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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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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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    Area of Science:

    • Genetics
    • Statistical Genetics
    • Bioinformatics

    Background:

    • Quantitative trait loci (QTL) mapping is crucial for understanding the genetic basis of complex phenotypes.
    • Accurate estimation of additive, dominance, and epistatic effects is challenging.
    • Existing methods for multiple QTL mapping and model selection have limitations.

    Purpose of the Study:

    • To propose a novel procedure for phenotype modeling using QTLs.
    • To implement a Bayesian approach for estimating genetic effects and performing model selection.
    • To identify main and epistatic QTLs for systolic blood pressure.

    Main Methods:

    • A data-driven reversible jump (DDRJ) Bayesian approach for multiple QTL mapping and model selection.
    • Comparison of DDRJ with standard reversible jump (RJ), QTLBim, multiple interval mapping (MIM), and LASSO.
    • Application to real and simulated data sets.

    Main Results:

    • The DDRJ method accurately estimates the number and genomic locations of QTLs in epistatic models.
    • DDRJ outperforms existing methods in identifying relevant QTLs without increasing false positives.
    • The proposed model selection procedure is robust for complex regression models.

    Conclusions:

    • The DDRJ approach provides a superior method for QTL mapping and model selection.
    • This technique enhances the understanding of genetic architectures for complex traits.
    • The methodology has broader applicability in statistical genetics and other research areas.