Ryan is a DPhil student supervised by Chris Holmes, Chris Spencer, and David Steinsaltz.
He has a wide range of research interests including genomics, computational statistics, and decision theory.
He is nearing the completion of his thesis: “Ancestral Trees as Weighted Networks: Applications in Large Genome Wide Association and Selection Scans.”
His thesis uses computational tricks for decoding Hidden Markov Models and results on the finite-sample behavior of quadratic forms to develop a scalable haplotype testing framework.
This work aims to help identify genes that might drive human disease and features an application to malaria susceptibility.
After graduation, Ryan will continue this line of research while pursuing a medical doctorate at Washington University in St. Louis.
In the long run, he aims to pursue an academic career at the intersection of medicine and statistics.
Publications
2015
H. C. Martin
,
R. Christ
,
J. G. Hussin
,
J. O’Connell
,
S. Gordon
,
H. Mbarek
,
J. Hottenga
,
K. McAloney
,
G. Willemsen
,
P. Gasparini
,
N. Pirastu
,
G. W. Montgomery
,
P. Navarro
,
N. Soranzo
,
D. Toniolo
,
V. Vitart
,
J. F. Wilson
,
J. Marchini
,
D. I. Boomsma
,
N. G. Martin
,
P. Donnelly
,
Multicohort analysis of the maternal age effect on recombination, Nature Communications, vol. 6, 7846, 2015.
Several studies have reported that the number of crossovers increases with maternal age in humans, but others have found the opposite. Resolving the true effect has implications for understanding the maternal age effect on aneuploidies. Here, we revisit this question in the largest sample to date using single nucleotide polymorphism (SNP)-chip data, comprising over 6,000 meioses from nine cohorts. We develop and fit a hierarchical model to allow for differences between cohorts and between mothers. We estimate that over 10 years, the expected number of maternal crossovers increases by 2.1% (95% credible interval (0.98%, 3.3%)). Our results are not consistent with the larger positive and negative effects previously reported in smaller cohorts. We see heterogeneity between cohorts that is likely due to chance effects in smaller samples, or possibly to confounders, emphasizing that care should be taken when interpreting results from any specific cohort about the effect of maternal age on recombination.
@article{Martin2015,
author = {Martin, Hilary C. and Christ, Ryan and Hussin, Julie G. and O'Connell, Jared and Gordon, Scott and Mbarek, Hamdi and Hottenga, Jouke-Jan and McAloney, Kerrie and Willemsen, Gonnecke and Gasparini, Paolo and Pirastu, Nicola and Montgomery, Grant W. and Navarro, Pau and Soranzo, Nicole and Toniolo, Daniela and Vitart, Veronique and Wilson, James F. and Marchini, Jonathan and Boomsma, Dorret I. and Martin, Nicholas G. and Donnelly, Peter},
doi = {10.1038/ncomms8846},
issn = {2041-1723},
journal = {Nature Communications},
pages = {7846},
pmid = {26242864},
title = {{Multicohort analysis of the maternal age effect on recombination}},
volume = {6},
year = {2015}
}
