Localization of adaptive variants in human genomes using averaged one-dependence estimation

Nature Communications

Volumn 9, Issue 1, 2018, Pages

  Sugden, Lauren Alpert ^a   Atkinson, Elizabeth G ^b   Fischer, Annie P ^a   Rong, Stephen ^a,c   Henn, Brenna M ^b   Ramachandran, Sohini ^a  

^a BROWN UNIVERSITY   (United States)

^b STONY BROOK UNIVERSITY   (United States)

^c BROWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADIPONECTIN; PLATELET DERIVED GROWTH FACTOR ALPHA RECEPTOR; TRIACYLGLYCEROL;

ANALYTICAL FRAMEWORK; DEMOGRAPHY; ESTIMATION METHOD; EVOLUTIONARY BIOLOGY; GENE; GENE EXPRESSION; GENOME; HUNTER-GATHERER; INTEGRATED APPROACH; MUTATION; NUMERICAL MODEL; OBESITY; SIMULATION;

ADAPTIVE MUTATION; ARTICLE; ASTN2 GENE; DEMOGRAPHY; DUT GENE; EXOME; GENE; GENE FREQUENCY; GENE LOCUS; GENE MUTATION; GENETIC ASSOCIATION; HAPLOGROUP; HUMAN; HUMAN GENOME; KHOISAN; MACHINE LEARNING; METABOLISM; OBESITY; PEPD GENE; PHACTR3 GENE; SELECTIVE SWEEP; SIDT2 GENE; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; ADAPTATION; AFRICA; ALGORITHM; BIOLOGICAL MODEL; GENETIC SELECTION; GENETICS; HUMAN GENOME PROJECT; MUTATION; POPULATION GENETICS; PROBABILITY;

SOUTHERN AFRICA;

ADAPTATION, PHYSIOLOGICAL; AFRICA, SOUTHERN; ALGORITHMS; GENETICS, POPULATION; GENOME, HUMAN; HUMAN GENOME PROJECT; HUMANS; MODELS, GENETIC; MUTATION; POLYMORPHISM, SINGLE NUCLEOTIDE; PROBABILITY; SELECTION, GENETIC;

EID: 85042230281     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-018-03100-7     Document Type: Article

References (110)

