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Principles of Pharmacogenetics: Types of Genetic Variants01:27

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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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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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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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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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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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Precision Grounding: augmenting large language models with evidence-based databases for trustworthy genetic variant

Xinsong Du1, Anna Nagy2, Michael F Oates3

  • 1Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, United States; Department of Biomedical Informatics, Harvard Medical School, Boston, United States.

International Journal of Medical Informatics
|April 8, 2026
PubMed
Summary

Precision Grounding, a novel method, enhances large language models (LLMs) for genetic variant summarization. This approach significantly improves accuracy and reduces clinical errors, supporting better decision-making.

Keywords:
Clinical decision supportGenomicsHallucinationLarge language modelsRetrieval augmented generationSummarization

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Area of Science:

  • Genomics
  • Artificial Intelligence
  • Clinical Decision Support

Background:

  • Large language models (LLMs) show promise in summarizing complex genetic variant information.
  • Current methods often lack the precision required for clinical decision-making due to insufficient evidence grounding.
  • Accurate interpretation of genetic variants is crucial for diagnosing and treating diseases.

Purpose of the Study:

  • To introduce Precision Grounding, a novel method for augmenting LLMs with variant-specific evidence.
  • To enhance the accuracy and clinical utility of LLM-generated summaries for genetic variants.
  • To reduce hallucinations and improve the reliability of LLM outputs in a clinical context.

Main Methods:

  • Proposed Precision Grounding, a query tool integrating expert-selected resources for factual information retrieval.
  • Developed CATT, an open-source tool that queries ClinGen, ClinVar, and GenCC databases using Variation IDs.
  • Compared Precision Grounding against web-search grounding and retrieval-augmented generation (MedRAG) using 50 expert-selected variants and GPT-4o.

Main Results:

  • Precision Grounding significantly outperformed web-search grounding and MedRAG in accuracy (4.76/5) and completeness (4.94/5).
  • MedRAG struggled with clinically relevant information, while web-search grounding was less effective than Precision Grounding.
  • Precision Grounding substantially reduced clinically significant hallucinations, including incorrect pathogenicity classifications and variant misinterpretations.

Conclusions:

  • Precision Grounding offers a superior approach to grounding LLMs for genetic variant summarization.
  • The open-source CATT tool facilitates the integration of curated genetic knowledge, minimizing LLM hallucinations.
  • This framework enhances variant interpretation accuracy and completeness, showing strong potential for clinical decision-support systems and genomics workflows.