# Scalable Computational Methods for Genealogical Inference: from species level to single cells

> **NIH NIH R56** · UNIVERSITY OF CALIFORNIA BERKELEY · 2023 · $315,000

## Abstract

PROJECT SUMMARY
Massive amounts of genomic data are currently being generated, providing unprecedented opportunities for
biomedical researchers to characterize various biological components and processes. In order to utilize these
data to make new biological discoveries and improve human health, accurate models and scalable computational
tools need to be developed to facilitate analysis and interpretation. The central objective of this project is to
address this challenge by developing more realistic probabilistic models, scalable algorithms, and user-friendly
software tools to enable the biomedical research community to better harness large genomic data. Many prob-
lems in genomics rely on computational methods for inferring genealogical information from large sequence data
and interpreting the reconstructed trees. In this application, we propose to make significant strides towards im-
proving this line of research by developing a suite of robust and scalable algorithms for probabilistic models of
molecular evolution and genealogical inference across multiple timescales. We will achieve our goal by carrying
out the following specific aims: 1) A fundamental problem in statistical analysis of molecular evolution is esti-
mating model parameters, for which maximum likelihood estimation (MLE) is typically employed. Unfortunately,
MLE is a computationally expensive task, in some cases prohibitively so. In Aim 1, we will utilize a novel MLE
framework and modern optimization methods to develop a broadly applicable computational method that
achieves several orders of magnitude speedup in MLE while maintaining high statistical efficiency for
general models of molecular evolution. We will apply our tools to improve phylogenetic inference for two clin-
ically important superfamilies of membrane proteins in humans, namely G protein-coupled receptors (GPCRs)
and Solute carrier (SLC) transporters. 2) Because of meiotic recombination, the genetic variability within humans
cannot be represented by a single tree. Instead, there are millions of different trees across the genome, where
each position in the genome will tend to have its own tree that only differs minimally from the trees in nearby
sites. The collection of all these trees, and the set of recombination points creating new trees, is represented
by the Ancestral Recombination Graph (ARG), which has a number of applications in human genetics. Despite
substantial recent progress on reconstructing ARGs, however, current methods are either too slow to scale up to
large data sets, or they do not sample ARGs accurately from a well-calibrated posterior distribution. In Aim 2,
will develop a new scalable computational method to improve ARG reconstruction and sampling. We
will test the method extensively on simulated data, develop a number of applications, and apply it on a number
of different human data sets to illustrate its utility. 3) Applications of genealogical inference methods have been
rapidly growing i...

## Key facts

- **NIH application ID:** 10889303
- **Project number:** 1R56HG013117-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Ian H Holmes
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $315,000
- **Award type:** 1
- **Project period:** 2023-09-01 → 2024-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10889303

## Citation

> US National Institutes of Health, RePORTER application 10889303, Scalable Computational Methods for Genealogical Inference: from species level to single cells (1R56HG013117-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10889303. Licensed CC0.

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