Terpolovsky, A. | Search bioRxiv

bioRxiv2023

Retracing Human Genetic Histories and Natural Selection Using Precise Local Ancestry Inference

In an increasingly diverse world, including admixed individuals in genomic studies is imperative for equity and portability. A crucial first step is precise local ancestry inference (LAI). We have developed Orchestra, a LAI model with unprecedented accuracy, and trained on over 10,000 single-origin individuals from 35 worldwide populations. We employed Orchestra to delve into genetic relationships and demographic histories, with a focus on Latin Americans, a prime example of admixture, and the Ashkenazi Jewish, whose origins have long been debated. Finally, Orchestra enabled us to map signatures of selection, notably identifying trace Scandinavian ancestry in British samples and unveiling an immune-rich region linked to respiratory infections. Our work advances the field of LAI and holds promise for improvements in future applications for admixed populations. One-Sentence SummaryOrchestra unveils Latino and Ashkenazi ancestral roots and a candidate Viking locus under selection in the British population

genomics↗

bioRxiv2021

A comparative analysis of current phasing and imputation software

Whole-genome data has become significantly more accessible over the last two decades. This can largely be attributed to both reduced sequencing costs and imputation models which make it possible to obtain nearly whole-genome data from less expensive genotyping methods, such as microarray chips. Although there are many different approaches to imputation, the Hidden Markov Model remains the most widely used. In this study, we compared the latest versions of the most popular Hidden Markov Model based tools for phasing and imputation: Beagle 5.2, Eagle 2.4.1, Shapeit 4, Impute 5 and Minimac 4. We benchmarked them on three input datasets with three levels of chip density. We assessed each imputation software on the basis of accuracy, speed and memory usage, and showed how the choice of imputation accuracy metric can result in different interpretations. The highest average concordance rate was achieved by Beagle 5.2, followed by Impute 5 and Minimac 4, using a reference-based approach during phasing and the highest density chip. IQS and R2 metrics revealed that IMPUTE5 obtained better results for low frequency markers, while Beagle 5.2 remained more accurate for common markers (MAF>5%). Computational load as measured by run time was lower for Beagle 5.2 than Impute 5 and Minimac 4, while Minimac utilized the least memory of the imputation tools we compared. ShapeIT 4, used the least memory of the phasing tools examined, even with the highest density chip. Finally, we determined the combination of phasing software, imputation software, and reference panel, best suited for different situations and analysis needs and created an automated pipeline that provides a way for users to create customized chips designed to optimize their imputation results.

genomics↗