• Fan Liu, A Emile J Hendriks, Arwin Ralf, Annemieke M Boot, Emelie Benyi, Lars Sävendahl, Ben A Oostra, Cornelia van Duijn, Albert Hofman, Fernando Rivadeneira, André G Uitterlinden, Stenvert L S Drop, and Manfred Kayser.
• Department of Forensic Molecular Biology, Erasmus MC University Medical Center Rotterdam, P. O. Box 2040, 3000 CA, Rotterdam, The Netherlands, f.liu@erasmusmc.nl.
• Hum. Genet. 2014 May 1; 133 (5): 587-97.

AbstractGenomic 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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