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"Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models"

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If you have a question about this talk, please contact Alison Quenault .

Abstract: Genome-wide association studies (GWAS) have identified thousands of genomic variants that are significantly associated with complex phenotypic traits. Yet, for most complex traits, the translation of GWAS findings into personalised medicine applications remains limited, as the accuracy achieved by genomic prediction models is usually inadequate for clinical practice. In this talk, I will describe our work on genomic prediction of complex traits, where we empirically compared several widely used prediction models, including Ridge Regression and LASSO estimated from cohort data, and polygenic risk scores based on summary statistics from large meta-analyses of GWAS . I will discuss how prediction accuracy changes with respect to relatedness in the study cohorts and genetic architecture of the trait, and show how we can combine genomic predictors to increase accuracy. I will conclude with what I think are promising directions for future research in the area of genomic prediction.

This talk is part of the MRC Biostatistics Unit Seminars series.

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