Author Contributions
J.X.K., S.Y.W., Z.J., Y.N.M., and H.Y.C contributed equally to this
work. Y.S.J., H.C.L, M.M.Z. conceived and contributed the work. C.S.,
J.X, J.X.T., Y.D., W.H.L., H.S.T., X.Y.G., drafted and modified the
manuscript. S.B., C.Z. are important contributors of the GWAS Project.
The GWAS Project provided data support.
References
1 Ennis, S., Murray, A., Brightwell, G., Morton, N. E., & Jacobs, P. A.
(2007). Closely linked cis-acting modifier of expansion of the CGG
repeat in high risk FMR1 haplotypes. Hum Mutat, 28 (12),
1216-1224. doi:10.1002/humu.20600
2 Pearson, C. E., Nichol Edamura, K., & Cleary, J. D. (2005). Repeat
instability: mechanisms of dynamic mutations. Nat Rev Genet,
6 (10), 729-742. doi:10.1038/nrg1689
3 Gerhardt, J., Zaninovic, N., Zhan, Q., Madireddy, A., Nolin, S. L.,
Ersalesi, N., et al. (2014). Cis-acting DNA sequence at a replication
origin promotes repeat expansion to fragile X full mutation. J
Cell Biol, 206 (5), 599-607. doi:10.1083/jcb.201404157
4 Chen, J. M., Ferec, C., & Cooper, D. N. (2006). A systematic analysis
of disease-associated variants in the 3’ regulatory regions of human
protein-coding genes II: the importance of mRNA secondary structure in
assessing the functionality of 3’ UTR variants. Hum Genet,
120 (3), 301-333. doi:10.1007/s00439-006-0218-x
5 Shen, L. X., Basilion, J. P., & Stanton, V. P., Jr. (1999).
Single-nucleotide polymorphisms can cause different structural folds of
mRNA. Proc Natl Acad Sci U S A, 96 (14), 7871-7876.
doi:10.1073/pnas.96.14.7871
6 Ramirez-Bello, J., & Jimenez-Morales, M. (2017). [Functional
implications of single nucleotide polymorphisms (SNPs) in protein-coding
and non-coding RNA genes in multifactorial diseases]. Gac Med
Mex, 153 (2), 238-250.
7 Haas, U., Sczakiel, G., & Laufer, S. D. (2012). MicroRNA-mediated
regulation of gene expression is affected by disease-associated SNPs
within the 3’-UTR via altered RNA structure. RNA Biol, 9 (6),
924-937. doi:10.4161/rna.20497
8 Wittenhagen, L. M., & Kelley, S. O. (2003). Impact of disease-related
mitochondrial mutations on tRNA structure and function. Trends
Biochem Sci, 28 (11), 605-611. doi:10.1016/j.tibs.2003.09.006
9 Tanaka, M., Takeyasu, T., Fuku, N., Li-Jun, G., & Kurata, M. (2004).
Mitochondrial genome single nucleotide polymorphisms and their
phenotypes in the Japanese. Ann N Y Acad Sci, 1011 , 7-20.
doi:10.1007/978-3-662-41088-2_2
10 Zhang, F., & Lupski, J. R. (2015). Non-coding genetic variants in
human disease. Hum Mol Genet, 24 (R1), R102-110.
doi:10.1093/hmg/ddv259
11 Hu, X., Feng, Y., Zhang, D., Zhao, S. D., Hu, Z., Greshock, J., et
al. (2014). A functional genomic approach identifies FAL1 as an
oncogenic long noncoding RNA that associates with BMI1 and represses p21
expression in cancer. Cancer Cell, 26 (3), 344-357.
doi:10.1016/j.ccr.2014.07.009
12 McCaskill, J. S. (1990). The equilibrium partition function and base
pair binding probabilities for RNA secondary structure.Biopolymers, 29 (6-7), 1105-1119. doi:10.1002/bip.360290621
13 Lorenz, R., Bernhart, S. H., Honer Zu Siederdissen, C., Tafer, H.,
Flamm, C., Stadler, P. F., et al. (2011). ViennaRNA Package 2.0.Algorithms Mol Biol, 6 , 26. doi:10.1186/1748-7188-6-26
14 Zuker, M. (1989). Computer prediction of RNA structure. Methods
Enzymol, 180 , 262-288. doi:10.1016/0076-6879(89)80106-5
15 Leontis, N. B., & Westhof, E. (2001). Geometric nomenclature and
classification of RNA base pairs. RNA, 7 (4), 499-512.
doi:10.1017/s1355838201002515
16 Abu Almakarem, A. S., Petrov, A. I., Stombaugh, J., Zirbel, C. L., &
Leontis, N. B. (2012). Comprehensive survey and geometric classification
of base triples in RNA structures. Nucleic Acids Res, 40 (4),
1407-1423. doi:10.1093/nar/gkr810
17 Doherty, E. A., Batey, R. T., Masquida, B., & Doudna, J. A. (2001).
A universal mode of helix packing in RNA. Nat Struct Biol, 8 (4),
339-343. doi:10.1038/86221
18 van Batenburg, F. H., Gultyaev, A. P., & Pleij, C. W. (2001).
