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Current approaches for parsing true variation (i.e. signal) from noise, broadly involve estimating a baseline value of the latter, below which all sequence data are ignored. In an effort to deliver a more objective criterion for setting such thresholds, a novel approach based on phylogenetic principles is presented here., Our method deconstructs a special category of noise from true mitochondrial genome data, namely nuclear insertions of mitochondrial DNA (Numts). This bioinformatic approach leverages the relationship of massively parallel sequence reads and is capable of discovering putative Numts (pNumts) in absence of a reference genome. The new method was tested on a whole mitochondrial genome dataset (n = 41 individuals from an admixed population sample from Rio de Janeiro) and led to the discovery of 451 pNumt variants. Comparison of these pNumts haplotypes against an existing Numt database revealed 147 exact matches to previously discovered Numts, while 122 haplotypes differed only by a single base pair and none matched exclusively to the mitochondrial genome. In general, these sequences were considerably more divergent from the mitochondrial genome than from those of the Numt database, supporting that the novel pNumts were probably hitherto uncatalogued variants. Unlike previous techniques, our method appears to be able to detect both polymorphic and fixed Numt sequences. It was also found that the region containing the D-Loop and associated Promoters (DLP) in the human mitochondrial genome, which harbors markers of forensic genetics importance, is the origin of several Numts. Though currently designed for the mitochondrial genome, our novel approach has the potential to be expanded to other scenarios that might require construing signal from noise, including the deconvolution of mixtures, thus significantly improving how analytical thresholds may be established., (Published by Elsevier B.V.)

CYP2D6 is a critical pharmacogenetic target, and polymorphisms in the gene region are commonly used to infer enzyme activity score and predict resulting metabolizer phenotype: poor, intermediate, extensive/normal, or ultrarapid which can be useful in determining cause and/or manner of death in some autopsies. Current genotyping approaches are incapable of identifying novel and/or rare variants, so CYP2D6 star allele definitions are limited to polymorphisms known a priori. While useful for most predictions, recent studies using massively parallel sequencing data have identified additional polymorphisms in CYP2D6 that are predicted to alter enzyme function but are not considered in current star allele nomenclature. The 1000 Genomes Project data were used to produce full-gene haplotypes, describe their distribution in super-populations, and predict enzyme activity scores. Full-gene haplotypes generated lower activity scores than current approaches due to inclusion of additional damaging polymorphisms in the star allele. These findings are critical for clinical implementation of metabolizer phenotype prediction because a fraction of the population may be incorrectly considered normal metabolizers but actually may be poor or intermediate metabolizers.

The short tandem repeat allele identification tool (STRait Razor), a program used to characterize the haplotypes of short tandem repeats (STRs) in massively parallel sequencing (MPS) data, was redesigned. STRait Razor v3.0 performs ∼660× faster allele identification than its previous version (v2s), a speedup that is largely due to a novel indexing strategy used to perform "fuzzy" (approximate) string matching of anchor sequences. Written in a portable compiled language, C++, STRait Razor v3.0 functions on all major operating systems including Microsoft Windows, and it has cross-platform multithreading support. In silico estimates of precision and accuracy of STRait Razor v3.0 were 100% in this evaluation and results were highly concordant with those of Strait Razor v2s. STRait Razor v3.0 adds several key features that simplify the haplotype reporting process, including simple filters to remove low frequency haplotypes as well as merging haplotypes within a locus encoded on opposite strands of the DNA molecule., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Comparisons of whole-genome sequences from ancient and contemporary samples have pointed to several instances of archaic admixture through interbreeding between the ancestors of modern non-Africans and now extinct hominids such as Neanderthals and Denisovans. One implication of these findings is that some adaptive features in contemporary humans may have entered the population via gene flow with archaic forms in Eurasia. Within Africa, fossil evidence suggests that anatomically modern humans (AMH) and various archaic forms coexisted for much of the last 200,000 yr; however, the absence of ancient DNA in Africa has limited our ability to make a direct comparison between archaic and modern human genomes. Here, we use statistical inference based on high coverage whole-genome data (greater than 60×) from contemporary African Pygmy hunter-gatherers as an alternative means to study the evolutionary history of the genus Homo. Using whole-genome simulations that consider demographic histories that include both isolation and gene flow with neighboring farming populations, our inference method rejects the hypothesis that the ancestors of AMH were genetically isolated in Africa, thus providing the first whole genome-level evidence of African archaic admixture. Our inferences also suggest a complex human evolutionary history in Africa, which involves at least a single admixture event from an unknown archaic population into the ancestors of AMH, likely within the last 30,000 yr.

