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Localizing and Classifying Adaptive Targets with Trend Filtered Regression.

Mehreen R Mughal1, Michael DeGiorgio2,3

  • 1Bioinformatics and Genomics at the Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA.

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This summary is machine-generated.

Trendsetter identifies genomic regions under natural selection by accounting for correlated data. This new method accurately detects hard and soft sweeps, even with missing data.

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Area of Science:

  • Population genetics
  • Genomics
  • Evolutionary biology

Background:

  • Identifying genomic regions under natural selection is crucial for understanding evolution.
  • Existing methods often overlook the correlation between summary statistics due to linkage disequilibrium.

Purpose of the Study:

  • To introduce Trendsetter, a novel approach for detecting natural selection in genomic data.
  • To account for correlations in summary statistics across genomic locations.

Main Methods:

  • Trendsetter utilizes trend filtering to manage correlated statistics in adjacent genomic regions.
  • A penalized regression framework with regularization addresses multicollinearity.
  • The model learns spatial distributions of summary statistics.

Main Results:

  • Trendsetter demonstrates high power in detecting hard and soft sweeps.
  • The method is robust to missing data and background selection.
  • Analysis of human data identified known selected regions (LCT, EDAR) and novel candidates.

Conclusions:

  • Trendsetter offers a powerful and robust method for identifying genomic regions under selection.
  • The approach effectively models the spatial distribution of population genetic statistics.
  • It provides insights into selection patterns across human populations.