Identifying Y-chromosome haplogroups in arbitrarily large samples of sequenced or genotyped men
Abstract
We have developed an algorithm to rapidly and accurately identify the Y-chromosome haplogroup of each male in a sample of one to millions. The algorithm, implemented in the yHaplo* software package (yHaplo), does not rely on any particular genotyping modality or platform. Full sequences yield the most granular haplogroup classifications, but genotyping arrays can yield reliable calls, provided a reasonable number of phylogenetically informative variants has been assayed. The algorithm is robust to missing data, genotype errors, mutation recurrence, and other complications. We have tested the software on full sequences from phase 3 of the 1000 Genomes Project and on subsets thereof constructed by downsampling to SNPs present on each of four genotyping arrays. We have also run the software on array data from more than 600,000 males.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 130
- Organism
- Assay
- Research Focus
- Approach
In-depth summary
Claude Opus 5.5 · xhigh effort · 17 Oct 2026 · from the full text of version 1 (19 Nov 2016)
yHaplo walks the Y-chromosome tree with an error-tolerant breadth-first search, labelling the 1,244 1000 Genomes males in 20 seconds and matching every gold-standard call.
The semi-automated method the author used to build reliable reference haplogroup calls needs a manual curation step and is described as somewhat onerous for samples of about 1,000 or more. yHaplo automates the assignment with a search designed to tolerate missing genotypes, genotyping errors, recurrent and reversion mutations, and errors in the marker database, and reads both the tree and its defining markers from editable text files so calls can follow later revisions.
Key results
ShownAssignments from sequencing data matched a manually curated reference set exactly across a globally diverse panel.
For each of the 1,244 males in phase 3 of the 1000 Genomes Project (~60,000 single nucleotide variants, 26 populations), the yHaplo call was identical to a gold-standard call built semi-automatically with manual curation and a current version of the ISOGG marker database. · Results
SuggestedGenotyping-array content was enough for correct, though coarser, assignments after the sequence data were thinned to each platform's markers.
The 1000 Genomes data were downsampled to the SNPs present on each of four versions of the 23andMe array and re-called; the author reports every call as accurate but less granular than the sequence-based call. No per-array counts or error rates are given in the text. · Results
ShownHaplogroup calling is fast on the full 1000 Genomes male set, both from the preferred genotype format and directly from the project's own VCF file.
The 1,244 phase 3 male sequences are processed in 20 seconds in the preferred sample-major genotype format, and in under 1.5 minutes directly from the SNP-major VCF hosted by the 1000 Genomes Project. · Discussion
SuggestedThe method scales to a consumer-scale cohort, though validation at that scale was a targeted spot check rather than a full comparison.
yHaplo was run on more than 600,000 males in the 23andMe database; several hundred calls were inspected manually, with emphasis on lineages for which few diagnostic markers had been assayed. No concordance rate is reported for this set. · Results
Methods, data and reproducibility
The tool builds the Y-chromosome phylogeny from a text file of major haplogroups plus ISOGG-curated informative SNPs, naming new branches by Y-Chromosome Consortium convention. A modified breadth-first search then forks or stops each candidate path according to how many ancestral and derived alleles a branch shows, and reports the stopped path carrying the most derived-state SNPs.
- Y-chromosome genotypes from full sequencing, phase 3 of the 1000 Genomes Project, used whole and downsampled to four genotyping-array marker sets · human · n = 1,244 males, ~60,000 single nucleotide variants, 26 populations
- Array genotype data from male customers in the 23andMe database · human · n = more than 600,000 males
- ISOGG database of phylogenetically informative Y-chromosome SNPs, plus a text file encoding the primary tree structure · human · n = 1,757 assignable haplogroup labels at the time of writing
- Methods & tools
- yHaplo, ISOGG database, 23andMe genotyping arrays (four versions), Python
- Experimental conditions
- 1,244 sequences in 20 seconds from sample-major genotypes, under 1.5 minutes from the SNP-major VCF; no hardware stated.
- Reproducibility gaps
- The software is said to be in the 23andMe code repository under a non-commercial, non-exclusive licence, but its address was lost in text extraction. The 1000 Genomes and 23andMe genotype data are named without accessions, and no access route to the 23andMe customer data is given.
Limitations (3)
- Array-based calls are less granular than sequence-based ones and need a reasonable number of informative markers on the platform; where the tree is sparsely covered, the search's tolerance of one ancestral genotype on a branch could mislead it, which a post-processing reversion step is meant to catch. · stated by the authors
- Accuracy is reported qualitatively for the array-downsampled and 600,000-sample runs: no error rates, discordance counts or granularity statistics appear in the text. · noted in this summary
- The only exact comparison is against a gold standard that uses the same ISOGG marker database, so errors shared by both would not show up, and it covers one cohort of 1,244 men. · noted in this summary
Who should read:Read this if you need to assign Y-chromosome haplogroups across a large sequenced or genotyped cohort, or to check which paternal lineages a study sample contains.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- YHP: Y-chromosome Haplogroup Predictor for predicting male lineages based on Y-STRs
Mengyuan Song, Yuxiang Zhou, … Yiping Hou·Forensic Science InternationalPeer-reviewed·Jun 18, 2024
- HaploSight: An alternative approach for Y chromosomal haplogroup identification
Lukkaet Laoprapaipan, Kittichai Phusawang, … Pattarapon Promnun·bioRxiv·Mar 13, 2024
- yallHap: Modern Y-chromosome haplogroup inference with probabilistic scoring and ancient DNA support
Alaina Hardie·bioRxiv·Jan 1, 2026
- SNAPPY: Single Nucleotide Assignment of Phylogenetic Parameters on the Y chromosome
Alissa L. Severson, Jonathan Shortt, … Christopher R. Gignoux·bioRxiv·Oct 29, 2018
- ybyra: Y-chromosome haplogroup calling using a tree-based scoring method
Thomaz Pinotti, Hugh McColl, … Lucas Czech·bioRxiv·Nov 20, 2025
- Generation of high-resolution a priori Y-chromosome phylogenies using “next-generation” sequencing data
Gregory R. Magoon, Raymond H. Banks, … Andrew J. Grierson·bioRxiv·Dec 13, 2013