AdmixSim 2: a forward-time simulator for modeling complex population admixture

Background Computer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture. Results We here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations. Conclusions AdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.

Deconvoluting complex correlates of COVID19 severity with local ancestry inference and viral phylodynamics: Results of a multiomic pandemic tracking strategy

The SARS-CoV-2 pandemic has differentially impacted populations of varied race, ethnicity and socioeconomic status. Admixture mapping and local ancestry inference represent powerful tools to examine genetic risk within multi-ancestry genomes independent of these confounding social constructs. Here, we leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from 1,327 nasopharyngeal swab residuals and integrate them with digital phenotypes from electronic health records. We demonstrate over-representation of individuals possessing Oceanian and Indigenous American ancestry in SARS-CoV-2 positive populations. Genome-wide-association disaggregated by admixture mapping reveals regions of chromosomes 5 and 14 associated with COVID19 severity within African and Oceanic local ancestries, respectively, independent of overall ancestry fraction. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. We further present summary data from a multi-omic investigation of human-leukocyte-antigen (HLA) typing, nasopharyngeal microbiome and human transcriptomics that reveal metagenomic and HLA associations with severe COVID19 infection. This work demonstrates the power of multi-omic pandemic tracking and genomic analyses to reveal distinct epidemiologic, genetic and biological associations for those at the highest risk.

Local ancestry inference provides insight into Tilapia breeding programmes

Tilapia is one of the most commercially valuable species in aquaculture with over 5 million tonnes of Nile tilapia, Oreochromis niloticus, produced worldwide every year. It has become increasingly important to keep track of the inheritance of the selected traits under continuous improvement (e.g. growth rate, size at maturity or genetic gender), as selective breeding has also resulted in genes that can hitchhike as part of the process. The goal of this study was to generate a Local Ancestry Interence workflow that harnessed existing tilapia genotyping-by-sequencing studies, such as Double Digest RAD-seq derived Single-Nucleotide Polymorphism markers. We developed a workflow and implemented a suite of tools to resolve the local ancestry of each chromosomal locus based on reference panels of tilapia species of known origin. We used tilapia species, wild populations and breeding programmes to validate our methods. The precision of the pipeline was evaluated on the basis of its ability to identify the genetic makeup of samples of known ancestry. The easy and inexpensive application of local ancestry inference in breeding programmes will facilitate the monitoring of the genetic profile of individuals of interest, the tracking of the movement of genes from parents to offspring and the detection of hybrids and their origin.

Comparing local ancestry inference models in populations of two- and three-way admixture

Local ancestry estimation infers the regional ancestral origin of chromosomal segments in admixed populations using reference populations and a variety of statistical models. Integrating local ancestry into complex trait genetics has the potential to increase detection of genetic associations and improve genetic prediction models in understudied admixed populations, including African Americans and Hispanics. Five methods for local ancestry estimation that have been used in human complex trait genetics are LAMP-LD (2012), RFMix (2013), ELAI (2014), Loter (2018), and MOSAIC (2019). As users rather than developers, we sought to perform direct comparisons of accuracy, runtime, memory usage, and usability of these software tools to determine which is best for incorporation into association study pipelines. We find that in the majority of cases RFMix has the highest median accuracy with the ranking of the remaining software dependent on the ancestral architecture of the population tested. Additionally, we estimate the O(n) of both memory and runtime for each software and find that for both time and memory most software increase linearly with respect to sample size. The only exception is RFMix, which increases quadratically with respect to runtime and linearly with respect to memory. Effective local ancestry estimation tools are necessary to increase diversity and prevent population disparities in human genetics studies. RFMix performs the best across methods, however, depending on application, other methods perform just as well with the benefit of shorter runtimes. Scripts used to format data, run software, and estimate accuracy can be found at https://github.com/WheelerLab/LAI_benchmarking.

XGMix: Local-Ancestry Inference with Stacked XGBoost

Genomic medicine promises increased resolution for accurate diagnosis, for personalized treatment, and for identification of population-wide health burdens at rapidly decreasing cost (with a genotype now cheaper than an MRI and dropping). The benefits of this emerging form of affordable, data-driven medicine will accrue predominantly to those populations whose genetic associations have been mapped, so it is of increasing concern that over 80% of such genome-wide association studies (GWAS) have been conducted solely within individuals of European ancestry [1]. The severe under-representation of the majority of the world’s populations in genetic association studies stems in part from an addressable algorithmic weakness: lack of simple, accurate, and easily trained methods for identifying and annotating ancestry along the genome (local ancestry). Here we present such a method (XGMix) based on gradient boosted trees, which, while being accurate, is also simple to use, and fast to train, taking minutes on consumer-level laptops.

Comparing local ancestry inference models in populations of two- and three-way admixture

Background: Local ancestry estimation infers the regional ancestral origin of chromosomal segments in admixed populations using reference populations and a variety of statistical models. Integrating local ancestry into complex trait genetics has the potential to increase detection of genetic associations and improve genetic prediction models in understudied admixed populations, including African Americans and Hispanics. Five methods for local ancestry estimation are LAMP-LD (2012), RFMix (2013), ELAI (2014), Loter (2018), and MOSAIC (2019), but direct comparisons of accuracy, runtime, and memory usage of all these software tools have not previously been reported across common patterns of human admixture. Results: We found that in cases of two-way admixture, RFMix and ELAI had the highest median accuracy depending on population structure, while in cases of three-way admixture, we found RFMix, MOSAIC, and LAMP-LD had the highest median accuracy. Additionally, we estimate the O(n) of both memory and runtime for each software and find that for both time and memory most software expand linearly with respect to sample size. The only exception is RFMix, which expands quadratically with respect to runtime and linearly with respect to memory. Conclusions: Effective local ancestry estimation tools are necessary to combat population disparities in human genetics studies. RFMix performs the best across methods, however, depending on application, other methods perform similarly well with the benefit of shorter runtimes. Scripts used to format data, run software, and estimate accuracy can be found at https://github.com/WheelerLab/LAI_benchmarking .