MitoFREQ: A Novel Approach for Mitogenome Frequency Estimation from Top-level Haplogroups and Single Nucleotide Variants

TL;DR

MitoFREQ introduces a practical framework for estimating mitogenome population frequencies using only top-level haplogroups and a single SNV, enabling robust likelihood-ratio based evaluation even for partial or degraded mtDNA profiles. By leveraging TLHG frequency data from HelixMTdb and gnomAD and pooling SNV frequencies within TLHGs, the method computes $\mathrm{LR} = 1 / \big( P(\text{TLHG}) P(X_k \mid \text{TLHG}) \big)$ to express evidential weight, with refinements shown to increase LR under mild assumptions. The approach is validated on high-quality forensic-like datasets and GenBank sequences, yielding LR ranges from $\sim 10^{2}$ to $\sim 10^{5}$ and demonstrated concordance between TLHG inference from full genomes and the 227-position subset. An open-source R package `mitofreq` (with a Shiny app) implements the method and includes TLHG data, supporting broader adoption in forensic mtDNA interpretation. The work highlights how population-resource biases (e.g., underrepresentation of African lineages) and SNV-frequency variability influence LR calculations, and it points to future extensions using more SNVs or graphical models to further refine mitogenome evidence interpretation.

Abstract

Lineage marker population frequencies can serve as one way to express evidential value in forensic genetics. However, for high-quality whole mitochondrial DNA genome sequences (mitogenomes), population data remain limited. In this paper, we offer a new method, MitoFREQ, for estimating the population frequencies of mitogenomes. MitoFREQ uses the mitogenome resources HelixMTdb and gnomAD, harbouring information from 195,983 and 56,406 mitogenomes, respectively. Neither HelixMTdb nor gnomAD can be queried directly for individual mitogenome frequencies, but offers single nucleotide variant (SNV) allele frequencies for each of 30 "top-level" haplogroups (TLHG). We propose using the HelixMTdb and gnomAD resources by classifying a given mitogenome within the TLHG scheme and subsequently using the frequency of its rarest SNV within that TLHG weighted by the TLHG frequency. We show that this method is guaranteed to provide a higher population frequency estimate than if a refined haplogroup and its SNV frequencies were used. Further, we show that top-level haplogrouping can be achieved by using only 227 specific positions for 99.9% of the tested mitogenomes, potentially making the method available for low-quality samples. The method was tested on two types of datasets: high-quality forensic reference datasets and a diverse collection of scrutinised mitogenomes from GenBank. This dual evaluation demonstrated that the approach is robust across both curated forensic data and broader population-level sequences. This method produced likelihood ratios in the range of 100-100,000, demonstrating its potential to strengthen the statistical evaluation of forensic mtDNA evidence. We have developed an open-source R package `mitofreq` that implements our method, including a Shiny app where custom TLHG frequencies can be supplied.

Figures (4)

• Figure 1: An example of subdivisions' effect on the LR.
• Figure 2: Comparison of the SNV frequencies, $p_{\text{Helix}}$ and $p_{\text{gnomAD}}$. Each point represents an alternative allele at a mitogenome position in a TLHG. The Pearson correlation of the $\log_{10}$ transformed SNV frequencies was 0.982. We only considered SNVs observed in both databases.
• Figure 3: Distribution of $\log_{10}(LR)$ values using the smallest one from the rank 1 and rank 2 TLHG prediction and pooled SNV frequencies. The reference $\log_{10}(LR)$s for singletons are from the count estimators in Table \ref{['tbl-Brenner-GGT-LR']}.
• Figure 4: Summary statistics of $LR$ distributions using the smallest one from the rank 1 and rank 2 TLHG prediction. The columns correspond to the datasets, and the rows correspond to the summary statistics.