I am a first-year PhD student in Genome Sciences at the University of Washington. I develop statistical methods for human genetics, and most of my work so far has been on fine-mapping.
I earned my master’s in biostatistics at Columbia University, where I worked with Dr. Gao Wang in the Statistical Functional Genomics lab on SuSiE, a widely used framework for fine-mapping. Before that I earned a bachelor’s in applied mathematics and statistics at Macalester College, working with Dr. Brianna Heggeseth on hidden Markov models for chronic kidney disease of uncertain etiology in Nicaragua.
Manuscript in preparation
Reframes LD reference mismatch as several distinct problems rather than one, and uses genotype sketches, shareable random projections of genotype data, to correct for finite reference uncertainty and population drift.
bioRxiv preprint · 2025
A modular redesign of the SuSiE fine-mapping framework that other methods can build on, with up to 15-fold speedups for summary-statistics fine-mapping, plus SuSiE-ash, which uses adaptive shrinkage to improve calibration under polygenic architectures.
bioRxiv DOI susieR Analysis code
The Sum of Single Effects (SuSiE) model is widely adopted for genetic fine-mapping, yet its original implementation faces architectural limitations that hinder extensibility and performance. We present SuSiE 2.0, a modular redesign that enables extensions to build on a shared algorithmic backbone, as demonstrated by mvSuSiE and mfSuSiE adopting this framework. The redesign also delivers substantial speed gains, accelerating summary-statistics fine-mapping by up to 15-fold and SuSiE-inf by up to 30-fold in settings where variants greatly outnumber samples. We also introduce SuSiE-ash, which uses adaptive shrinkage to recover part of the calibration gap between SuSiE and the more conservative SuSiE-inf under polygenic architectures while staying close to the power of SuSiE. Because background-driven false discoveries cannot be suppressed without giving up power, we recommend SuSiE for the genome-wide scan and reserve SuSiE-ash for genes of interest. Real-data benchmarks show SuSiE-based methods predict gene expression competitively with established models. This reveals them as effective yet underappreciated tools for TWAS.
International Journal of Epidemiology · 2023
Hidden Markov models applied to seven years of longitudinal kidney-function data, separating the onset of disease from its progression in an at-risk Nicaraguan cohort.
Background. Mesoamerican nephropathy is a leading contributor to premature mortality in Central America. Efforts to identify the cause are hampered by difficulties in distinguishing associations with potential initiating factors from common exposures thought to exacerbate the progression of all forms of established chronic kidney disease (CKD). We explored evidence of disease onset or departure from the healthy estimated glomerular filtration rate distribution [departure from ~eGFR(healthy)] in an at-risk population.
Methods. Two community-based cohorts (adults aged 18–30 years, n = 351 and 420) from 11 rural communities in Northwest Nicaragua were followed up over 7 and 3 years respectively. We examined associations with both (i) incident CKD and (ii) the time point of departure from ~eGFR(healthy), using a hidden Markov model.
Results. CKD occurred in men only (male incidence rate: 0.7%/year). Fifty-three (out of 1878 visits, 2.7%) and 8 (out of 1067 visits, 0.8%) episodes of probable departure from ~eGFR(healthy) occurred in men and women, respectively. Cumulative time in sugarcane work and symptoms of excess occupational sun exposure were associated with incident CKD. The same exposures were associated with probability of departure from ~eGFR(healthy) in time-updated analyses along with measured and self-reported weight loss, nausea, vomiting and cramps, as well as non-steroidal anti-inflammatory drug use.
Conclusions. CKD burden in this population is high and risk factors for established disease are occupational. Additionally, a syndrome suggesting an alternative exposure is associated with evidence of disease onset supporting a possible separate unknown initiating factor for which further investigation is needed. Interventions to reduce the impact of occupational risks should be pursued meanwhile.