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関連する概念動画

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.
GWAS does not require the identification of the target gene involved in...
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Polygenic Traits01:18

Polygenic Traits

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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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Multiple Allele Traits01:49

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The Concept of Multiple Allelism
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Pharmacogenomics: Identification of New Drug Targets01:29

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Pleiotropy01:33

Pleiotropy

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Incomplete Dominance01:43

Incomplete Dominance

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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大規模言語モデルが複雑形質GWASにおける因果遺伝子を特定する

Suyash S Shringarpure1, Wei Wang2, Sotiris Karagounis2

  • 123andMe Inc., Palo Alto, CA, USA, suyashss@gmail.com.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
まとめ
この要約は機械生成です。

大規模言語モデル（LLM）は、ゲノムワイド関連解析（GWAS）遺伝子座における因果遺伝子を正確に特定する。これらのモデルは、複雑形質の遺伝的発見を加速するためのスケーラブルで一般化可能なアプローチを提供する。

キーワード:
大規模言語モデルゲノムワイド関連解析因果遺伝子遺伝的発見計算生物学

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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科学分野:

  • 遺伝学
  • バイオインフォマティクス
  • 計算生物学

背景:

  • ゲノムワイド関連解析（GWAS）遺伝子座における因果遺伝子の特定は、複雑な形質を理解するために重要であるが、依然として大きな課題である。
  • 現在の文献マイニング手法は、包括的な遺伝的解析に必要な精度とスケーラビリティを欠いていることが多い。

研究 の 目的:

  • GWAS遺伝子座における可能性のある因果遺伝子の優先順位付けにおける大規模言語モデル（LLM）の効果を評価する。
  • LLMのパフォーマンスを既存の最先端手法と比較し、新しい遺伝子座に対する一般化能力を評価する。

主な方法:

  • 高信頼度の因果遺伝子のベンチマークデータセットを使用した、汎用LLMの体系的な評価。
  • 新しい遺伝子座に対するパフォーマンスをテストするために、23の未発表GWASからの独自のデータセットを含めた。
  • 既存の遺伝的解析手法と統合した場合のLLMのパフォーマンス評価。

主要な成果:

  • LLMは、現在の最先端手法と同等またはそれ以上の精度でGWAS遺伝子座における因果遺伝子の優先順位付けにおいて高い精度を示した。
  • LLMは、新しい遺伝子座においても堅牢なパフォーマンスを示し、強力な一般化能力を示唆した。
  • LLMを既存の手法と統合することで、全体的な因果遺伝子同定パフォーマンスが大幅に向上した。

結論:

  • LLMは、GWASにおける因果遺伝子同定のための、正確でスケーラブルかつ一般化可能なアプローチを提供する。
  • 本研究は、LLMを複雑な形質をunderlieする遺伝子の発見を加速するための強力なツールとして確立する。
  • LLMは、遺伝学的研究のための人工知能の活用における重要な進歩を表す。