Human whole-exome genotype data for Alzheimer’s disease

Yuk Yee Leung (), Adam C. Naj, Yi-Fan Chou, Otto Valladares, Michael Schmidt, Kara Hamilton-Nelson, Nicholas Wheeler, Honghuang Lin, Prabhakaran Gangadharan, Liming Qu, Kaylyn Clark, Amanda B. Kuzma, Wan-Ping Lee, Laura Cantwell, Heather Nicaretta, Jonathan Haines, Lindsay Farrer, Sudha Seshadri, Zoran Brkanac, Carlos Cruchaga, Margaret Pericak-Vance, Richard P. Mayeux, William S. Bush, Anita Destefano, Eden Martin, Gerard D. Schellenberg and Li-San Wang ()
Additional contact information
Yuk Yee Leung: University of Pennsylvania
Adam C. Naj: University of Pennsylvania
Yi-Fan Chou: University of Pennsylvania
Otto Valladares: University of Pennsylvania
Michael Schmidt: Miller School of Medicine, University of Miami
Kara Hamilton-Nelson: Miller School of Medicine, University of Miami
Nicholas Wheeler: Case Western Reserve University
Honghuang Lin: UMass Chan Medical School
Prabhakaran Gangadharan: University of Pennsylvania
Liming Qu: University of Pennsylvania
Kaylyn Clark: University of Pennsylvania
Amanda B. Kuzma: University of Pennsylvania
Wan-Ping Lee: University of Pennsylvania
Laura Cantwell: University of Pennsylvania
Heather Nicaretta: University of Pennsylvania
Jonathan Haines: Case Western Reserve University
Lindsay Farrer: Boston University Chobanian & Avedisian School of Medicine
Sudha Seshadri: Boston University School of Medicine
Zoran Brkanac: University of Washington
Carlos Cruchaga: Washington University School of Medicine
Margaret Pericak-Vance: Miller School of Medicine, University of Miami
Richard P. Mayeux: Columbia University and the New York Presbyterian Hospital
William S. Bush: Case Western Reserve University
Anita Destefano: Boston University School of Public Health
Eden Martin: Miller School of Medicine, University of Miami
Gerard D. Schellenberg: University of Pennsylvania
Li-San Wang: University of Pennsylvania

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract The heterogeneity of the whole-exome sequencing (WES) data generation methods present a challenge to a joint analysis. Here we present a bioinformatics strategy for joint-calling 20,504 WES samples collected across nine studies and sequenced using ten capture kits in fourteen sequencing centers in the Alzheimer’s Disease Sequencing Project. The joint-genotype called variant-called format (VCF) file contains only positions within the union of capture kits. The VCF was then processed specifically to account for the batch effects arising from the use of different capture kits from different studies. We identified 8.2 million autosomal variants. 96.82% of the variants are high-quality, and are located in 28,579 Ensembl transcripts. 41% of the variants are intronic and 1.8% of the variants are with CADD > 30, indicating they are of high predicted pathogenicity. Here we show our new strategy can generate high-quality data from processing these diversely generated WES samples. The improved ability to combine data sequenced in different batches benefits the whole genomics research community.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-44781-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44781-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-44781-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().