Systematic Review on Local Ancestor Inference From a Mathematical and Algorithmic Perspective

Abstract

Genotypic data provide deep insights into the population history and medical genetics. The local ancestry inference (LAI) (also termed local ancestry deconvolution) method uses the hidden Markov model (HMM) to solve the mathematical problem of ancestry reconstruction based on genomic data. HMM is combined with other statistical models and machine learning techniques for particular genetic tasks in a series of computer tools. In this article, we surveyed the mathematical structure, application characteristics, historical development, and benchmark analysis of the LAI method in detail, which will help researchers better understand and further develop LAI methods. Firstly, we extensively explore the mathematical structure of each model and its characteristic applications. Next, we use bibliometrics to show detailed model application fields and list articles to elaborate on the historical development. LAI publications had experienced a peak period during 2006–2016 and had kept on moving in the following years. The efficiency, accuracy, and stability of the existing models were evaluated by the benchmark. We find that phased data had higher accuracy in comparison with unphased data. We summarize these models with their distinct advantages and disadvantages. The Loter model uses dynamic programming to obtain a globally optimal solution with its parameter-free advantage. Aligned bases can be used directly in the Seqmix model if the genotype is hard to call. This research may help model developers to realize current challenges, develop more advanced models, and enable scholars to select appropriate models according to given populations and datasets.

Complex hybridization between deeply diverged fish species in a disturbed ecosystem

“…To facilitate this work, we developed a user-friendly pipeline for running local ancestry inference in hybrids which derive their genomes from three source populations, as well as a collection of simulation scripts to test expected performance (see Appendix 1). Although several methods have been developed that accommodate local ancestry inference with three source populations (reviewed in Wu et al 2021), there are few pipelines available that allow researchers to move from raw reads to probabilities of ancestry across the genome. By expanding our previously developed local ancestry inference pipeline and simulation scripts (Schumer et al , 2020), we are able to provide a toolkit that can be used by researchers to study complex hybridization events in diverse species groups.…”

Multi‐Omics Studies in Historically Excluded Populations: The Road to Equity

“…Numerous local ancestry inference (LAI) methods have been developed. Here, we cover a few of the most popular, although a thorough review can be found by Wu et al 24 STRUCTURE is a method to correct for local ancestry by using Hidden Markov Models (HMMs). 25 However, this method does not use the potentially rich information contained within haplotypes.…”

The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

“…Local ancestry information can help to understand fine scale admixture and the population genetic history, identify recent targets of selection, guide the selection of reference panels for genotype imputation, and improve the detection power of genetic association studies of admixed populations [184,[186][187][188][189]. Identifying the ancestry of chromosomal segments in admixed individuals facilitates the accurate identification of the history of genetic variants under selection [188], particularly where adaptive introgression has fixed or nearly fixed regions of the genome with specific population ancestry [190].…”

“…The most popular algorithms for LAI rely on hidden Markov models (HMM), an extension of a Markov chain, to identify the transformation of a genomic region from the reference, which is often not obvious [198]. These methods provide the posterior probabilities for each possible ancestry state at each ancestry-informative site along the chromosome [189,190]. The estimates obtained depend largely on reference populations; therefore, approaches to identify convergent signals of ancestry across multiple tests using different references have been developed [199].…”