Research¶
Human Population Genetics¶
A large fraction of my research is centered on human population genetics - with a goal to better understand human evolutionary history and studies of human disease. A major throughline has been models of genealogical relationships within large samples, both for statistical genetics of complex traits or inference of population history. Some recent topics of interest include:
Population genetic inference using ARGs for demographic history, natural selection, and phenotypes
Refining genealogical inference with noisy samples and diverse mutational mechanisms (e.g., repeat polymorphisms)
Effective multi-ancestry study design and meta-analysis in genome-wide association studies
Representative Publications
Yulin Zhang* ‡, Arjun Biddanda* ‡ , Sarah A. Johnson, Colm O’Dushlaine, Priya Moorjani‡. (2026) Recovering signatures of archaic introgression using ancestral recombination graphs Science | Full Text | Software
Brian C Zhang, Arjun Biddanda, Árni F Gunnarsson, Fergus Cooper, Pier Francesco Palamara. (2023) Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits. Nature Genetics | Full Text | Software
Arjun Biddanda, Daniel P Rice, John Novembre. (2020). Geographic patterns of human allele frequency variation - a variant-centric perspective. eLife | Full Text | Software
Statistical Genetics of Chromosomal Abnormalities¶
Nearly half of conceptions in humans fail to lead to live birth. A major driver of pregnancy losses are errors in chromosome segregation, resulting in an improper number of chromosomes, or aneuploidy. I have developed statistical models for the analysis of both chromosomal errors and other diverse error types, focusing primarily on interptation of pre-implantation genetic testing (PGT) data from in-vitro fertilization (IVF) embryos. My broader goal is to develop novel statistical methods to infer a wider spectrum of genetic errors occurring during early development and improve our understanding of such errors, the fitness consequences of these errors, and parental genetic risk — collectively informing precision reproductive health.
Some active topics of research in this area:
Improving haplotype-based models for whole-chromosome and segmental aneuploidy detection
Mathematical models of negative selection and evolutionary rescue during gestation
Representative Publications
Sara A. Carioscia* , Arjun Biddanda* , Margaret R. Starostik, Xiaona Tang, Eva R. Hoffmann, Zachary P. Demko, Rajiv C. McCoy. (2026) Common variation in meiosis genes shapes human recombination phenotypes and aneuploidy risk. Nature | Full Text
Theoretical Population Genetics¶
Much of my applied work draws on results from population genetic theory, and I enjoy working on problems that involve more mathematical rigor. Typically, these are oriented towards understanding how theory can be used to intuit or improve statistical population genetic inference. Some active topics of research in this domain are:
Genealogical models with spatial and temporal structure
Evolution of recombination rates and modifiers of meiotic fidelity
Mathematical models for intra-cellular/somatic evolutionary inference
Representative Publications