1. Weir, B. S. et al. Genetic Data Analysis. Methods for Discrete Population Genetic Data (Sinauer Associates, Sunderland, 1990).
2. 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).
3. Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585-595 (1989).
4. Nielsen, R. et al. Genomic scans for selective sweeps using SNP data. Genome Res. 15, 1566-1575 (2005).
5. Pavlidis, P., Živković, D., Stamatakis, A. & Alachiotis, N. SweeD: likelihood-based detection of selective sweeps in thousands of people. Mol. Biol. Evol. 30, 2224-2234 (2013).
6. Akey, J. M. Constructing genomic maps of positive selection in humans: where do we go from here? Genome Res. 19, 711-722 (2009).
7. Vitti, J. J., Grossman, S. R. & Sabeti, P. C. Detecting natural selection in genomic data. Annu. Rev. Genet. 47, 97-120 (2013).
8. Pavlidis, P., Jensen, J. D. & Stephan, W. Searching for footprints of positive selection in whole-genome SNP data from nonequilibrium populations. Genetics 185, 907-922 (2010).
9. Grossman, S. et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327, 883-886 (2010).
10. Lin, K., Li, H., Schlötterer, C. & Futschik, A. Distinguishing positive selection from neutral evolution: boosting the performance of summary statistics. Genetics 187, 229-244 (2011).
11. Ronen, R., Udpa, N., Halperin, E. & Bafna, V. Learning natural selection from the site frequency spectrum. Genetics 195, 181-193 (2013).
12. Grossman, S. R. et al. Identifying recent adaptations in large-scale genomic data. Cell 152, 703-713 (2013).
13. Pybus, M. et al. Hierarchical boosting: A machine-learning framework to detect and classify hard selective sweeps in human populations. Bioinformatics 31, 3946-3952 (2015).
14. Schrider, D. R. & Kern, A. D. S/HIC: robust identification of soft and hard sweeps using machine learning. PLoS Genet. 12, e1005928 (2016).
15. Sheehan, S. & Song, Y. S. Deep learning for population genetic inference. PLoS Comput. Biol. 12, e1004845 (2016).
16. Sabeti, P. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913-918 (2007).
17. Messer, P. W. & Petrov, D. A. Population genomics of rapid adaptation by soft selective sweeps. Trends Ecol. Evol. 28, 659-669 (2013).
18. Garud, N. R., Messer, P. W., Buzbas, E. O. & Petrov, D. A. Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps. PLoS Genet. 11, e1005004 (2015).
19. Schrider, D. R. & Kern, A. D. Soft sweeps are the dominant mode of adaptation in the human genome. Mol. Biol. Evol. 34, 1863-1877 (2017).
20. Webb, G. I., Boughton, J. R. & Wang, Z. Not so naive Bayes: Aggregating one-dependence estimators. Mach. Learn. 58, 5-24 (2005).
21. Zadrozny, B. & Elkan, C. Transforming classifier scores into accurate multiclass probability estimates. In Proc. 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Brodley, C. E. & Danyluk, A. P.) 694-699 (ACM, New York, 2002).
22. Wagh, K. et al. Lactase persistence and lipid pathway selection in the Maasai. PLoS ONE 7, e44751 (2012).
23. Schaffner, S. et al. Calibrating a coalescent simulation of human genome sequence variation. Genome Res. 15, 1576-1583 (2005).
24. Soejima, M. & Koda, Y. Population differences of two coding SNPs in pigmentation-related genes SLC24A5 and SLC45A2. Int. J. Leg. Med 121, 36-39 (2007).
25. Reich, D. et al. Reduced neutrophil count in people of African descent is due to a regulatory variant in the Duffy antigen receptor for chemokines gene. PLoS Genet. 5, e1000360 (2009).
26. Eiberg, H. et al. Blue eye color in humans may be caused by a perfectly associated founder mutation in a regulatory element located within the HERC2 gene inhibiting OCA2 expression. Hum. Genet. 123, 177-187 (2008).
27. Uren, C. et al. Fine-scale human population structure in southern Africa reflects ecogeographic boundaries. Genetics 204, 303-314 (2016).
28. Henn, B. M. et al. Hunter-gatherer genomic diversity suggests a southern African origin for modern humans. Proc. Natl. Acad. Sci. USA 108, 5154-5162 (2011).
29. Gronau, I., Hubisz, M. J., Gulko, B., Danko, C. G. & Siepel, A. Bayesian inference of ancient human demography from individual genome sequences. Nat. Genet. 43, 1031-1034 (2011).
