Biology subjects

Cokoglu, S. S.

Publications and source records attributed to Cokoglu, S. S..

3 recordsLinked to original sources

bioRxiv2023

Benchmarking kinship estimation tools for ancient genomes using pedigree simulations

There is growing interest in uncovering genetic kinship patterns in past societies using low-coverage paleogenomes. Here, we benchmark four tools for kinship estimation with such data: lcMLkin, NgsRelate, KIN, and READ, which differ in their input, IBD-estimation methods and statistical approaches. We used pedigree and ancient genome sequence simulations to evaluate these tools when only a limited number (1K to 50K) of shared SNPs (with minor allele frequency [≥]0.01) are available. The performance of all four tools was comparable using [≥]20K SNPs. We found that first-degree related pairs can be accurately classified even with 1K SNPs, with 85% F1 scores using READ and 96% using NgsRelate or lcMLkin. Distinguishing third-degree relatives from unrelated pairs or second-degree relatives was also possible with high accuracy (F1 >90%) with 5K SNPs using NgsRelate and lcMLkin, while READ and KIN showed lower success (69% and 79%, respectively). Meanwhile, noise in population allele frequencies and inbreeding (first cousin mating) led to deviations in kinship coefficients, with different sensitivities across tools. We conclude that using multiple tools in parallel might be an effective approach to achieve robust estimates on ultra-low coverage genomes.

genetics↗

bioRxiv2023

Investigating DNA methylation changes associated with food production using paleogenomes

AO_SCPLOWBSTRACTC_SCPLOWThe Neolithic transition introduced major diet and lifestyle changes to human populations across continents. Beyond well-documented bioarchaeological and genetic effects, whether these changes also had molecular-level epigenetic repercussions in past human populations has been an open question. In fact, methylation signatures can be inferred from UDG-treated ancient DNA through postmortem damage patterns, but with low signal-to-noise ratios; it is thus unclear whether published paleogenomes would provide the necessary resolution to discover systematic effects of lifestyle and diet shifts. To address this we compiled UDG-treated shotgun genomes of 13 pre-Neolithic hunter-gatherer (HGs) and 21 Neolithic farmer (NFs) individuals from West and North Eurasia, published by six different laboratories and with coverage c.1x-58x (median=9x). We used epiPALEOMIX and a Monte Carlo normalization scheme to estimate methylation levels per genome. Our paleomethylome dataset showed expected genome-wide methylation patterns such as CpG island hypomethylation. However, analysing the data using various approaches did not yield any systematic signals for subsistence type, genetic sex, or tissue effects. Comparing the HG-NF methylation differences in our dataset with methylation differences between hunter-gatherers vs. farmers in modern-day Central Africa also did not yield consistent results. Meanwhile, paleomethylome profiles did cluster strongly by their laboratories of origin. Our results mark the importance of minimizing technical noise for capturing subtle biological signals from paleomethylomes.

evolutionary biology↗

bioRxiv2021

CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data

AO_SCPLOWBSTRACTC_SCPLOWTo date, ancient genome analyses have been largely confined to the study of single nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by typical low coverage (<1 x) and short fragments (<80 bps), precluding standard CNV detection software to be effectively applied to ancient genomes. Here we present CONGA, tailored for genotyping CNVs at low coverage. Simulations and down-sampling experiments suggest that CONGA can genotype deletions >1 kbps with F-scores >0.75 at [≥]1x, and distinguish between heterozygous and homozygous states. We applied CONGA to genotype 10,002 outgroup-ascertained deletions across a heterogenous set of 71 ancient human genomes spanning the last 50,000 years, produced using variable experimental protocols. A fraction of these (21/71) display divergent deletion profiles unrelated to their population origin, but attributable to technical factors such as coverage and read length. The majority of the sample (50/71), despite originating from nine different laboratories and having coverages 0.44x-26x (median 4x) and read lengths 52-121 bp (median 69), exhibit coherent deletion frequencies. Across these 50 genomes, inter-individual genetic diversity measured using SNPs and CONGA-genotyped deletions are strongly correlated. CONGA-genotyped deletions also display purifying selection signatures, as expected. CONGA thus paves the way for systematic CNV analyses in ancient genomes, despite the technical challenges posed by low and variable genome coverage.

genomics↗