NCD Risk Factor Collaboration (NCD-RisC). A century of trends in adult human height. eLife 5, e13410 (2016).
Homburger, J. R. et al. Genomic insights into the ancestry and demographic history of South America. PLoS Genet. 11, e1005602 (2015).
Harris, D. N. et al. Evolutionary genomic dynamics of Peruvians before, during, and after the Inca Empire. Proc. Natl Acad. Sci. USA 115, E6526–E6535 (2018).
Ruiz-Linares, A. et al. Admixture in Latin America: geographic structure, phenotypic diversity and self-perception of ancestry based on 7,342 individuals. PLoS Genet. 10, e1004572 (2014).
Browning, B. L. & Browning, S. R. Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194, 459–471 (2013).
Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).
Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).
Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).
Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).
Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359, 424–428 (2018).
Domingue, B. W. et al. The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health. Proc. Natl Acad. Sci. USA 115, 702–707 (2018).
Rask-Andersen, M., Karlsson, T., Ek, W. E. & Johansson, Å. Gene–environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status. PLoS Genet. 13, e1006977 (2017).
Pelova, N. Considerations on the so-called myelolipoma of the adrenals. Nauchni Tr. Vissh. Med. Inst. Sofiia 48, 31–35 (1969).
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Voight, B. F., Kudaravalli, S., Wen, X. & Pritchard, J. K. A map of recent positive selection in the human genome. PLoS Biol. 4, e72 (2006).
Johnson, K. E. & Voight, B. F. Patterns of shared signatures of recent positive selection across human populations. Nat. Ecol. Evol. 2, 713–720 (2018).
Akbari, A. et al. Identifying the favored mutation in a positive selective sweep. Nat. Methods 15, 279–282 (2018).
Sabeti, P. C. et al. Detecting recent positive selection in the human genome from haplotype structure. Nature 419, 832–837 (2002).
Nei, M. & Li, W. H. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl Acad. Sci. USA 76, 5269–5273 (1979).
Arbiza, L., Zhong, E. & Keinan, A. NRE: a tool for exploring neutral loci in the human genome. BMC Bioinformatics 13, 301 (2012).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Albers, P. K. & McVean, G. Dating genomic variants and shared ancestry in population-scale sequencing data. PLoS Biol. 18, e3000586 (2020).
Lamason, R. L. et al. SLC24A5, a putative cation exchanger, affects pigmentation in zebrafish and humans. Science 310, 1782–1786 (2005).
Fan, S., Hansen, M. E. B., Lo, Y. & Tishkoff, S. A. Going global by adapting local: a review of recent human adaptation. Science 354, 54–59 (2016).
Adhikari, K. et al. A GWAS in Latin Americans highlights the convergent evolution of lighter skin pigmentation in Eurasia. Nat. Commun. 10, 358 (2019).
Sturm, R. A. & Duffy, D. L. Human pigmentation genes under environmental selection. Genome Biol. 13, 248 (2012).
Günther, T. & Coop, G. Robust identification of local adaptation from allele frequencies. Genetics 195, 205–220 (2013).
Lasker, G. W. Differences in anthropometric measurements within and between three communities in Peru. Hum. Biol. 34, 63–70 (1962).
Sengle, G. & Sakai, L. Y. The fibrillin microfibril scaffold: a niche for growth factors and mechanosensation? Matrix Biol. 47, 3–12 (2015).
Schrenk, S., Cenzi, C., Bertalot, T., Conconi, M. T. & Di Liddo, R. Structural and functional failure of fibrillin-1 in human diseases (review). Int. J. Mol. Med. 41, 1213–1223 (2018).
Collod-Béroud, G. et al. Update of the UMD-FBN1 mutation database and creation of an FBN1 polymorphism database. Hum. Mutat. 22, 199–208 (2003).
Tiecke, F. et al. Classic, atypically severe and neonatal Marfan syndrome: twelve mutations and genotype-phenotype correlations in FBN1 exons 24–40. Eur. J. Hum. Genet. 9, 13–21 (2001).
