The current standard practice for assembling individual genomes involves mapping millions of short DNA sequences (also known as DNA ‘reads’) against a pre-constructed reference genome. Mapping vast amounts of short reads in a timely manner is a computationally challenging task that inevitably produces artefacts, including biases against alleles not found in the reference genome. This reference bias and other mapping artefacts are expected to be exacerbated in ancient DNA (aDNA) studies, which rely on the analysis of low quantities of damaged and very short DNA fragments (~30–80 bp). Nevertheless, the current gold-standard mapping strategies for aDNA studies have effectively remained unchanged for nearly a decade, during which time new software has emerged. In this study, we used simulated aDNA reads from three different human populations to benchmark the performance of 30 distinct mapping strategies implemented across four different read mapping software—BWA-aln, BWA-mem, NovoAlign and Bowtie2—and quantified the impact of reference bias in downstream population genetic analyses. We show that specific NovoAlign, BWA-aln and BWA-mem parameterizations achieve high mapping precision with low levels of reference bias, particularly after filtering out reads with low mapping qualities. However, unbiased NovoAlign results required the use of an IUPAC reference genome. While relevant only to aDNA projects where reference population data are available, the benefit of using an IUPAC reference demonstrates the value of incorporating population genetic information into the aDNA mapping process, echoing recent results based on graph genome representations.

Genomic information is increasingly used to inform medical treatments and manage future disease risks. However, any personal and societal gains must be carefully balanced against the risk to individuals contributing their genomic data. Expanding our understanding of actionable genomic insights requires researchers to access large global datasets to capture the complexity of genomic contribution to diseases. Similarly, clinicians need efficient access to a patient’s genome as well as population-representative historical records for evidence-based decisions. Both researchers and clinicians hence rely on participants to consent to the use of their genomic data, which in turn requires trust in the professional and ethical handling of this information.Here, we review existing and emerging solutions for secure and effective genomic information management, including storage, encryption, consent, and authorization that are needed to build participant trust. We discuss recent innovations in cloud computing, quantum-computing-proof encryption, and self-sovereign identity. These innovations can augment key developments from within the genomics community, notably GA4GH Passports and the Crypt4GH file container standard. We also explore how decentralized storage as well as the digital consenting process can offer culturally acceptable processes to encourage data contributions from ethnic minorities.We conclude that the individual and their right for self-determination needs to be put at the center of any genomics framework, because only on an individual level can the received benefits be accurately balanced against the risk of exposing private information.

Xu et al. (2021) recently recommended a new parameterization of BWA-mem as a superior alternative to the widely-used BWA-aln algorithm to map ancient DNA sequencing data. Here, we compare the BWA-mem parameterization recommended by Xu et al. with the best-performing alignment methods determined in the recent benchmarks of Oliva and colleagues (2021), demonstrating that BWA-aln is still the gold-standard for ancient DNA read alignment .

Genomics plays a crucial role in addressing health disparities, yet most studies rely on the hg38 linear reference genome, limiting the potential of pangenomic approaches, particularly for underrepresented populations. In this study, we focus on characterising East African populations, particularly Somalis, by constructing a variation graph using Mozabites from the Human Genome Diversity Project (HGDP) given their ancestral affinity with Somalis. We evaluated the effectiveness of this graph-based reference in estimating effective population sizes (Ne) in Bedouins compared to the hg38 reference and examined its impact on allele frequencies and genome-wide association studies (GWAS). Applying a coalescent model to the graph-based reference produced a Ne estimate of approximately 17 for the Bedouin population, which was significantly lower than the estimate from the hg38 reference (approximately 79,000). Only the graph-based estimate fell within the 95% confidence interval in simulations, indicating improved accuracy. Moreover, graph variants exhibited significantly lower allele frequencies (p-value < 2.2 × 10⁻¹⁶), suggesting potential effects on the interpretation and power of GWAS. Notably, GWAS variants specific to Bedouins derived from the graph showed lower frequencies (p = 0.023) than those obtained from the linear reference. These findings suggest that a pangenomic approach, informed by populations with ancestral affinities such as the Mozabites, provides more accurate estimates of Ne and allele frequencies. This highlights the importance of pangenomic strategies to better capture genetic diversity in underrepresented populations, a critical step towards improving population genetics studies, personalised medicine, and equitable healthcare.

Xu and colleagues (Xu et al., 2021) recently suggested a new parameterisation of BWA-mem (Li, 2013) as an alternative to the current standard BWA-aln (Li and Durbin, 2009) to process ancient DNA sequencing data. The authors tested several combinations of the -k and -r parameters to optimise BWA-mem ’s performance with degraded and contaminated ancient DNA samples. They report that using BWA-mem with − k 19 − r 2.5 parameters results in a mapping efficiency comparable to BWA-aln with − I 1024 − n 0.03 (i.e. a derivation of the standard parameters used in ancient DNA studies; (Schubert et al., 2012)), while achieving significantly faster run times. We recently performed a systematic benchmark of four mapping software (i.e. BWA-aln , BWA-mem , NovoAlign ( http://www.novocraft.com/products/novoalign ), and Bowtie2 (Langmead and Salzberg, 2012) for ancient DNA sequencing data and quantified their precision, accuracy, specificity, and impact on reference bias (Oliva et al., 2021). Notably, while multiple parameterisations were tested for BWA-aln , NovoAlign , and Bowtie2 , we only tested BWA-mem with default parameters. Here, we use the alignment performance metrics from Oliva et al. to directly compare the recommended BWA-mem parameterisation reported in Xu et al. with the best performing alignment methods determined in the Oliva et al. benchmarks, and we make recommendations based on the results.

Background The application of genomics in translational medicine has gained significant attention as a means to address health disparities. However, pangenomic approaches have been underutilised with most studies relying on the hg38 linear reference. This hinders human evolution studies in fully leveraging whole-genome data. We evaluate the dependability of a well-established variation graph construction algorithm for inferring effective population sizes (Ne) in the Bedouin against hg38, its effect on allele frequencies and potential impact on medical genetics studies, such as GWAS. Our goal is to apply these insights to characterise genetic variation in East African populations. Methods The variation graph was built using 14 Somali individuals from the Human Origins dataset, based on their ancestral affinity to East Africans. Ne was obtained using a coalescent approach ('Relate') for variants detected using the graph-based reference, and compared with the hg38-derived variant calls, for Bedouin individuals from the HGDP. The estimates' quality was evaluated through coalescent simulations. The Wilcoxon Rank Sum Test assessed allele frequency differences between graph-based and linear references, applied both at a genome-wide and exclusively for Bedouin-specific GWAS hits. Results Coalescent analyses revealed that graph-based variants yielded Ne ≈ 17, a 1000-fold decrease from Ne ≈ 79000 for the linear reference. Only the graph-based approach accurately estimated Ne within the 95% CI from simulations (16-19 vs. 324-3468 for hg38). Also, significantly lower rank sum statistics (P-value < 2.2 × 10^-16) were observed for variants from the hg38 reference compared to the graph alignment. Similarly, GWAS variants generated from the graph exhibited lower frequencies (p = 0.023) which could impact the power and interpretation of GWAS and other medical genetics analysis. Conclusion The pangenomic approach, employing variation graphs, more accurately infers Ne in the Bedouin population compared to the hg38 reference. Our findings contribute to the growing evidence demonstrating the impact of these results on genomic medicine as a whole, including GWAS, personalised medicine, and the understanding of genetic variations in diverse populations, which is crucial for equitable healthcare.