is designed to predict phenotypes using HTS data using explicit tables that describe the correlation of SNVs with traits
In order to understand how much is known in the context of blood groups, we report the amount of SNVs in BGMUT and in dbSNP for each blood system, and use these two parameters to measure the completeness of our haplotype tables

Summary

Introduction

Advances in genome sequencing due to high throughput sequencing (HTS) over the last years have detected a huge amount of new Single Nucleotide Variants (SNVs) [1], producing a tremendous growth of variation databases. The BLOODchip system [21] is a first example of a commercial solution using genotype data to detect blood types, showing that modern sequencing techniques can be used for the identification of six different blood groups Even though this is an appealing idea, a number of issues must still be solved, since the three billion human nucleotides are difficult to manage [22]. The Rh trait is just an example with good genotype knowledge and a complicated basis, since it is encoded by two different genes resulting in the two proteins RhD and RhCE [27] The former is the determinant of the most common Rh antigen while the latter is responsible for a large part of weak inter medium Rh traits. While these are not well studied and poorly understood, such genetic complexity well explains the importance of large human variant databases for blood groups such as BGMUT [5]

Methods

Results

Conclusion