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Related Concept Videos

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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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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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci.

Hanfei Xu1, Shreyash Gupta2, Ian Dinsmore3

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This study integrates genetic and gene expression data to uncover biological mechanisms behind body mass index (BMI) risk loci. Researchers identified seven key genes, including SNAPC3 and YPEL3, linking genetic variations to obesity pathways.

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Area of Science:

  • Genetics and Genomics
  • Metabolic Disease Research
  • Systems Biology

Background:

  • Genome-wide association studies (GWAS) have identified numerous genetic loci associated with body mass index (BMI).
  • However, the biological mechanisms connecting these risk loci to BMI regulation remain largely unknown.
  • Integrative omics analyses offer a powerful approach to elucidate these complex biological pathways.

Purpose of the Study:

  • To identify genes and biological pathways that link genetic variations at BMI risk loci to BMI.
  • To integrate genotype and gene expression data for a comprehensive understanding of BMI regulation.
  • To translate findings from genetic associations to functional biological insights.

Main Methods:

  • Analysis of genotype and blood gene expression data from the Framingham Heart Study (FHS) in up to 5,619 samples.
  • Association analyses of single nucleotide polymorphisms (SNPs) with BMI (PBMI) and with transcript levels (PSNP).
  • Correlated meta-analysis (PMETA) of SNP and transcript data, followed by Bonferroni correction and validation in independent datasets and specific brain and liver tissues.

Main Results:

  • Seven candidate genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, ZNF646) were identified in five BMI-associated regions.
  • Results for SNAPC3 and YPEL3 were validated in independent blood gene expression data.
  • Significant associations were observed for YPEL3 in the nucleus accumbens and for NT5C2, SNAPC3, TMEM245, YPEL3, and ZNF646 in the liver.

Conclusions:

  • The identified genes provide crucial links between genetic variations at obesity risk loci and underlying biological mechanisms.
  • These findings contribute to translating GWAS discoveries into functional understanding of BMI regulation.
  • The study highlights the utility of integrative omics approaches for dissecting complex traits like obesity.