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We developed Regenie.QRS, a new method for genome-wide association studies (GWAS) that detects how genetic effects vary across the full phenotype distribution. This approach improves the detection of complex genotype-phenotype associations.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Genotype-phenotype associations are often dynamic and context-dependent, leading to heterogeneity across the phenotype distribution.
  • Linear regression may not fully capture these complex genetic effects, necessitating advanced analytical methods.

Purpose of the Study:

  • To introduce Regenie.QRS, a computationally efficient whole-genome quantile regression method for biobank-scale genome-wide association studies (GWAS).
  • To detect and characterize heterogeneous genotype-phenotype associations more effectively than traditional linear regression.

Main Methods:

  • Developed Regenie.QRS, a novel technique integrating polygenic effect estimation with non-mixed quantile regression.
  • Incorporated estimated polygenic effects as an offset within the quantile regression model for enhanced sensitivity.
  • Validated the method through simulations and applications on large-scale datasets like the UK Biobank and ProgeNIA/SardiNIA.

Main Results:

  • Regenie.QRS demonstrated robust control of type I error and superior power in detecting heterogeneous genetic associations compared to linear regression.
  • The method showed improved power over marginal quantile regression tests.
  • Identified context-dependent genetic effects, such as variants at the G6PC2 locus influencing glucose levels differently across the distribution.

Conclusions:

  • Regenie.QRS is an effective tool for identifying and characterizing heterogeneous genotype-phenotype associations in large-scale GWAS.
  • The findings highlight the importance of considering the entire phenotype distribution for a comprehensive understanding of genetic effects.
  • The approach has broad applicability in human genetics, plant breeding, and animal conservation studies.