Machine learning to optimize automated RH genotyping using whole-exome sequencing data

Author:

Chang Ti-Cheng1,Yu Jing2,Wang Zhaoming3ORCID,Hankins Jane S.4ORCID,Weiss Mitchell J.4,Wu Gang1,Westhoff Connie M.5,Chou Stella T.6ORCID,Zheng Yan2ORCID

Affiliation:

1. 1Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN

2. 2Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN

3. 3Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN

4. 4Department of Hematology, St. Jude Children’s Research Hospital, Memphis, TN

5. 5Laboratory of Immunohematology and Genomics, New York Blood Center Enterprises, New York, NY

6. 6Department of Pediatrics, Children’s Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, PA

Abstract

Abstract Rh phenotype matching reduces but does not eliminate alloimmunization in patients with sickle cell disease (SCD) due to RH genetic diversity that is not distinguishable by serological typing. RH genotype matching can potentially mitigate Rh alloimmunization but comprehensive and accessible genotyping methods are needed. We developed RHtyper as an automated algorithm to predict RH genotypes using whole-genome sequencing (WGS) data with high accuracy. Here, we adapted RHtyper for whole-exome sequencing (WES) data, which are more affordable but challenged by uneven sequencing coverage and exacerbated sequencing read misalignment, resulting in uncertain predictions for (1) RHD zygosity and hybrid alleles, (2) RHCE∗C vs. RHCE∗c alleles, (3) RHD c.1136C>T zygosity, and (4) RHCE c.48G>C zygosity. We optimized RHtyper to accurately predict RHD and RHCE genotypes using WES data by leveraging machine learning models and improved the concordance of WES with WGS predictions from 90.8% to 97.2% for RHD and 96.3% to 98.2% for RHCE among 396 patients in the Sickle Cell Clinical Research and Intervention Program. In a second validation cohort of 3030 cancer survivors (15.2% Black or African Americans) from the St. Jude Lifetime Cohort Study, the optimized RHtyper reached concordance rates between WES and WGS predications to 96.3% for RHD and 94.6% for RHCE. Machine learning improved the accuracy of RH predication using WES data. RHtyper has the potential, once implemented, to provide a precision medicine-based approach to facilitate RH genotype–matched transfusion and improve transfusion safety for patients with SCD. This study used data from clinical trials registered at ClinicalTrials.gov as #NCT02098863 and NCT00760656.

Publisher

American Society of Hematology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3