Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

A Shafquat, RG Crystal, JG Mezey - BMC bioinformatics, 2020 - Springer
Background Heterogeneity in the definition and measurement of complex diseases in
Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification …

Data Simulation to Optimize the GWAS Framework in Diverse Populations

JW Mugo, ER Chimusa, N Mulder - medRxiv, 2023 - medrxiv.org
Whole-genome or genome-wide association studies have become a fundamental part of
modern genetic studies and methods for dissecting the genetic architecture of common traits …

FRANC: a unified framework for multi-way local ancestry deconvolution with high density SNP data

E Geza, NJ Mulder, ER Chimusa… - Briefings in …, 2020 - academic.oup.com
Several thousand genomes have been completed with millions of variants identified in the
human deoxyribonucleic acid sequences. These genomic variations, especially those …

JasMAP: A Joint Ancestry and SNP Association Method for a Multi-way Admixed Population

JW Mugo, ER Chimusa, N Mulder - medRxiv, 2023 - medrxiv.org
The large volume of research findings submitted to the GWAS catalog in the last decade is a
clear indication of the exponential progress of these studies and association approaches …

Investigating local ancestry inference models in mixed ancestry individual genomes

E Geza - 2022 - open.uct.ac.za
Owing to historical events including the slave trade, agricultural interests, colonialism, and
political and/or economical instability, most modern humans are a mosaic of segments …

[图书][B] Improving GWAS phenotypes through Bayesian and machine learning approaches

A Shafquat - 2020 - search.proquest.com
Large-scale genome-wide association studies (GWAS) have enabled detection of numerous
candidate genetic loci that may impact human diseases, expanded our knowledge about the …

[PDF][PDF] A. Personal Statement

PD Senior, RA Andersen - Neurobiology, 1995 - stanford.edu
I have trained in both electrical engineering and computer science (MIT PhD) and in systems
neuroscience (Caltech postdoc). I joined Stanford as an Assistant Professor in 2001, and …