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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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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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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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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Causal Genetic Inference Using Haplotypes as Instrumental Variables.

Fan Wang1, Nuala J Meyer2, Keith R Walley3

  • 1Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

Genetic Epidemiology
|December 3, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces haplotypes as instrumental variables (IVs) to enhance causal inference in genomic studies. Using haplotypes improves the detection of gene expression

Keywords:
Mendelian randomizationcausal effect estimatehaplotypeinstrumental variable (IV)

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Causal inference in genomic studies uses genetic variants as instrumental variables (IVs) to account for unobserved confounders.
  • A key assumption for valid IV inference is a strong association between the IV and the biomarker (gene/protein expression).
  • Single nucleotide polymorphisms (SNPs) often act as weak IVs, limiting the power of causal effect estimates.

Purpose of the Study:

  • To propose and evaluate the use of haplotypes as instrumental variables (IVs) to strengthen the association with gene/protein expression.
  • To improve the power and accuracy of causal effect inference in genomic studies.
  • To compare the performance of haplotype-based IVs against traditional SNP-based IVs.

Main Methods:

  • Developed a novel haplotype regression model within a two-stage IV framework.
  • Incorporated a model selection procedure to identify optimal haplotype instruments.
  • Evaluated the method's performance through simulations, considering complete and missing genotype data.
  • Compared the proposed method against IV approaches using multiple SNPs.

Main Results:

  • Haplotypes as IVs significantly increased the power to detect causal effects of gene/protein expression on clinical outcomes compared to using SNPs alone.
  • The proposed method demonstrated improved performance under both complete and missing genotype scenarios.
  • The study identified a causal link between interleukin-1 beta (IL-1β) protein expression and sepsis mortality.

Conclusions:

  • Haplotypes serve as more powerful instrumental variables than SNPs for causal inference in genomic studies.
  • The developed method enhances the ability to infer causal relationships between gene/protein expression and clinical outcomes.
  • Overexpression of IL-1β is associated with increased mortality in sepsis patients.