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Substituents on the benzene ring that direct an incoming electrophile to undergo substitution at the meta position are called meta directors. All meta directors either have a positive charge on the atom directly bonded to the ring or a partial positive charge. These groups function by withdrawing electrons from the ring through inductive and resonance effects. Consider the carbocation intermediates formed upon the addition of an electrophile on nitrobenzene at the...
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All meta-directing substituents are deactivating groups. These substituents withdraw electrons from the aromatic ring, making the ring less reactive toward electrophilic substitution. For example, the nitration of nitrobenzene is 100,000 times slower than that of benzene because of the deactivating effect of the nitro group. The first step in an electrophilic aromatic substitution is the addition of an electrophile to form a resonance-stabilized carbocation. The energy diagrams for...
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test.

Diptavo Dutta1,2, Sarah A Gagliano Taliun1,2, Joshua S Weinstock1,2

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.

Genetic Epidemiology
|August 22, 2019
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Summary
This summary is machine-generated.

Meta-MultiSKAT enhances genetic association studies by meta-analyzing multiple phenotypes. This framework improves detection power for common and rare variants, outperforming single-phenotype methods.

Keywords:
kernel-regressionmeta-analysismultiple-phenotypesrare-variantregion-based

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Joint meta-analysis of multiple correlated phenotypes boosts the power of genetic association studies.
  • Existing methods may not fully leverage multi-phenotype data or account for study-specific heterogeneity.

Purpose of the Study:

  • To develop Meta-MultiSKAT, a novel meta-analysis framework for testing associations between multiple continuous phenotypes and genetic variants using summary statistics.
  • To enhance the power and robustness of genetic association analyses by integrating multiple phenotypes, genetic models, and study heterogeneity.

Main Methods:

  • Developed Meta-MultiSKAT, a framework utilizing summary statistics and a kernel matrix to model phenotype correlations and study heterogeneity.
  • Incorporated genotype kernels for testing both rare and common variants, including their combined effects.
  • Implemented fast and accurate omnibus tests to combine diverse genetic models, functional annotations, and multi-phenotype data.

Main Results:

  • Meta-MultiSKAT maintains exome-wide type-I error rates at 2.5 × 10-6 in simulations.
  • Demonstrated an average improvement in detection power of 23%–38% compared to single-phenotype meta-analysis approaches.
  • Successfully applied Meta-MultiSKAT to analyze white blood cell subtype traits from the Michigan Genomics Initiative and SardiNIA studies.

Conclusions:

  • Meta-MultiSKAT offers a powerful and flexible framework for multi-phenotype genetic association studies.
  • The method effectively handles varying phenotype sets and correlation patterns across studies.
  • Meta-MultiSKAT significantly improves power for detecting genetic associations, particularly for complex traits.