Highlights • Concordance assessment of the Precision ID mtDNA whole genome panel on the Ion S5 and the MiSeq FGx. • Estimation of amplicon haplotypes using read-backed phasing. • Detection and mitigation of the effects of nuclear mitochondrial sequences. Abstract Sequencing whole mitochondrial genomes by capillary electrophoresis is a costly and time/labor-intensive endeavor. Many of the previous Sanger sequencing-based approaches generated amplicons that were several kilobases in length; lengths that are likely not amenable for most forensic applications. However, with the advent of massively parallel sequencing (MPS) short-amplicon multiplexes covering the entire mitochondrial genome can be sequenced relatively easily and rapidly. Recently, the Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific by Applied Biosystems™) has been introduced. This panel is composed of 162 amplicons (in two multiplexes) that are considerably smaller in length (∼163bp) and thus are more amenable to analyzing challenged samples. This panel was evaluated on both the Ion S5™ System (Thermo Fisher Scientific) and the MiSeq™ FGx Desktop Sequencer (Illumina). A script was developed to extract phased haplotypes associated with these amplicons. Levels of read-depth were compared across sequencing pools and between sequencing technologies and haplotype concordances were assessed. Given modest thresholds on read depth, the haplotypes identified by either technology were consistent. Nuclear mitochondrial sequences (Numts) were also inferred, and the effect of different mapping strategies commonly used to filter out Numts were contrasted. Some Numts are co-amplified with this amplification kit, and while the choice of reference sequence can mitigate some of these effects, some data from the mitochondrial genome were lost in the process in this study. This study demonstrates that the Ion and MiSeq platforms provide consistent haplotype estimation of the whole mitochondrial genome, thus providing further support for the reliability and validity of the Precision ID mtDNA Whole Genome Panel. [ABSTRACT FROM AUTHOR]

Forensic genetic investigations typically rely on analysis of DNA for attribution purposes. There are times, however, when the amount and/or the quality of the DNA is limited, and thus little or no information can be obtained regarding the source of the sample. An alternative biochemical target that also contains genetic signatures is protein. One class of genetic signatures is protein polymorphisms that are a direct consequence of simple/single/short nucleotide polymorphisms (SNPs) in DNA. However, to interpret protein polymorphisms in a forensic context, certain complexities must be understood and addressed. These complexities include: 1) SNPs can generate 0, 1, or arbitrarily many polymorphisms in a polypeptide; and 2) as an object of expression that is modulated by alleles, genes and interactions with the environment, proteins may be present or absent in a given sample. To address these issues, a novel approach was taken to generate the expected protein alleles in a reference sample based on whole genome (or exome) sequence data and assess the significance of the evidence using a haplotype-based semi-continuous likelihood algorithm that leverages whole proteome data. Converting the genomic information into the proteomic information allows for the zero-to-many relationship between SNPs and GVPs to be abstracted away. When viewed as a haplotype, many GVPs that correspond to the same SNP is equivalent to many SNPs in perfect linkage disequilibrium (LD). As long as the likelihood formulation correctly accounts for LD, the correspondence between the SNP and the proteome can be safely neglected. Tests were performed on simulated samples, including single-source and two-person mixtures, and the power of using a classical semi-continuous likelihood versus one that has been adapted to neglect drop-out was compared. Additionally, summary statistics and a rudimentary set of decision guidelines were introduced to help identify mixtures from protein data. • A bioinformatic pipeline is described to estimate genetically variable peptide profiles from whole genome sequencing data. • The pipeline is designed to consider either short- or long-read massively parallel sequencing data. • A semicontinuous likelihood that considers linkage and codon degeneracy is introduced. • The likelihood formulation is applied to single-source samples and mixtures. [ABSTRACT FROM AUTHOR]

