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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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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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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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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Infinium Assay for Large-scale SNP Genotyping Applications
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Interpreting SNP heritability in admixed populations.

Jinguo Huang1,2, Nicole Kleman3, Saonli Basu4

  • 1Bioinformatics and Genomics, Huck Institutes of the Life Sciences, Pennsylvania State University.

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Summary

SNP heritability estimates can be biased in admixed populations due to linkage disequilibrium (LD) generated by admixture. This study clarifies these biases and their implications for genetic studies.

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Area of Science:

  • Population Genetics
  • Quantitative Genetics
  • Statistical Genomics

Background:

  • SNP heritability (h²SNP) estimates the proportion of phenotypic variance explained by genotyped SNPs.
  • h²SNP is considered a lower bound of total heritability (h²), but its interpretation is complex, especially with population structure and assortative mating.
  • Population structure can inflate h²SNP estimates due to confounding from linkage disequilibrium (LD) or shared environments.

Purpose of the Study:

  • To investigate biases in SNP heritability estimates in admixed populations using analytical theory and simulations.
  • To clarify the interpretation of h²SNP and its relationship to total heritability (h²) in the context of admixture.
  • To analyze the impact of admixture-generated LD on heritability estimation methods like GREML and Haseman-Elston (HE) regression.

Main Methods:

  • Analytical theory development to model heritability in admixed populations.
  • Simulations to evaluate heritability estimation methods under various genetic architectures and admixture histories.
  • Comparison of Genome-wide restricted maximum likelihood (GREML) and Haseman-Elston (HE) regression biases.

Main Results:

  • Admixture generates LD, contributing to genetic variance and potentially biasing h²SNP estimates even without confounding factors.
  • GREML may under- or over-estimate h²SNP relative to h² depending on the genetic architecture.
  • HE regression can exaggerate the LD contribution, leading to biases in the opposite direction compared to GREML.
  • Both GREML and HE estimates of local ancestry heritability are also biased due to admixture-induced LD.

Conclusions:

  • Admixture-induced LD creates a systematic bias in SNP heritability estimates, affecting their interpretation as a lower bound of total heritability.
  • Understanding and potentially correcting for these biases is crucial for accurate genetic architecture inference in admixed populations.
  • The findings have implications for genome-wide association studies and polygenic prediction in diverse populations.