A saturated map of common genetic variants associated with human height
Loïc Yengo, Sailaja Vedantam, Eirini Marouli, Julia Sidorenko, Eric Bartell, Saori Sakaue, Marielisa Graff, Anders Eliasen, Yunxuan Jiang, Sridharan Raghavan, Jenkai Miao, Joshua Arias, Sarah E. Graham, Ronen E. Mukamel, Cassandra N. Spracklen, Xianyong Yin, Shyh‐Huei Chen, Teresa Ferreira, Heather H Highland, Yingjie Ji, Tugce Karaderi, Kuang Lin, Kreete Lüll, Deborah E. Malden, Carolina Medina‐Gómez, Moara Machado, Amy Moore, Sina Rüeger, Xueling Sim, Scott Vrieze, Tarunveer S. Ahluwalia, Masato Akiyama, Matthew Allison, Marcus Alvarez, Mette K. Andersen, Alireza Ani, Vivek Appadurai, Liubov Arbeeva, Seema Bhaskar, Lawrence F. Bielak, Sailalitha Bollepalli, Lori L. Bonnycastle, Jette Bork‐Jensen, Jonathan P. Bradfield, Yuki Bradford, Peter S. Braund, Jennifer A. Brody, Kristoffer Sølvsten Burgdorf, Brian E. Cade, Hui Cai, Qiuyin Cai, Archie Campbell, Marisa Cañadas‐Garre, Eulalia Catamo, Jin Fang Chai, Xiaoran Chai, Li-Ching Chang, Yi‐Cheng Chang, Chien-Hsiun Chen, Alessandra Chesi, Seung Hoan Choi, Ren‐Hua Chung, Massimiliano Cocca, Maria Pina Concas, Christian Couture, Gabriel Cuéllar-Partida, Rebecca Danning, E. Warwick Daw, Frauke Degenhard, Graciela E. Delgado, Alessandro Delitala, Ayşe Demirkan, Xuan Deng, Poornima Devineni, Alexander Dietl, Maria Dimitriou, Latchezar Dimitrov, Rajkumar Dorajoo, Arif B. Ekici, Jorgen Engmann, Zammy Fairhurst-Hunter, Aliki‐Eleni Farmaki, Jessica D. Faul, Juan-Carlos Fernandez-Lopez, Lukas Forer, Margherita Francescatto, Sandra Freitag‐Wolf, Christian Fuchsberger, Tessel E. Galesloot, Yan Gao, Zishan Gao, Frank Geller, Olga Giannakopoulou, Franco Giulianini, Anette P. Gjesing, Anuj Goel, Scott D. Gordon, Mathias Gorski, Jakob Grove, Xiuqing Guo
Abstract
Abstract Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.