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Common DNA variants predict tall stature in Europeans

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Abstract

Genomic prediction of the extreme forms of adult body height or stature is of practical relevance in several areas such as pediatric endocrinology and forensic investigations. Here, we examine 770 extremely tall cases and 9,591 normal height controls in a population-based Dutch European sample to evaluate the capability of known height-associated DNA variants in predicting tall stature. Among the 180 normal height-associated single nucleotide polymorphisms (SNPs) previously reported by the Genetic Investigation of ANthropocentric Traits (GIANT) genome-wide association study on normal stature, in our data 166 (92.2 %) showed directionally consistent effects and 75 (41.7 %) showed nominally significant association with tall stature, indicating that the 180 GIANT SNPs are informative for tall stature in our Dutch sample. A prediction analysis based on the weighted allele sums method demonstrated a substantially improved potential for predicting tall stature (AUC = 0.75; 95 % CI 0.72–0.79) compared to a previous attempt using 54 height-associated SNPs (AUC = 0.65). The achieved accuracy is approaching practical relevance such as in pediatrics and forensics. Furthermore, a reanalysis of all SNPs at the 180 GIANT loci in our data identified novel secondary association signals for extreme tall stature at TGFB2 (P = 1.8 × 10−13) and PCSK5 (P = 7.8 × 10−11) suggesting the existence of allelic heterogeneity and underlining the importance of fine analysis of already discovered loci. Extrapolating from our results suggests that the genomic prediction of at least the extreme forms of common complex traits in humans including common diseases are likely to be informative if large numbers of trait-associated common DNA variants are available.

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Acknowledgments

The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters for their help in generating the GWAS database, as well as Karol Estrada and Maksim V. Struchalin for their support in generating and analyzing imputed genomic data. This study was supported in part by funding from the Netherlands Forensic Institute (NFI), by a grant from the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) within the framework of the Forensic Genomics Consortium Netherlands (FGCN), the Swedish Research Council (K2010-54X-15073-07-3), and by Ferring Pharmaceuticals, Eli Lilly and Company, and Ace Pharmaceuticals. The generation and management of GWAS genotype data for the Rotterdam Study are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research (NWO) project nr. 050-060-810. The Rotterdam Study is funded by the Erasmus MC University Medical Center Rotterdam, the Erasmus University Rotterdam, the Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science of the Netherlands, the Ministry for Health, Welfare and Sports of the Netherlands, the European Commission (DG XII), the Municipality of Rotterdam and the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) within the framework of the Netherlands Consortium on Healthy Ageing (NCHA). None of the funding agencies had influenced the design, execution or results of this study.

Conflict of interest

SLS Drop has received research grants from Ace, Ferring and Eli Lilly. All other authors declare no conflicts of interest.

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Authors and Affiliations

  1. Department of Forensic Molecular Biology, Erasmus MC University Medical Center Rotterdam, P. O. Box 2040, 3000 CA, Rotterdam, The Netherlands

    Fan Liu, Arwin Ralf & Manfred Kayser

  2. Division of Endocrinology, Department of Pediatrics, Sophia Children’s Hospital, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

    A. Emile J. Hendriks & Stenvert L. S. Drop

  3. Division of Endocrinology, Department of Pediatrics, University Medical Center Groningen, Groningen, The Netherlands

    Annemieke M. Boot

  4. Department of Women’s and Children’s Health, Karolinska Institute, Stockholm, Sweden

    Emelie Benyi & Lars Sävendahl

  5. Department of Clinical Genetics, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

    Ben A. Oostra

  6. Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

    Cornelia van Duijn, Albert Hofman, Fernando Rivadeneira & André G. Uitterlinden

  7. Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

    Fernando Rivadeneira & André G. Uitterlinden

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  1. Fan Liu
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439_2013_1394_MOESM3_ESM.tif

Supplementary Fig. 1a Association result of the case–control designed GWAS for tall stature in 770 tall and 9,591 non-tall Dutch Europeans. A, Manhattan plot. (TIFF 1321 kb)

Supplementary Fig. 1b B, QQ plot. (TIFF 1321 kb)

439_2013_1394_MOESM5_ESM.tif

Supplementary Fig. 2. Performance of six classifiers in predicting tall stature from DNA variants. Box plots of AUC values were generated for weighted allele sums (WAS); least absolute shrinkage and selection operator (LASSO); support vector machine (SVM), neuron networks (NN); classification and regression trees (CART); and random forest (RF). For each method, 1,000 AUC values were derived from cross-validations using randomly split training–testing samples (80 % - 20 %). Summary statistics in the box plots include median (black line), 25-75 % quartiles (grey box), minimum and maximum (dashed interval), and outliers (dots). A. using randomly selected 180 SNPs over the genome. B. using the 180 SNPs from the GIANT paper (Lango Allen et al. 2010). Note that this analysis included 20 % of controls and all cases because the computational burden of SVM scales with sample size. (TIFF 714 kb)

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Liu, F., Hendriks, A.E.J., Ralf, A. et al. Common DNA variants predict tall stature in Europeans. Hum Genet 133, 587–597 (2014). https://doi.org/10.1007/s00439-013-1394-0

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