Biology subjects

Argos, M.

Publications and source records attributed to Argos, M..

5 recordsLinked to original sources

bioRxiv2026

Explaining the unexplained admixture mapping signals via rare variants: the HCHS/SOL

In admixed populations, formed by a mixing of two or more previously isolated populations, genomic segments can be traced to their ancestral populations ("ancestries"). Admixture mapping (AM) associates local ancestry with outcomes in admixed populations, detecting signals when causal variants differ in frequency or effect across ancestral populations. Prior work showed that adjusting for nearby GWAS-identified common variants does not fully explain some AM signals. Here, we assessed two approaches to explain the previously unexplained AM signal: (1) including sets of rare variants; (2) increasing the genomic region considered when searching for common variants. We studied these hypotheses comprehensively using a whole-genome sequencing dataset coupled with metabolomics from the Hispanic Community Health Study/Study of Latinos. We detected multiple sets of rare variants with replicated association with metabolite levels. Yet these rare variants appear to explain only a small fraction of the AM signal, while inclusion of common variants from a larger genomic region appears to explain the majority of the AM signals.

genetics↗

bioRxiv2025

Old vs. New Local Ancestry Inference in HCHS/SOL: A Comparative Study

Hispanic/Latino populations are admixed, with genetic contributions from multiple ancestral populations. Studies of genetic association in these admixed populations often use methods such as admixture mapping, which relies on inferred counts of "local" ancestry, i.e., of the source ancestral population at a locus. Local ancestries are inferred using external reference panels that represent ancestral populations, making the choice of inference method and reference panel critical. This study used a dataset of Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) to evaluate the "old" local ancestry inference performed using the state-of-the-art inference method, RFMix, alongside "new" inferences performed using Fast Local Ancestry Estimation (FLARE), which also used an updated reference panel. We compared their performance in terms of global and local ancestry correlations, as well as admixture mapping-based associations. Overall, the old RFMix and new FLARE inferences were highly similar for both global and local ancestries, with FLARE-inferred datasets yielding admixture mapping results consistent with those computed from RFMix. However, in some genomic regions the old and new local ancestries have relatively lower correlations (Pearson R < 0.9). Most of these genomic regions (86.42%) were mapped to either ENCODE blacklist regions, or to gene clusters, compared to 7.67% of randomly-matched regions with high correlations (Pearson R > 0.97) between old and new local ancestries.

genetics↗

bioRxiv2021

AFA: Computationally efficient Ancestral Frequency estimation in Admixed populations: the Hispanic Community Health Study/Study of Latinos

We developed a computationally efficient method, Ancestral Frequency estimation in Admixed populations (AFA), to estimate the frequencies of bi-allelic variants in admixed populations with an unlimited number of ancestries. AFA uses maximum likelihood estimation by modeling the conditional probability of having an allele given proportions of genetic ancestries. It can be applied using either global or local proportions of genetic ancestries. Simulations mimicking admixture demonstrated the high accuracy of the method. We implemented the method on data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an admixed population with three predominant continental ancestries: Amerindian, European, and African. Comparison of the European and African estimated frequencies to the respective gnomAD frequencies demonstrated high correlations, with Pearson R2=0.97-0.99. We provide a genome-wide dataset of the estimated three ancestral allele frequencies in HCHS/SOL for all available variants with allele frequency between 5%-95% in at least one of the three ancestral populations.

genomics↗

bioRxiv2018

The contribution of parent-to-offspring transmission of telomeres to the heritability of telomere length in humans

Leukocyte telomere length (LTL) is a heritable trait with two potential sources of heritability (h2): inherited variation in non-telomeric regions (e.g., SNPs that influence telomere maintenance) and variability in the lengths of telomeres in gametes that produce offspring zygotes (i.e., \"direct\" inheritance). Prior studies of LTL h2 have not attempted to disentangle these two sources. Here, we use a novel approach for detecting the direct inheritance of telomeres by studying the association between identity-by-descent (IBD) sharing at chromosome ends and phenotypic similarity in LTL. We measured genome-wide SNPs and LTL for a sample of 5,069 Bangladeshi adults with substantial relatedness. For each of the 7,254 relative pairs identified, we used SNPs near the telomeres to estimate the number of chromosome ends shared IBD, a proxy for the number of telomeres shared IBD (Tshared). We then estimated the association between Tshared and the squared pairwise difference in LTL (({Delta}LTL)2) within various classes of relatives (siblings, avuncular, cousins, and distant), adjusting for overall genetic relatedness ({phi}). The association between Tshared and ({Delta}LTL)2 was inverse among all relative pair types. In a meta-analysis including all relative pairs ({phi} >0.05), the association between Tshared and ({Delta}LTL)2 (P=0.002) was stronger than the association between {phi} and ({Delta}LTL)2 (P=0.45). Our results provide strong evidence that telomere length (TL) in parental germ cells impacts TL in offspring cells and contributes to LTL h2 despite telomere \"reprogramming\" during embryonic development. Applying our method to larger studies will enable robust estimation of LTL h2 attributable to direction transmission.

genetics↗

bioRxiv2016

Co-occurring eQTLs and mQTLs: detecting shared causal variants and shared biological mechanisms

Inherited genetic variation impacts local gene expression and DNA methylation in humans. Expression and methylation quantitative trait loci (cis-eQTLs and cis-mQTLs) often occur at the same genomic location, suggesting a common causal variant and shared mechanism. Using DNA and RNA from peripheral blood of Bangladeshi individuals, we use \"co-localization\" methods to identify 3,695 eQTL-mQTL pairs that are likely to share a causal variant. Using partial correlation analysis and mediation analysis, we identify >500 pairs with evidence of a causal relationships between expression and methylation (i.e., shared mechanism) with many additional pairs that we are underpowered to detect. These co-localized pairs are enriched for SNPs showing opposite effects on expression and methylation, although a many affect multiple CpGs in opposite directions. Evidence of shared SNP-age interaction also supports shared mechanisms for two eQTL-mQTL pairs. This work demonstrates the pervasiveness of co-regulated expression and methylation traits in the human genome. This approach can be applied to other types of molecular QTLs to enhance our understanding of regulatory mechanisms.

genomics↗