Applications in Computational Biology

Deleteriousness Prediction

  • Objective: Assess whether a genetic variant, specifically a missense variant (which causes amino acid changes), is deleterious (harmful).

  • Challenges: Tens of thousands of variants may exist in a patient’s genome, necessitating computational tools for prediction.

  • Popular Tools:

    • SIFT, PolyPhen, MutationTaster, GERP, FatHMM, among others, are widely used to predict deleteriousness.

  • Issues with Current Methods:

    • Type 1 Circularity: Benchmark datasets used for both training and testing tools overlap significantly.
    • Type 2 Circularity: Proteins often contain only deleterious or neutral variants, leading to artificially high accuracy via majority vote.

  • Solutions:

    • Cleanly separate training and test datasets to avoid circularity.
    • Stratify datasets by protein membership.

Phenotype Prediction and Epistasis

  • Goal: Predict phenotypic traits (observable characteristics) from an individual's genotype (genetic makeup).

  • Genome-Wide Association Studies (GWAS): Analyze genome-wide genetic variations to find associations with phenotypes.

  • Recent Work:

    • Example from Vilain Lab (UCLA): Claimed that specific methylation patterns in twins could predict sexual orientation with 70% accuracy, but criticisms included small sample size and overfitting.

  • Lessons:

    1. Low sample sizes still hinder predictions of complex traits.
    2. Overfitting must be avoided by building models that generalize well to unseen data.
    3. Correcting for multiple testing is crucial in high-dimensional spaces to avoid false positives.

Epistasis (Gene-Gene Interactions)

  • Definition: Epistasis refers to the interaction between genes where the effect of one gene is modified by one or more other genes.

  • Types:

    • Bateson's Masking Model: One gene masks the effect of another gene.
    • General Epistasis: More complex interactions between two loci.

  • Models for Epistasis:

    1. Multiplicative Interaction: Odds increase multiplicatively with certain genotypes.
    2. Threshold Model: Interaction only manifests when both loci contain disease-associated alleles.

  • Applications: Epistasis is often cited as one explanation for the missing heritability of complex traits, such as breast cancer, where gene interactions affect disease risk.

Bottlenecks in Two-Locus Mapping

  • Scale: The large number of single nucleotide polymorphisms (SNPs), typically \( 10^5 - 10^7 \), leads to considering an enormous number of SNP pairs (~\( 10^{10} - 10^{14} \)).

  • Challenges:

    • Multiple hypothesis testing.
    • Long computational runtimes.

  • Approaches:

    • Exhaustive Enumeration: Requires specialized hardware like GPUs.
    • Filtering Methods: Prioritize SNPs based on statistical criteria (e.g., large main effects) or biological criteria (e.g., protein-protein interactions).

Conclusion and Future Directions

  • Data mining techniques in computational biology have advanced significantly, providing methods for predicting deleteriousness, phenotypic traits, and uncovering gene interactions.
  • Major challenges still include avoiding overfitting, managing small sample sizes, and handling the computational burden of analyzing vast genetic datasets.