The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity
Mutations in noncoding sequence can cause disease but are very difficult to recognize. Here, we present two approaches intended to help identify noncoding regions of the genome that may carry mutations influencing disease. The first approach is based on comparing observed and predicted levels of standing human variation in the noncoding exome sequence of a gene. The second approach is based on the phylogenetic conservation of a gene’s noncoding exome sequence using GERP++. We find that both approaches can predict genes known to cause disease through changes in expression level, genes depleted of loss-of-function alleles in the general population, and genes permissive of copy number variants in the general population. We find that both scores aid in interpreting loss-of-function mutations and in defining regions of noncoding sequence that are more likely to harbor mutations that influence risk of disease.
Vyšlo v časopise:
The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity. PLoS Genet 11(9): e32767. doi:10.1371/journal.pgen.1005492
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1005492
Souhrn
Mutations in noncoding sequence can cause disease but are very difficult to recognize. Here, we present two approaches intended to help identify noncoding regions of the genome that may carry mutations influencing disease. The first approach is based on comparing observed and predicted levels of standing human variation in the noncoding exome sequence of a gene. The second approach is based on the phylogenetic conservation of a gene’s noncoding exome sequence using GERP++. We find that both approaches can predict genes known to cause disease through changes in expression level, genes depleted of loss-of-function alleles in the general population, and genes permissive of copy number variants in the general population. We find that both scores aid in interpreting loss-of-function mutations and in defining regions of noncoding sequence that are more likely to harbor mutations that influence risk of disease.
Zdroje
1. Makrythanasis P, Antonarakis SE (2013) Pathogenic variants in non-protein-coding sequences. Clin Genet 84: 422–428. doi: 10.1111/cge.12272 24007299
2. Ward LD, Kellis M (2012) Interpreting noncoding genetic variation in complex traits and human disease. Nat Biotechnol 30: 1095–1106. doi: 10.1038/nbt.2422 23138309
3. Treisman R, Orkin SH, Maniatis T (1983) Specific transcription and RNA splicing defects in five cloned beta-thalassaemia genes. Nature 302: 591–596. 6188062
4. Signori E, Bagni C, Papa S, Primerano B, Rinaldi M, et al. (2001) A somatic mutation in the 5'UTR of BRCA1 gene in sporadic breast cancer causes down-modulation of translation efficiency. Oncogene 20: 4596–4600. 11494157
5. Chatterjee S, Pal JK (2009) Role of 5'- and 3'-untranslated regions of mRNAs in human diseases. Biol Cell 101: 251–262. doi: 10.1042/BC20080104 19275763
6. Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, et al. (2010) Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol 6: e1001025. doi: 10.1371/journal.pcbi.1001025 21152010
7. Cooper GM, Stone EA, Asimenos G, Program NCS, Green ED, et al. (2005) Distribution and intensity of constraint in mammalian genomic sequence. Genome Res 15: 901–913. 15965027
8. Ward LD, Kellis M (2012) Evidence of abundant purifying selection in humans for recently acquired regulatory functions. Science 337: 1675–1678. 22956687
9. Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, et al. (2013) Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 342: 1235587. doi: 10.1126/science.1235587 24092746
10. Ward LD, Kellis M (2012) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40: D930–934. doi: 10.1093/nar/gkr917 22064851
11. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, et al. (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22: 1790–1797. doi: 10.1101/gr.137323.112 22955989
12. Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, et al. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46: 310–315. doi: 10.1038/ng.2892 24487276
13. Ritchie GR, Dunham I, Zeggini E, Flicek P (2014) Functional annotation of noncoding sequence variants. Nat Methods 11: 294–296. doi: 10.1038/nmeth.2832 24487584
14. Cirulli ET, Lasseigne BN, Petrovski S, Sapp PC, Dion PA, et al. (2015) Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways. Science.
