Genome-Wide Association Study of Aging

While human aging is a multifaceted process influenced by many factors and characterized by reduced maintenance of homeostatic mechanisms, age-related diseases, and death, there exists substantial variability in how humans age. Genome-wide association studies (GWASs) have begun to identify aging-related loci using single-phenotype approaches, including extreme longevity, healthspan, and parental lifespan. At CD BioSciences, our team of experts combines their extensive knowledge in genomics with advanced bioinformatics tools to provide GWAS services.

Overview of Our Genome-Wide Association Study Services

At CD BioSciences, we offer a wide range of analysis services to support GWAS in aging. Based on the genotype information of millions of molecular markers (e.g., SNP markers, CNV markers) at the genome-wide level in a given population, we utilize GWAS to analyze the correlation between individual phenotypes and genotypes in populations, thus uncovering the genetic variation affecting complex traits.

Our Workflow of Genome-Wide Association Study

Fig. 1 Technical routes for genome-wide association studies.

Our GWAS Analysis Components

• Raw data filtering
  We begin by filtering and cleaning the raw genomic data to eliminate low-quality or unreliable data, ensuring the accuracy of subsequent analyses.
• Data comparison
  We utilize sophisticated algorithms to compare the genetic data of individuals, identifying common genetic variations that may be associated with aging traits.
• SNP and indel detection
  Single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) are important genetic variations implicated in aging. Our analysis tools accurately detect and annotate these variations, to uncover their potential role in age-related processes.
• Linkage disequilibrium analysis
  Linkage disequilibrium (LD) refers to the nonrandom association of alleles at different loci. Our analytical pipelines assess the distribution and imbalance of chromosomes, shedding light on potential genetic markers associated with aging.
• Population structure analysis
  By employing principal component analysis (PCA), evolutionary tree analysis, and structure analysis techniques, we can determine population substructure and genetic relationships, offering valuable information about the genetic diversity within aging populations.
• Polymorphism analysis
  Polymorphisms, or genetic variations, can impact aging traits. Our analysis examines the presence and distribution of polymorphisms, highlighting their potential relevance in the context of aging.
• Candidate gene extraction and mutation annotation
  Our analysis pipelines identify potential candidate genes associated with aging traits and annotate potential mutations within these genes.
• Phenotypic analysis
  We integrate genetic data with phenotypic information to identify correlations between genetic variations and specific aging-related phenotypic traits.
• Genotype analysis
  We conduct in-depth genotype analysis to assess the genetic variations present and their potential implications for aging.
• GWAS analysis
  Utilizing various statistical models, including simple generalized linear model analysis, generalized linear model analysis, mixed linear model analysis, and K+Q linear model analysis, we perform comprehensive GWAS analysis to identify significant genetic associations with aging traits.

CD BioSciences, with its extensive experience and expertise in genomics analysis, offers a comprehensive range of services to support GWAS in aging research. If you are interested in our services, please feel free to contact us or make an online inquiry.