An empirical evaluation of genotype imputation of ancient DNA

Abstract

Genome-wide association study (GWAS) is fundamentally designed to detect disease-causing genes. To reduce spurious associations or improve statistical power, about 80% of GWASs arbitrarily adjusted for demographic and clinical covariates. However, adjustment strategies in GWASs have not achieved consistent conclusions. Given the initial aim of GWAS that is to identify the causal association between a specific causal single-nucleotide polymorphism (SNP) and disease trait, we summarized all complex relationships of the target SNP, covariate and disease trait into 15 causal diagrams according to various roles of the covariate. Following each causal diagram, we conducted a series of theoretical justifications and statistical simulations. Our results demonstrate that it is unadvisable to adjust for any demographic or clinical covariates. We illustrate our point by applying GWASs for body mass index (BMI) and breast cancer, including adjusting and non-adjusting for age and smoking status. Genetic effects and P values might vary across different strategies. Instead, adjustments for SNPs (G') should be strongly recommended when G' are in linkage disequilibrium with the target SNP, and correlated with disease trait conditional on the target SNP. Specifically, adjustment for such G' can block all the confounding paths between the target SNP and disease trait, and avoid over-adjusting for colliders or intermediaries.

Imputation of Ancient Whole Genome Sus scrofa DNA Introduces Biases Toward Main Population Components in the Reference Panel

“…Imputation was assessed by comparing imputed genotypes to their corresponding HQ genotypes, similar to previous studies ( Gamba et al, 2014 ; Martiniano et al, 2017 ; Ausmees et al, 2021 ). Three of the seven samples with high-coverage data (KD033, KD037, and VEM185) were downsampled with Picard v2.18.17 ( http://broadinstitute.github.io/picard ), to create low coverage samples for imputation ranging from 0.5 to 2× with steps of 0.5×.…”

“…In aDNA studies, imputation has been applied on human genomes and achieved high levels of concordance between imputed genotypes and their high-quality (HQ) counterparts (>99%) ( Gamba et al, 2014 ; Martiniano et al, 2017 ; Ausmees et al, 2021 ). Imputation of aDNA beyond humans is lacking; livestock aDNA is critical to understand pivotal moments in recent evolution such as domestication and pose an excellent case study.…”

Achieving improved accuracy for imputation of ancient DNA

“…Unlike most previous studies of imputation of aDNA, we did not filter the imputation input to remove genotypes that could be affected by post-mortem damage, which was motivated by the findings in [15]. More information about the samples and data preparation procedure can be found in [14].…”

“…In the aDNA community, imputation has mainly been performed using the algorithm of Beagle 4.0 with genotype likelihoods as input [9]- [14]. In a recent study, an evaluation of different imputation pipelines on an empirical ancient sample was conducted [15].…”

Quantitative Human Paleogenetics: what can ancient DNA tell us about complex trait evolution?

“…An alternative approach is to perform low-coverage shotgun sequencing, followed by imputation, using a large reference panel (Ausmees et al, 2019;Hui et al, 2020). This has the dual advantages of reducing ascertainment bias and increasing the number of GWAS variants available to calculate polygenic scores.…”