30. Martin, A. R. et al. An unexpectedly complex architecture for skin pigmentation in africans. Cell 171, 1340-1353 (2017).
31. Kawashima, M., Ohashi, J., Nishida, N. & Tokunaga, K. Evolutionary analysis of classical HLA class I and II genes suggests that recent positive selection acted on DPB1 04:01 in Japanese population. PLoS ONE 7, e46806 (2012).
32. Zhou, Q., Zhao, L. & Guan, Y. Strong selection at MHC in Mexicans since admixture. PLoS Genet. 12, e1005847 (2016).
33. Schlebusch, C. M. et al. Genomic variation in seven Khoe-San groups reveals adaptation and complex African history. Science 338, 374-379 (2012).
34. Hedrick, P. W. Balancing selection and MHC. Genetica 104, 207-214 (1998).
35. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 14, 128 (2013).
36. Lonsdale, J. et al. The genotype-Tissue expression (GTEx) project. Nat. Genet. 45, 580-585 (2013).
37. Riestra, P. et al. Gender-specific associations between ADIPOQ gene polymorphisms and adiponectin levels and obesity in the Jackson Heart Study cohort. BMC Med. Genet. 16, 65 (2015).
38. Glastonbury, C. A. et al. Adiposity-dependent regulatory effects on multi-Tissue transcriptomes. Am. J. Hum. Genet. 99, 567-579 (2016).
39. Granka, J. M. et al. Limited evidence for classic selective sweeps in African populations. Genetics 192, 1049-1064 (2012).
40. Peter, B. M., Huerta-Sanchez, E. & Nielsen, R. Distinguishing between selective sweeps from standing variation and from a de novo mutation. PLoS Genet. 8, e1003011 (2012).
41. Akbari, A. et al. Fine-mapping the favored mutation in a positive selective sweep. Preprint available from: https://doi.org/10.1101/139055 (2017).
42. Scheet, P. & Stephens, M. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629-644 (2006).
43. McManus, K. F. et al. Population genetic analysis of the DARC locus (Duffy) reveals adaptation from standing variation associated with malaria resistance in humans. PLoS Genet. 13, e1006560 (2017).
44. Neel, J. V. Diabetes mellitus: A "thrifty" genotype rendered detrimental by "progress"? Am. J. Hum. Genet. 14, 353 (1962).
45. Ayub, Q. et al. Revisiting the thrifty gene hypothesis via 65 loci associated with susceptibility to type 2 diabetes. Am. J. Hum. Genet. 94, 176-185 (2014).
46. Saint-Hilaire, É. G. & Cuvier, F. Histoire Naturelle des Mammifères (Belin, Paris, 1833).
47. Qureshi, S. Displaying Sara Baartman, the 'Hottentot Venus'. Hist. Sci. 42, 233-257 (2004).
48. Ersek, R. A., Newton Bell, H. & Vazquez Salisbury, A. Serial and superficial suction for steatopygia (Hottentot bustle). Aesthet. Plast. Surg. 18, 279-282 (1994).
49. Manolopoulos, K., Karpe, F. & Frayn, K. Gluteofemoral body fat as a determinant of metabolic health. Int J. Obes. (Lond.) 34, 949-959 (2010).
50. Ahima, R. S. & Lazar, M. A. The health risk of obesity-better metrics imperative. Science 341, 856-858 (2013).
51. Andrés, A. M. et al. Targets of balancing selection in the human genome. Mol. Biol. Evol. 26, 2755-2764 (2009).
52. Lenz, T. L., Spirin, V., Jordan, D. M. & Sunyaev, S. R. Excess of deleterious mutations around HLA genes reveals evolutionary cost of balancing selection. Mol. Biol. Evol. 33, 2555-2564 (2016).
53. Albrechtsen, A., Moltke, I. & Nielsen, R. Natural selection and the distribution of identity-by-descent in the human genome. Genetics 186, 295-308 (2010).
54. Spurgin, L. G. & Richardson, D. S. How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings. Proc. R. Soc. Lond. Ser. B 277, 979-988 (2010).
55. Chen, H., Patterson, N. & Reich, D. Population differentiation as a test for selective sweeps. Genome Res 20, 393-402 (2010).
56. Kong, A. et al. A high-resolution recombination map of the human genome. Nat. Genet 31, 241-247 (2002).
57. Kim, Y. & Stephan, W. Detecting a local signature of genetic hitchhiking along a recombining chromosome. Genetics 160, 765-777 (2002).
58. Gravel, S. et al. Demographic history and rare allele sharing among human populations. Proc. Natl. Acad. Sci. USA 108, 11983-11988 (2011).
