Biology subjects

Qin, Z. S.

Publications and source records attributed to Qin, Z. S..

3 recordsLinked to original sources

bioRxiv2025

Early-life nutrition supplementation and epigenetic age in middle-adulthood among Guatemalan adults

ObjectivesEpigenetic clocks are biomarkers of aging. Epigenetic clocks are associated with early-life famine exposure. We investigated the impact of a cluster-randomized early-life nutrition intervention on epigenetic age. MethodsWe analyzed follow-up data from participants in the INCAP Nutrition Supplementation Trial, conducted in 4 villages in eastern Guatemala. DNA methylation was measured in buffy coat samples using the Illumina InfiniumTM MethylationEPICv2.0 array and standard quality control procedures. Epigenetic age was quantified using DunedinPACE, PhenoAge, and GrimAge. PhenoAge and GrimAge acceleration were calculated as residuals by regressing epigenetic age on chronological age. We used intent-to-treat difference-in-difference modeling to assess the impact of a protein-energy supplement provided during the first 1,000 days of life (conception to age 2y) on epigenetic age in middle adulthood. Covariates included sex, birth year, the trial supplement type (atole [intervention] vs. fresco [control]), exposure period of supplement (any of the first 1,000 days, other), and a random effect to account for sibships. The primary coefficient of interest was represented by the interaction between supplement type and exposure period. ResultsThe analysis included 1095 participants (mean age 45.0 y (SD 4.3); 60.3 % female, 40.3 % exposed to any atole during the first 1,000 days, mean DunedinPACE 1.2 (SD 0.1), Phenoage 46.7 y (SD 6.7), and GrimAge 56.3 y (SD 4.1). In difference-in-difference analyses, exposure to atole during any of the first 1,000-day period was associated with lower DunedinPACE (- 0.03, 95% CI -0.06, -0.004), PhenoAge acceleration (- 1.91 y, 95% CI -3.43, -0.39), and GrimAge acceleration (-0.85 y, 95% CI -1.53, -0.11) compared to other exposures. Following additional adjustment for cell type proportions, the direction of the coefficients remained the same but were no longer statistically significant. ConclusionsExposure to atole during the first 1,000 days was associated with modest reductions in epigenetic age as measured by DunedinPACE, PhenoAge, and GrimAge. These findings complement prior evidence of epigenetic age acceleration among individuals with early-life famine exposure.

genomics↗

bioRxiv2025

A novel machine learning-based algorithm for eQTL identification reveals complex pleiotropic effects in the MHC region

Expression quantitative trait loci (eQTLs) are regulatory variants that affect the expression level of their target genes and have significant impact on disease biology. However, eQTL mapping has been done mostly in one tissue at a time, despite the known prevalence of correlations among tissues. Multivariate analyses incorporating multiple phenotypes are available, but they emphasize linear combinations of phenotypes. We present MTClass, a machine learning framework that attempts to classify an individuals genotype based on a vector of multi-phenotype expression levels of a given gene. We conduct simulation studies and multiple case studies using real and imputed data, and we demonstrate that MTClass detects more functionally relevant variants and genes compared to existing single-tissue approaches as well as multi-phenotype association tests. Our results suggest that the importance of expression regulation at the MHC region may have been underestimated, and they provide fresh biological insights into genetic variants that have pleiotropic effects, influencing gene expression in a complex manner. Key pointsO_LIMTClass is a machine learning-based approach that classifies genotypes based on multi-phenotype expression data, providing a novel method for identifying eQTLs. C_LIO_LIMTClass outperforms traditional linear methods like MultiPhen and MANOVA in detecting eQTLs with greater functional impact and in capturing complex genotype-phenotype relationships. C_LIO_LIMTClass identified immune-related variants in the HLA region, suggesting that existing approaches may have underestimated the complexity of these variants effects across tissues. C_LIO_LIMTClass is more flexible and reliable than linear multivariate methods, handling multicollinearity, zero-expressed features, and various input values with greater ease. C_LI

bioinformatics↗

bioRxiv2017

Identifying tagging SNPs for African specific genetic variation from the African Diaspora Genome

A primary goal of The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to develop an African Diaspora Power Chip (ADPC), a genotyping array consisting of tagging SNPs, useful in comprehensively identifying African specific genetic variation. This array is designed based on the novel variation identified in 642 CAAPA samples of African ancestry with high coverage whole genome sequence data (~30x depth). This novel variation extends the pattern of variation catalogued in the 1000 Genomes and Exome Sequencing Projects to a spectrum of populations representing the wide range of West African genomic diversity. These individuals from CAAPA also comprise a large swath of the African Diaspora population and incorporate historical genetic diversity covering nearly the entire Atlantic coast of the Americas. Here we show the results of designing and producing such a microchip array. This novel array covers African specific variation far better than other commercially available arrays, and will enable better GWAS analyses for researchers with individuals of African descent in their study populations. A recent study1 cataloging variation in continental African populations suggests this type of African-specific genotyping array is both necessary and valuable for facilitating large-scale GWAS in populations of African ancestry.

genomics↗