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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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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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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Updated: May 23, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Data-driven insights to inform splice-altering variant assessment.

Patricia J Sullivan1, Julian M W Quinn2, Pamela Ajuyah3

  • 1Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia; School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia; University of New South Wales Centre for Childhood Cancer Research, UNSW Sydney, Sydney, NSW, Australia.

American Journal of Human Genetics
|March 8, 2025
PubMed
Summary

This study introduces data-driven heuristics to interpret human splice-altering variants (SAVs), improving the understanding of genetic variants affecting mRNA splicing. These evidence-based tools enhance variant evaluation beyond traditional binary predictions.

Keywords:
RNA splicingcancer genomicsclinical genomicsgenomicsmedical genomicsnext-generation sequencingpersonalized medicinesplice-altering variantsvariant classificationwhole-genome sequencing

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Area of Science:

  • Genetics
  • Molecular Biology
  • Bioinformatics

Background:

  • Genetic variants can disrupt mRNA splicing, a complex process.
  • Accurately predicting the impact of variants on splicing, especially outside splice sites, is challenging.

Purpose of the Study:

  • To develop data-driven heuristics for interpreting human splice-altering variants (SAVs).
  • To improve the identification and functional evaluation of SAVs.
  • To bridge the gap between computational predictions and splicing biology.

Main Methods:

  • Analyzed ~202,000 canonical exons and 19,000 validated splicing branchpoints to define splicing criteria.
  • Utilized over 12,000 experimentally validated variants from SpliceVarDB to establish heuristics.
  • Developed a 'spliceogenicity' measure based on variant impact at specific locations or motifs.

Main Results:

  • Defined sequence, spacing, and motif strength criteria met by 95.9% of examined exons.
  • Established heuristics supported by at least 10 validated variants for robust evaluation.
  • Quantified spliceogenicity to assess variant impact in context.

Conclusions:

  • The developed heuristics provide an evidence-based approach for identifying and evaluating SAVs.
  • This method enhances genetic variant evaluation frameworks with detailed, context-aware analysis.
  • Offers a more comprehensive understanding of splicing variant impacts compared to binary prediction tools.