Brian C. Zhang

ORCID: 0000-0003-0366-6521
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About
Contact & Profiles
Research Areas
  • Genetic and phenotypic traits in livestock
  • Genetic Mapping and Diversity in Plants and Animals
  • Genetic Associations and Epidemiology
  • Microbial Metabolic Engineering and Bioproduction
  • Mitochondrial Function and Pathology
  • Genetic Neurodegenerative Diseases

Adaptive Biotechnologies (United States)
2024

University of Oxford
2021-2023

Abstract Genome-wide genealogies compactly represent the evolutionary history of a set genomes and inferring them from genetic data has potential to facilitate wide range analyses. We introduce method, ARG-Needle, for accurately biobank-scale sequencing or genotyping array data, as well strategies utilize perform association other complex trait use these methods build genome-wide using 337,464 UK Biobank individuals test across seven traits. Genealogy-based detects more rare ultra-rare...

10.1038/s41588-023-01379-x article EN cc-by Nature Genetics 2023-05-01

Abstract The ancestral recombination graph (ARG) is a graph-like structure that encodes detailed genealogical history of set individuals along the genome. ARGs are accurately reconstructed from genomic data have several downstream applications, but inference sets comprising millions samples and variants remains computationally challenging. We introduce Threads, threading-based method significantly reduces computational costs ARG while retaining high accuracy. apply Threads to infer 487,409...

10.1101/2024.08.31.610248 preprint EN bioRxiv (Cold Spring Harbor Laboratory) 2024-09-02

Abstract Accurate inference of gene genealogies from genetic data has the potential to facilitate a wide range analyses. We introduce method for accurately inferring biobank-scale genome-wide sequencing or genotyping array data, as well strategies utilize within linear mixed models perform association and other complex trait use these new methods build using 337,464 UK Biobank individuals detect associations in 7 traits. Genealogy-based detects more rare ultra-rare signals ( N = 133,...

10.1101/2021.11.03.466843 preprint EN bioRxiv (Cold Spring Harbor Laboratory) 2021-11-04
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