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Dilnoza Muslimova on Muck Rack

Dilnoza Muslimova

As seen in: Nature
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Articles

Overcoming attenuation bias in regressions using polygenic indices - Nature Communications

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By Hans van Kippersluis, Pietro Biroli, Titus J. Galama, Stephanie von Hinke, Dilnoza Muslimova, Ronald de Vlaming
| Nature Verified
Abstract Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating.
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Rank concordance of polygenic indices - Nature Human Behaviour

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By Cornelius A. Rietveld, Dilnoza Muslimova
| Nature Verified
Abstract Polygenic indices (PGIs) are increasingly used to identify individuals at risk of developing disease and are advocated as screening tools for personalized medicine and education. Here we empirically assess rank concordance between PGIs created with different construction methods and discovery samples, focusing on cardiovascular disease and educational attainment.
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