59. Messer, P. W. SLiM: simulating evolution with selection and linkage. Genetics 194, 1037-1039 (2013).
60. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825-2830 (2011).
61. Zadrozny, B. & Elkan, C. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In ICML '01 Proc. of the Eighteenth International Conference on Machine Learning Vol. 1 (eds Brodley, C. E. & Danyluk, A. P.) 609-616 (2001).
62. Niculescu-Mizil, A. & Caruana, R. Predicting good probabilities with supervised learning. In Proc. 22nd International Conference on Machine Learning (eds De Raedt, L. & Wrobel, S.) 625-632 (ACM, New York, 2005).
63. 1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68-74 (2015).
64. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336-2337 (2010).
65. Sjöstrand, A. E., Sjödin, P. & Jakobsson, M. Private haplotypes can reveal local adaptation. BMC Genet. 15, 61 (2014).
66. Khor, C. C. et al. CISH and susceptibility to infectious diseases. N. Engl. J. Med. 362, 2092-2101 (2010).
67. Fujimoto, A. et al. A scan for genetic determinants of human hair morphology: EDAR is associated with Asian hair thickness. Hum. Mol. Genet. 17, 835-843 (2008).
68. Kimura, R. et al. A common variation in EDAR is a genetic determinant of shovel-shaped incisors. Am. J. Hum. Genet. 85, 528-535 (2009).
69. Peschon, J. J. et al. An essential role for ectodomain shedding in mammalian development. Science 282, 1281-1284 (1998).
70. Norton, H. L. et al. Genetic evidence for the convergent evolution of light skin in Europeans and East Asians. Mol. Biol. Evol. 24, 710-722 (2007).
71. Hider, J. L. et al. Exploring signatures of positive selection in pigmentation candidate genes in populations of East Asian ancestry. BMC Evol. Biol. 13, 150 (2013).
72. Marcus, J. H. & Novembre, J. Visualizing the geography of genetic variants. Bioinformatics 33, 594-595 (2016).
73. Heid, I. M. et al. Meta-Analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949-960 (2010).
74. Graff, M. et al. Generalization of adiposity genetic loci to US Hispanic women. Nutr. Diabetes 3, e85 (2013).
75. Bandstein, M. et al. A genetic risk score is associated with weight loss following Roux-en Y gastric bypass surgery. Obes. Surg. 26, 2183-2189 (2016).
76. Kubo, M. et al. Modulation of adipogenesis-related gene expression by estrogen-related receptor γ during adipocytic differentiation. Biochim. Biophys. Acta 1789, 71-77 (2009).
77. Dong, S.-S. et al. Epigenomic elements analyses for promoters identify ESRRG as a new susceptibility gene for obesity-related traits. Int. J. Obes. (Lond.) 40, 1170 (2016).
78. Dixen, K. et al. ERRγ enhances UCP1 expression and fatty acid oxidation in brown adipocytes. Obesity (Silver Spring) 21, 516-524 (2013).
79. Kim, D.-K. et al. Orphan nuclear receptor estrogen-related receptor γ (ERRγ) is key regulator of hepatic gluconeogenesis. J. Biol. Chem. 287, 21628-21639 (2012).
80. Kim, D.-K. et al. PKB/Akt phosphorylation of ERRγ contributes to insulin-mediated inhibition of hepatic gluconeogenesis. Diabetologia 57, 2576-2585 (2014).
81. Yoshihara, E. et al. ERRγ is required for the metabolic maturation of therapeutically functional glucose-responsive β cells. Cell Metab. 23, 622-634 (2016).
82. Sanoudou, D., Duka, A., Drosatos, K., Hayes, K. & Zannis, V. Role of Esrrg in the fibrate-mediated regulation of lipid metabolism genes in human ApoA-I transgenic mice. Pharmacogenom. J. 10, 165-179 (2010).
83. Babicz, K., Linke, W. A. & Krueger, M. Insulin signaling regulates cardiac titin isoform composition in development and diabetic cardiomyopathy. Biophys. J. 98, 757a (2010).
84. Bare, L. A. et al. Five common gene variants identify elevated genetic risk for coronary heart disease. Genet. Med. 9, 682-689 (2007).
85. Heid, I. M. et al. Clear detection of ADIPOQ locus as the major gene for plasma adiponectin: results of genome-wide association analyses including 4659 European individuals. Atherosclerosis 208, 412-420 (2010).
