bioRxiv Science⌕ Search

bioRxiv · 10.1101/402792

Association of SRB1, ITGB2 gene polymorphisms with coronary heart disease in Chinese Han population

Abstract

BackgroundPrevious studies in mice and humans have implicated the lipoprotein receptor SRB1 in association with atherosclerosis and lipid levels. In our previous proteomics research, the expression of ITGB2 has differences between epicardial and subcutaneous adipose tissue. However, the association between the reported variants and risk of coronary heart disease (CHD) was not confirmed.\n\nMethodsWe conducted a case-control study consisted of 496 CHD patients and 367 controls. The two groups are adjusted for age, sex, body mass index, diabetes status and the proportion of dyslipidemia. The genotypes and allele frequency of variants rs838880,rs5888,rs5889 in SRB1 and rs235326,rs2070947,rs2070946 in ITGB2 were determined using Sequenom Mass-ARRAY technology.\n\nResultsThe genotypes frequencies of all the six SNPs were consistent with Hardy-Weinberg Equilibrium test. For gene SRB1 rs838880, there was a significant difference in the alleles frequency(p=0.017), genotype frequency(p=0.0028), recessive model (p=0.000672) between CHD group and control group. For gene ITGB2 rs2070947, there was a significant difference in the recessive model (p=0.03). By comparing the clinical and serum metabolic indexes of SNP sites by genotype we find that among three genotypes of SRB1 rs5888,there were significant difference in the level of dyslipidemia history and serum LPA, among three genotypes of ITGB2 rs235236,there were significant difference in the levels of serum HDL,APOA1 and hypertension history, among three genotypes of ITGB2 rs2070947,there were significant difference in the level of serum APOA1,hsCRP.\n\nConclusionsOur findings indicated that SNP rs838880 of gene SRB1 and rs2070947 of gene ITGB2 are associated with the risk of CHD in Chinese han population.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sun, Y., Wang, L. T., Zeng, Y.. 2018-08-29. Association of SRB1, ITGB2 gene polymorphisms with coronary heart disease in Chinese Han population. https://doi.org/10.1101/402792

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Cellular context shapes the transcriptomic impact of somatic variants

Somatic SNVs are commonly prioritized by recurrence or sequence annotation, leaving the molecular consequences of private variants unresolved. We developed PrismSNV and Ligo-Seq, a single-cell full-length transcriptome sequencing platform, to quantify transcriptomic displacement associated with within-cell SNV-state toggling. Across 26 samples from three cancer types, more than 60% of candidate SNVs showed significant cell-type-specific displacement with over 30% variation in magnitude. Sequence-based deleteriousness was poorly aligned with cellular displacement, and fewer than 3% of top-displacement SNVs across six tumor types were catalogued in ClinVar. Displacement was strongest in transcriptionally plastic, less differentiated states. Analysis of 655,349 CD4+ T-cell transcriptomes further showed that SNV-gene associations changed in magnitude and sometimes direction during activation. Lack of recurrence or clinical annotation does not imply transcriptomic inertness; variant impact is shaped by cellular context rather than sequence alone, establishing the variant-cell pair as a critical unit for interpreting somatic variation.

genetics↗

Genetic and Epigenetic Landscape of Self-Identified Hispanics/Latino in All Of Us

Human genetic studies still underrepresent many populations, limiting discovery and interpretation of medically relevant variation. Extensive admixture in self-identified Hispanic/Latino populations increases the need for representative reference data and ancestry-aware interpretation of medically relevant variation. We used long-read sequencing to analyze genetic variation, ancestry, and DNA methylation in 1,490 self-identified Hispanic/Latino participants from the All of Us Research Program. We identified 10 million novel variants and more than 100 candidate phenotype associations, with fine-mapping prioritizing structural variants over nearby single-nucleotide variants in more than half. We linked 3,329 tandem repeats to DNA methylation, resolved widespread allele-specific methylation, and identified ancestry-linked regulation across paralogous genes. These results reveal previously inaccessible genetic and regulatory variation and show the value of long-read sequencing for diverse populations.

genetics↗

The genetic architecture of human DNA methylome variation

Population DNA methylome analyses remain largely confined to reference-defined genomic space despite extensive human genetic diversity. A central challenge is therefore to define comparable methylation phenotypes across diverse genomes and map the genetic architecture of methylome variation. Here, using 460 haplotypes from the Human Pangenome Reference Consortium Release 2, we develop two complementary pangenome-based frameworks: one compares methylation within homologous regions while retaining haplotype-specific structural configuration and CpG content; the other represents CpG island (CGI) variation at homologous loci as sequence-resolved alleles. We identified 47,039 CGI alleles, including 8,040 absent from CHM13; 62% were attributable to structural variants and 31% to small variants. Through local genealogies, we reconstructed CGI gain, loss and turnover, revealing how present-day methylation differences align with CGI evolutionary trajectories. We found that genome-wide, methylation variability was buffered against genetic diversity across more than 90% of the genome, with exceptions at extreme nucleotide diversity and locally around structural variants. Our methylation framework enabled population-scale allele-aware methylation quantitative trait locus (mQTL) mapping into non-reference and structurally complex sequence, where mQTL-associated intervals were enriched. mQTLs linked methylation to transcriptomic phenotypes, population differentiation and complex traits, supporting 8% of population differentially methylated regions and colocalizing with more than half of genome-wide association study loci. Together, our study redefines human methylome analysis and our findings reveal a genome-wide architecture of genetic-epigenetic coupling, reshaping how methylation variation is interpreted across human development and disease.

genetics↗