15. Petrovski S, Wang Q, Heinzen EL, Allen AS, Goldstein DB (2013) Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet 9: e1003709. doi: 10.1371/journal.pgen.1003709 23990802
16. Server EV NHLBI GO Exome Sequencing Project (ESP). Seattle, WA.
17. Conrad DF, Pinto D, Redon R, Feuk L, Gokcumen O, et al. (2010) Origins and functional impact of copy number variation in the human genome. Nature 464: 704–712. doi: 10.1038/nature08516 19812545
18. MacDonald JR, Ziman R, Yuen RK, Feuk L, Scherer SW (2014) The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Res 42: D986–992. doi: 10.1093/nar/gkt958 24174537
19. Genomes Project C, Abecasis GR, Auton A, Brooks LD, DePristo MA, et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65. doi: 10.1038/nature11632 23128226
20. Zhang F, Gu W, Hurles ME, Lupski JR (2009) Copy number variation in human health, disease, and evolution. Annu Rev Genomics Hum Genet 10: 451–481. doi: 10.1146/annurev.genom.9.081307.164217 19715442
21. Huang N, Lee I, Marcotte EM, Hurles ME (2010) Characterising and predicting haploinsufficiency in the human genome. PLoS Genet 6: e1001154. doi: 10.1371/journal.pgen.1001154 20976243
22. Girard SL, Gauthier J, Noreau A, Xiong L, Zhou S, et al. (2011) Increased exonic de novo mutation rate in individuals with schizophrenia. Nat Genet 43: 860–863. doi: 10.1038/ng.886 21743468
23. Neale BM, Kou Y, Liu L, Ma'ayan A, Samocha KE, et al. (2012) Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485: 242–245. doi: 10.1038/nature11011 22495311
24. Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, et al. (2012) De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485: 237–241. doi: 10.1038/nature10945 22495306
25. O'Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, et al. (2012) Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485: 246–250. doi: 10.1038/nature10989 22495309
26. Iossifov I, Ronemus M, Levy D, Wang Z, Hakker I, et al. (2012) De novo gene disruptions in children on the autistic spectrum. Neuron 74: 285–299. doi: 10.1016/j.neuron.2012.04.009 22542183
27. de Ligt J, Willemsen MH, van Bon BW, Kleefstra T, Yntema HG, et al. (2012) Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med 367: 1921–1929. doi: 10.1056/NEJMoa1206524 23033978
28. Epi KC, Epilepsy Phenome/Genome P, Allen AS, Berkovic SF, Cossette P, et al. (2013) De novo mutations in epileptic encephalopathies. Nature 501: 217–221. doi: 10.1038/nature12439 23934111
29. Fromer M, Pocklington AJ, Kavanagh DH, Williams HJ, Dwyer S, et al. (2014) De novo mutations in schizophrenia implicate synaptic networks. Nature 506: 179–184. doi: 10.1038/nature12929 24463507
30. Rauch A, Wieczorek D, Graf E, Wieland T, Endele S, et al. (2012) Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380: 1674–1682. doi: 10.1016/S0140-6736(12)61480-9 23020937
31. Xu B, Ionita-Laza I, Roos JL, Boone B, Woodrick S, et al. (2012) De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet 44: 1365–1369. doi: 10.1038/ng.2446 23042115
32. Gulsuner S, Walsh T, Watts AC, Lee MK, Thornton AM, et al. (2013) Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154: 518–529. doi: 10.1016/j.cell.2013.06.049 23911319
33. Iossifov I, O'Roak BJ, Sanders SJ, Ronemus M, Krumm N, et al. (2014) The contribution of de novo coding mutations to autism spectrum disorder. Nature 515: 216–221. doi: 10.1038/nature13908 25363768
34. Zaidi S, Choi M, Wakimoto H, Ma L, Jiang J, et al. (2013) De novo mutations in histone-modifying genes in congenital heart disease. Nature 498: 220–223. doi: 10.1038/nature12141 23665959