86. Nimptsch, K. et al. Genetic variation in the ADIPOQ gene, adiponectin concentrations and risk of colorectal cancer: A Mendelian randomization analysis using data from three large cohort studies. Eur. J. Epidemiol. 32, 419-430 (2017).
87. Dastani, Z. et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: A multi-ethnic meta-Analysis of 45,891 individuals. PLoS Genet. 8, e1002607 (2012).
88. Kanu, J. S. et al. Single nucleotide polymorphism rs3774261 in the AdipoQ gene is associated with the risk of coronary heart disease (CHD) in Northeast Han Chinese population: A case-control study. Lipids Health Dis. 15, 6 (2016).
89. Gajewska, J. et al. ADIPOQ-11377C>G polymorphism increases the risk of adipokine abnormalities and child obesity regardless of dietary intake. J. Pediatr. Gastroenterol. Nutr. 62, 122-129 (2016).
90. Zhou, J. M. et al. Association of the ADIPOQ Rs2241766 and Rs266729 polymorphisms with metabolic syndrome in the Chinese population: A meta-Analysis. Biomed. Environ. Sci. 29, 505-515 (2016).
91. Yao, M. et al. Association of ADIPOQ variants with type 2 diabetes mellitus susceptibility in ethnic Han Chinese from northeast China. J. Diabetes Investig. 7, 853-859 (2016).
92. Hsiao, T.-J. & Lin, E. A validation study of adiponectin rs266729 gene variant with type 2 diabetes, obesity, and metabolic phenotypes in a Taiwanese population. Biochem. Genet. 54, 830-841 (2016).
93. Ahl, S. et al. Adiponectin levels differentiate metabolically healthy vs unhealthy among obese and nonobese white individuals. J. Clin. Endocrinol. Metab. 100, 4172-4180 (2015).
94. Ko, J. K. Y. et al. Serum adiponectin is independently associated with the metabolic syndrome in Hong Kong, Chinese women with polycystic ovary syndrome. Gynecol. Endocrinol. 32, 390-394 (2016).
95. Berry, R. & Rodeheffer, M. S. Characterization of the adipocyte cellular lineage in vivo. Nat. Cell Biol. 15, 302-308 (2013).
96. Chen, H. et al. PDGF signalling controls age-dependent proliferation in pancreatic [bgr]-cells. Nature 478, 349-355 (2011).
97. Perdu, S. et al. Maternal obesity drives functional alterations in uterine NK cells. JCI Insight 1, e85560 (2016).
98. Jiao, H. et al. Pathway-based genome-wide association studies for plasma triglycerides in obese females and normal-weight controls. PLoS ONE 10, e0134923 (2015).
99. Comuzzie, A. G. et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS ONE 7, e51954 (2012).
100. Chang, G. et al. SIDT2 is involved in the NAADP-mediated release of calcium from insulin secretory granules. J. Mol. Endocrinol. 56, 249-259 (2016).
101. Gao, J., Gu, X., Mahuran, D. J., Wang, Z. & Zhang, H. Impaired glucose tolerance in a mouse model of sidt2 deficiency. PLoS ONE 8, e66139 (2013).
102. Gao, J., Yu, C., Xiong, Q., Zhang, Y. & Wang, L. Lysosomal integral membrane protein Sidt2 plays a vital role in insulin secretion. Int. J. Clin. Exp. Pathol. 8, 15622 (2015).
103. Asselbergs, F. W. et al. Large-scale gene-centric meta-Analysis across 32 studies identifies multiple lipid loci. Am. J. Hum. Genet. 91, 823-838 (2012).
104. Schupp, M. et al. Metabolite and transcriptome analysis during fasting suggest a role for the p53-Ddit4 axis in major metabolic tissues. BMC Genom. 14, 758 (2013).
105. Dos Santos, R. S. et al. dUTPase (DUT) is mutated in a novel monogenic syndrome with diabetes and bone marrow failure. Diabetes 66, 1086-1096 (2017).
106. Wu, Y. et al. A meta-Analysis of genome-wide association studies for adiponectin levels in East Asians identifies a novel locus near WDR11-FGFR2. Hum. Mol. Genet. 23, 1108-1119 (2013).
107. Cho, Y. S. et al. Meta-Analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat. Genet. 44, 67-72 (2011).
108. Scott, R. A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991-1005 (2012).
109. Lin, Q.-Z. et al. Sex-specific association of the peptidase D geners731839 polymorphism and serum lipid levels in the Mulao and Han populations. Int. J. Clin. Exp. Pathol. 7, 4156 (2014).
110. Zheng, J.-S. et al. Modulation of the association between the PEPD variant and the risk of type 2 diabetes by n-3 fatty acids in Chinese Hans. J. Nutrigenet. Nutrigenom. 8, 36-43 (2015).