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Related Concept Videos

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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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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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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 genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Prioritizing effector genes at trait-associated loci using multimodal evidence.

Marijn Schipper1, Christiaan A de Leeuw2, Bernardo A P C Maciel2

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Predicting disease-causing genes from genetic studies is hard. The FLAMES framework uses machine learning and network analysis to identify the most likely effector genes, improving accuracy for complex traits and diseases.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) identify numerous genetic loci linked to traits and diseases.
  • Pinpointing the specific effector genes responsible for these associations remains a significant challenge in genetic research.

Purpose of the Study:

  • To introduce the FLAMES (fine-mapped locus assessment model of effector genes) framework for predicting the most likely effector gene within a genetic locus.
  • To enhance the accuracy of effector gene prediction by integrating multiple lines of evidence.

Main Methods:

  • FLAMES employs machine learning models trained on biological data that link single-nucleotide polymorphisms (SNPs) to genes.
  • It evaluates SNP-based predictions alongside gene-centric evidence of GWAS signal convergence within functional networks.

Main Results:

  • The study demonstrates that FLAMES, by combining SNP-based and network convergence data, outperforms prioritization methods relying on a single evidence type.
  • FLAMES successfully resolved the FSHB locus associated with dizygotic twinning and identified schizophrenia risk genes with cross-life-stage relevance.

Conclusions:

  • The FLAMES framework provides a robust and accurate method for identifying effector genes from GWAS data.
  • Integrating diverse data modalities significantly improves the resolution of locus-trait associations, advancing our understanding of genetic contributions to disease.