35. Carvill GL, Heavin SB, Yendle SC, McMahon JM, O'Roak BJ, et al. (2013) Targeted resequencing in epileptic encephalopathies identifies de novo mutations in CHD2 and SYNGAP1. Nat Genet 45: 825–830. doi: 10.1038/ng.2646 23708187
36. Bernier R, Golzio C, Xiong B, Stessman HA, Coe BP, et al. (2014) Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158: 263–276. doi: 10.1016/j.cell.2014.06.017 24998929
37. Dong S, Walker MF, Carriero NJ, DiCola M, Willsey AJ, et al. (2014) De novo insertions and deletions of predominantly paternal origin are associated with autism spectrum disorder. Cell Rep 9: 16–23. doi: 10.1016/j.celrep.2014.08.068 25284784
38. Grozeva D, Carss K, Spasic-Boskovic O, Parker MJ, Archer H, et al. (2014) De novo loss-of-function mutations in SETD5, encoding a methyltransferase in a 3p25 microdeletion syndrome critical region, cause intellectual disability. Am J Hum Genet 94: 618–624. doi: 10.1016/j.ajhg.2014.03.006 24680889
39. Orosco LA, Ross AP, Cates SL, Scott SE, Wu D, et al. (2014) Loss of Wdfy3 in mice alters cerebral cortical neurogenesis reflecting aspects of the autism pathology. Nat Commun 5: 4692. doi: 10.1038/ncomms5692 25198012
40. Tonkin ET, Wang TJ, Lisgo S, Bamshad MJ, Strachan T (2004) NIPBL, encoding a homolog of fungal Scc2-type sister chromatid cohesion proteins and fly Nipped-B, is mutated in Cornelia de Lange syndrome. Nat Genet 36: 636–641. 15146185
41. Jones WD, Dafou D, McEntagart M, Woollard WJ, Elmslie FV, et al. (2012) De novo mutations in MLL cause Wiedemann-Steiner syndrome. Am J Hum Genet 91: 358–364. doi: 10.1016/j.ajhg.2012.06.008 22795537
42. Consortium EP (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57–74. doi: 10.1038/nature11247 22955616
43. Samocha KE, Robinson EB, Sanders SJ, Stevens C, Sabo A, et al. (2014) A framework for the interpretation of de novo mutation in human disease. Nat Genet 46: 944–950. doi: 10.1038/ng.3050 25086666
44. Pruitt KD, Harrow J, Harte RA, Wallin C, Diekhans M, et al. (2009) The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes. Genome Res 19: 1316–1323. doi: 10.1101/gr.080531.108 19498102
45. Hubbard T, Barker D, Birney E, Cameron G, Chen Y, et al. (2002) The Ensembl genome database project. Nucleic Acids Res 30: 38–41. 11752248
46. Kryukov GV, Pennacchio LA, Sunyaev SR (2007) Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am J Hum Genet 80: 727–739. 17357078
47. Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589–595. doi: 10.1093/bioinformatics/btp698 20080505
48. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, et al. (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature genetics 43: 491–498. doi: 10.1038/ng.806 21478889
49. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, et al. (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6: 80–92. doi: 10.4161/fly.19695 22728672
50. Venables WN, Ripley BD (2002) Modern Applied Statistics with R. New York: Springer.
51. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57: 289–300.
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2015 Číslo 9
- Je „freeze-all“ pro všechny? Odborníci na fertilitu diskutovali na virtuálním summitu
- Gynekologové a odborníci na reprodukční medicínu se sejdou na prvním virtuálním summitu
Najčítanejšie v tomto čísle
- Arabidopsis AtPLC2 Is a Primary Phosphoinositide-Specific Phospholipase C in Phosphoinositide Metabolism and the Endoplasmic Reticulum Stress Response
- Bridges Meristem and Organ Primordia Boundaries through , , and during Flower Development in
- KLK5 Inactivation Reverses Cutaneous Hallmarks of Netherton Syndrome
- The Chromatin Protein DUET/MMD1 Controls Expression of the Meiotic Gene during Male Meiosis in