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Accurate Genomic Prediction Of Human Height

Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Stephen D.H. Hsu
doi: https://doi.org/10.1101/190124
Louis Lello
1Michigan State University, Department of Physics and Astronomy, East Lansing, MI 48824
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Steven G. Avery
1Michigan State University, Department of Physics and Astronomy, East Lansing, MI 48824
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Laurent Tellier
1Michigan State University, Department of Physics and Astronomy, East Lansing, MI 48824
3Cognitive Genomics Laboratory, BGI
5University of Copenhagen, Department of Biology, Functional Genetics, Copenhagen, DK
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Ana I. Vazquez
2Michigan State University, Department of Epidemiology and Biostatistics, East Lansing, MI 48824
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Gustavo de los Campos
2Michigan State University, Department of Epidemiology and Biostatistics, East Lansing, MI 48824
4Michigan State University, Department of Statistics and Probability, East Lansing, MI 48824
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Stephen D.H. Hsu
1Michigan State University, Department of Physics and Astronomy, East Lansing, MI 48824
3Cognitive Genomics Laboratory, BGI
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Abstract

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the “missing heritability” problem – i.e., the gap between prediction R-squared and SNP heritability. The ~20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 18, 2017.
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Accurate Genomic Prediction Of Human Height
Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Stephen D.H. Hsu
bioRxiv 190124; doi: https://doi.org/10.1101/190124
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Accurate Genomic Prediction Of Human Height
Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Stephen D.H. Hsu
bioRxiv 190124; doi: https://doi.org/10.1101/190124

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