Population-genetic model inference from low-coverage allele frequency spectra

Overview

This repository contains an implementation of a method for correcting low-coverage distortion in demographic inference. The method is designed to address challenges arising from insufficient data coverage, enhancing the accuracy and reliability of the results. Within this repository, you will find code, data, and results encompassing all simulations and empirical data analyses presented in the manuscript "Population-genetic model inference from low-coverage allele frequency spectra".

Preprint

Fonseca, E.M., Tran, L., Mendonza, H., Gutenkunst, R.N. Population-genetic model inference from low-coverage allele frequency spectra (coming soon).

What does this repository contain?

• The /low-cov directory contains the entire codebase used to implementing the method designed to correct bias arising from low-coverage sequencing.

• The /simulations directory contains the code for generating and running simulations at various coverage levels. Moreover, it includes the resulting site frequency spectra from these simulations.

• The /demography directory contains the code for generating and running demographic inference under various coverage levels.

Prerequisites

• Python 3
• dadi (https://dadi.readthedocs.io/en/latest/)
• numpy
• math
• scipy
• itertools
• warnings

Usage

make_low_cov_func(demo_model, data_dict, pop_ids, nseq, nsub, sim_threshold, inbreeding)

• demo_model: specified demographic model in dadi
• data_dict: a data dictionary comprising information extracted from a VCF file
• pop_ids: population names to be analyzed
• nseq: total number of samples for a given population
• nsub: subsampled number of samples for a given population
• sim_threshold: This method switches between analytic and simulation-based methods. Setting this threshold to 0 will always use simulations, while setting it to 1 will always use analytics. Values in between indicate that simulations will be employed for thresholds below that value.
• inbreeding (bool): If True, the model accounts for inbreeding; if False, it does not.

Example

Import necessary libraries

import dadi
from dadi.LowCoverage import LowCoverage
import nlopt

Paths to input data files

datafile = '/simulations/simulated_datasets/1D_exp_growth_two_epochs_nu1_10_T1_0.1/heterogeneous_coverage/coverage_3/VCF_files_gatk/gatk_Replicate_1_filtered.vcf'
popfile = '/simulations/simulated_datasets/1D_exp_growth_two_epochs_nu1_10_T1_0.1/heterogeneous_coverage/coverage_3/VCF_files_gatk/popfile.txt'

Define data parameters

pop_id = 'pop1'  # Population name
nseq = 40  # Total number of sequenced chromosomes
nsub = 32  # Number of chromosomes to be subsampled
ss = {pop_id: nsub // 2}  # Subsampling dictionary

Create data dictionary from the VCF file

data_dict = dadi.Misc.make_data_dict_vcf(datafile, popfile, subsample=ss)

Assign outgroup information to each genomic position

for chrom_pos in data_dict:
    data_dict[chrom_pos]['outgroup_allele'] = data_dict[chrom_pos]['segregating'][0]
    data_dict[chrom_pos]['outgroup_context'] = data_dict[chrom_pos]['segregating'][0]

Generate the Site Frequency Spectrum (SFS)

data_fs = dadi.Spectrum.from_data_dict(data_dict, [pop_id], [nsub])

Define the demographic model (exponential growth)

demo_model_ex = dadi.Numerics.make_extrap_func(dadi.Demographics1D.growth)

Wrap the demographic model with low coverage model

demo_model_ex = LowCoverage.make_low_cov_func(demo_model_ex, data_dict, data_fs.pop_ids, [nseq], [nsub], sim_threshold=1e-2, inbreeding=False)

Define demographic parameters

param_names = ['nu1', 'T']  # Growth rate and time since growth started
lower_bounds = [1e-05, 1e-05]  # Lower bounds for parameters
upper_bounds = [50, 1]  # Upper bounds for parameters
params = [1, 0.05]  # Initial parameter values
grids = [50, 60, 70]  # Grid points for optimization

Perturb the initial parameters

p0 = dadi.Misc.perturb_params(params, fold=1, upper_bound=upper_bounds, lower_bound=lower_bounds)

Run optimization for parameter inference

popt, ll_model = dadi.Inference.opt(p0, data_fs, demo_model_ex, grids,
                                    lower_bound=lower_bounds,
                                    upper_bound=upper_bounds,
                                    algorithm=nlopt.LN_COBYLA,
                                    maxeval=1000, verbose=0)

Print the results

print('True parameters are: nu1 = 10 and T = 0.1')
print(f'The inferred parameters were: nu1 = {popt[0]:.2f} and T = {popt[1]:.2f}')

Questions

For any questions, please contact Emanuel M. Fonseca (emanuelmfonseca@arizona.email.edu or emanuelmfonseca@gmail.com) or Ryan Gutenkunst (rgutenk@arizona.edu).