Short tandem repeats of the nuclear genome have been the preferred markers for analyzing forensic DNA mixtures. However, when nuclear DNA in a sample is degraded or limited, mitochondrial DNA (mtDNA) markers provide a powerful alternative. Though historically considered challenging, the interpretation and analysis of mtDNA mixtures have recently seen renewed interest with the advent of massively parallel sequencing. However, there are only a few software tools available for mtDNA mixture interpretation. To address this gap, the Mitochondrial Mixture Deconvolution and Interpretation Tool (MMDIT) was developed. MMDIT is an interactive application complete with a graphical user interface that allows users to deconvolve mtDNA (whole or partial genomes) mixtures into constituent donor haplotypes and estimate random match probabilities on these resultant haplotypes. In cases where deconvolution might not be feasible, the software allows mixture analysis directly within a binary framework (i.e. qualitatively, only using data on allele presence/absence). This paper explains the functionality of MMDIT, using an example of an in vitro two-person mtDNA mixture with a ratio of 1:4. The uniqueness of MMDIT lies in its ability to resolve mixtures into complete donor haplotypes using a statistical phasing framework before mixture analysis and evaluating statistical weights employing a novel graph algorithm approach. MMDIT is the first available open-source software that can automate mtDNA mixture deconvolution and analysis. The MMDIT web application can be accessed online at https://www.unthsc.edu/mmdit/. The source code is available at https://github.com/SammedMandape/MMDIT%5fUI and archived on zenodo (https://doi.org/10.5281/zenodo.4770184). • Design, development, implementation, and functionality of Mitochondrial Mixture Deconvolution and Interpretation Tool (MMDIT) is presented. • mtDNA mixtures can be deconvolved or analyzed within a binary framework. • Computing statistical weights -> analyzing mixtures without deconvolution. [ABSTRACT FROM AUTHOR]

The scale of genetic methods are presently being expanded: forensic genetic assays previously were limited to tens of loci, but now technologies allow for a transition to forensic genomic approaches that assess thousands to millions of loci. However, there are subtle distinctions between genetic assays and their genomic counterparts (especially in the context of forensics). For instance, forensic genetic approaches tend to describe a locus as a haplotype, be it a microhaplotype or a short tandem repeat with its accompanying flanking information. In contrast, genomic assays tend to provide not haplotypes but sequence variants or differences, variants which in turn describe how the alleles apparently differ from the reference sequence. By the given construction, mitochondrial genetic assays can be thought of as genomic as they often describe genetic differences in a similar way. The mitochondrial genetics literature makes clear that sequence differences, unlike the haplotypes they encode, are not comparable to each other. Different alignment algorithms and different variant calling conventions may cause the same haplotype to be encoded in multiple ways. This ambiguity can affect evidence and reference profile comparisons as well as how "match" statistics are computed. In this study, a graph algorithm is described (and implemented in the MMDIT (Mitochondrial Mixture Database and Interpretation Tool) R package) that permits the assessment of forensic match statistics on mitochondrial DNA mixtures in a way that is invariant to both the variant calling conventions followed and the alignment parameters considered. The algorithm described, given a few modest constraints, can be used to compute the "random man not excluded" statistic or the likelihood ratio. The performance of the approach is assessed in in silico mitochondrial DNA mixtures. [ABSTRACT FROM AUTHOR]

Despite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions. An assortment of external reference panels containing information on haplotypic variation (from similar and different haplogroups) was leveraged to assess the effect of panel composition on phasing accuracy. Building on these simulations, mitochondrial genomes from the Human Mitochondrial DataBase were sourced to populate the panels and key parameter values were identified by deconvolving an additional 7290 in silico two-person mixtures. Finally, employing an optimized reference panel and phasing parameters, the approach was validated with in vitro two-person mixtures with differing proportions. Deconvolution was most accurate when the haplotypes in the mixture were similar to haplotypes present in the reference panel and when the mixture ratios were neither highly imbalanced nor subequal (e.g., 4:1). Overall, errors in haplotype estimation were largely bounded by the accuracy of the mixture's genotype results. The proposed framework is the first available approach that automates the reconstruction of complete individual mitotypes from mixtures, even in ratios that have traditionally been considered problematic. [ABSTRACT FROM AUTHOR]