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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Characteristic 'Pattern of Autofluorescence' of the Fingernails and Certain Skin's Positions is a Novel Diagnostic Biomarker for Acute Ischemic Stroke: A Preliminary Study.

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Updated: Aug 12, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Phenomic Studies on Diseases: Potential and Challenges.

Weihai Ying1,2

  • 11954 Huashan Road, Shanghai, 200030 China Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University.

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|January 30, 2023
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Summary
This summary is machine-generated.

Human phenomics, powered by multi-omics and AI, offers richer data for disease insights than genomics. Challenges remain in data analysis, standardization, and ethical guidelines for advancing precision medicine.

Keywords:
Big dataDiagnosisDiseasesPhenomicsPrecision medicine

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Area of Science:

  • Multi-omics and Artificial Intelligence (AI)
  • Human Phenomics
  • Precision Medicine

Background:

  • Phenomics leverages multi-dimensional human phenome data, enhanced by AI, for disease research.
  • Phenomic data offers richer insights than genomic data, revealing cross-scale correlations and disease mechanisms.
  • Big data-driven phenomic studies promise enhanced discovery efficiency for novel insights.

Purpose of the Study:

  • To highlight the revolutionary potential of phenomics in disease research.
  • To outline the advantages of phenomics over conventional approaches in precision medicine.
  • To identify key challenges and future directions for human phenomic studies.

Main Methods:

  • Analysis of multi-dimensional big data from human phenomes.
  • Integration of multi-omics data with artificial intelligence (AI) approaches.
  • Review of current evidence and challenges in phenomic research.

Main Results:

  • Phenomics provides richer patient data, reveals complex correlations, and enhances discovery efficiency.
  • Significant challenges include developing analytical approaches, data standards, clinical acquisition methods, and ethical guidelines.
  • International cooperation is crucial for advancing phenomic studies.

Conclusions:

  • Phenomics, augmented by AI, presents a transformative strategy for disease risk factor identification, biomarker discovery, and precision therapies.
  • Addressing current challenges in data analysis, standardization, and ethical frameworks is essential for realizing phenomics' full potential.
  • Advancements in phenomic studies are expected to significantly improve disease prevention, diagnosis, and treatment.