Smallridge, R. S. et al. Solution structure and dynamics of a calcium binding epidermal growth factor-like domain pair from the neonatal region of human fibrillin-1. J. Biol. Chem. 278, 12199–12206 (2003).
Booms, P., Tiecke, F., Rosenberg, T., Hagemeier, C. & Robinson, P. N. Differential effect of FBN1 mutations on in vitro proteolysis of recombinant fibrillin-1 fragments. Hum. Genet. 107, 216–224 (2000).
Jensen, S. A., Robertson, I. B. & Handford, P. A. Dissecting the fibrillin microfibril: structural insights into organization and function. Structure 20, 215–225 (2012).
Jensen, S. A., Corbett, A. R., Knott, V., Redfield, C. & Handford, P. A. Ca2+-dependent interface formation in fibrillin-1. J. Biol. Chem. 280, 14076–14084 (2005).
McGettrick, A. J., Knott, V., Willis, A. & Handford, P. A. Molecular effects of calcium binding mutations in Marfan syndrome depend on domain context. Hum. Mol. Genet. 9, 1987–1994 (2000).
Zoledziewska, M. et al. Height-reducing variants and selection for short stature in Sardinia. Nat. Genet. 47, 1352–1356 (2015).
Fumagalli, M. et al. Greenlandic Inuit show genetic signatures of diet and climate adaptation. Science 349, 1343–1347 (2015).
Luo, Y. et al. Early progression to active tuberculosis is a highly heritable trait driven by 3q23 in Peruvians. Nat. Commun. 10, 3765 (2019).
Zelner, J. L. et al. Identifying hotspots of multidrug-resistant tuberculosis transmission using spatial and molecular genetic data. J. Infect. Dis. 213, 287–294 (2016).
Odone, A. et al. Acquired and transmitted multidrug resistant tuberculosis: the role of social determinants. PLoS ONE 11, e0146642 (2016).
Zhou, X. & Stephens, M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat. Methods 11, 407–409 (2014).
Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135 (2008).
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
Conomos, M. P., Reiner, A. P., Weir, B. S. & Thornton, T. A. Model-free estimation of recent genetic relatedness. Am. J. Hum. Genet. 98, 127–148 (2016).
Gusev, A. et al. Whole population, genome-wide mapping of hidden relatedness. Genome Res. 19, 318–326 (2009).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Reich, D. et al. Reconstructing Native American population history. Nature 488, 370–374 (2012).
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Chen, C.-Y. et al. Improved ancestry inference using weights from external reference panels. Bioinformatics 29, 1399–1406 (2013).
Ziyatdinov, A. et al. lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals. BMC Bioinformatics 19, 68 (2018).
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
Schick, U. M. et al. Genome-wide association study of platelet count identifies ancestry-specific loci in Hispanic/Latino Americans. Am. J. Hum. Genet. 98, 229–242 (2016).
Balduzzi, S., Rücker, G. & Schwarzer, G. How to perform a meta-analysis with R: a practical tutorial. Evid. Based Ment. Health 22, 153–160 (2019).
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).
Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).
Bakshi, A. et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci. Rep. 6, 32894 (2016).
Szpiech, Z. A. & Hernandez, R. D. selscan: an efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 31, 2824–2827 (2014).
Marcus, J. H. & Novembre, J. Visualizing the geography of genetic variants. Bioinformatics 33, 594–595 (2017).
Kelleher, J., Etheridge, A. M. & McVean, G. Efficient Coalescent simulation and genealogical analysis for large sample sizes. PLOS Comput. Biol. 12, e1004842 (2016).
International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003).
Gravel, S. et al. Demographic history and rare allele sharing among human populations. Proc. Natl Acad. Sci. USA 108, 11983–11988 (2011).
Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Lin, D. et al. Digestion-ligation-only Hi-C is an efficient and cost-effective method for chromosome conformation capture. Nat. Genet. 50, 754–763 (2018).
Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).