PseudoBase: structural information on RNA pseudoknots. Nucleic
Acids Res, 29 (1), 194-195. doi:10.1093/nar/29.1.194
19 Zhao, Q., Zhao, Z., Fan, X., Yuan, Z., Mao, Q., & Yao, Y. (2021).
Review of machine learning methods for RNA secondary structure
prediction. PLoS Comput Biol, 17 (8), e1009291.
doi:10.1371/journal.pcbi.1009291
20 Xu, B., Zhu, Y., Cao, C., Chen, H., Jin, Q., Li, G., et al. (2022).
Recent advances in RNA structurome. Sci China Life Sci, 65 (7),
1285-1324. doi:10.1007/s11427-021-2116-2
21 Seetin, M. G., & Mathews, D. H. (2012). RNA structure prediction: an
overview of methods. Methods Mol Biol, 905 , 99-122.
doi:10.1007/978-1-61779-949-5_8
22 Engelen, S., & Tahi, F. (2010). Tfold: efficient in silico
prediction of non-coding RNA secondary structures. Nucleic Acids
Res, 38 (7), 2453-2466. doi:10.1093/nar/gkp1067
23 Bellaousov, S., & Mathews, D. H. (2010). ProbKnot: fast prediction
of RNA secondary structure including pseudoknots. RNA, 16 (10),
1870-1880. doi:10.1261/rna.2125310
24 Ruan, J., Stormo, G. D., & Zhang, W. (2004). An iterated loop
matching approach to the prediction of RNA secondary structures with
pseudoknots. Bioinformatics, 20 (1), 58-66.
doi:10.1093/bioinformatics/btg373
25 Hofacker, I. L., Fekete, M., Flamm, C., Huynen, M. A., Rauscher, S.,
Stolorz, P. E., et al. (1998). Automatic detection of conserved RNA
structure elements in complete RNA virus genomes. Nucleic Acids
Res, 26 (16), 3825-3836. doi:10.1093/nar/26.16.3825
26 Bindewald, E., & Shapiro, B. A. (2006). RNA secondary structure
prediction from sequence alignments using a network of k-nearest
neighbor classifiers. RNA, 12 (3), 342-352.
doi:10.1261/rna.2164906
27 Legendre, A., Angel, E., & Tahi, F. (2018). Bi-objective integer
programming for RNA secondary structure prediction with pseudoknots.BMC Bioinformatics, 19 (1), 13. doi:10.1186/s12859-018-2007-7
28 Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S.,
Tacker, M., & Schuster, P. (1989). Fast folding and comparison of RNA
secondary structures %J Monatshefte für Chemie / Chemical Monthly.125 (2).
29 Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends,
perspectives, and prospects. Science, 349 (6245), 255-260.
doi:10.1126/science.aaa8415
30 Fresco, J. R., Alberts, B. M., & Doty, P. (1960). Some molecular
details of the secondary structure of ribonucleic acid. Nature,
188 , 98-101. doi:10.1038/188098a0
31 Tinoco, I., Jr., Uhlenbeck, O. C., & Levine, M. D. (1971).
Estimation of secondary structure in ribonucleic acids. Nature,
230 (5293), 362-367. doi:10.1038/230362a0
32 Delisi, C., & Crothers, D. M. (1971). Prediction of RNA secondary
structure. Proc Natl Acad Sci U S A, 68 (11), 2682-2685.
doi:10.1073/pnas.68.11.2682
33 Zuker, M., & Stiegler, P. (1981). Optimal computer folding of large
RNA sequences using thermodynamics and auxiliary information.Nucleic Acids Res, 9 (1), 133-148. doi:10.1093/nar/9.1.133
34 Zuker, M. (1989). On finding all suboptimal foldings of an RNA
molecule. Science, 244 (4900), 48-52. doi:10.1126/science.2468181
35 Halvorsen, M., Martin, J. S., Broadaway, S., & Laederach, A. (2010).
Disease-associated mutations that alter the RNA structural ensemble.PLoS Genet, 6 (8), e1001074. doi:10.1371/journal.pgen.1001074
36 Mathews, D. H. (2014). Using the RNAstructure Software Package to
Predict Conserved RNA Structures. Curr Protoc Bioinformatics, 46 ,
12 14 11-22. doi:10.1002/0471250953.bi1204s46
37 Chen, X., Li, Y., Umarov, R., Gao, X., & Song, L. J. a. e.-p.
(2020). RNA Secondary Structure Prediction By Learning Unrolled
Algorithms. arXiv:2002.05810.https://ui.adsabs.harvard.edu/abs/2020arXiv200205810C
38 Fu, L., Cao, Y., Wu, J., Peng, Q., Nie, Q., & Xie, X. (2022). UFold:
fast and accurate RNA secondary structure prediction with deep learning.Nucleic Acids Res, 50 (3), e14. doi:10.1093/nar/gkab1074
39 Jaeger, J. A., Turner, D. H., & Zuker, M. (1989). Improved
predictions of secondary structures for RNA. Proc Natl Acad Sci U
S A, 86 (20), 7706-7710. doi:10.1073/pnas.86.20.7706
40 Jaeger, J. A., Turner, D. H., & Zuker, M. (1990). Predicting optimal
and suboptimal secondary structure for RNA. Methods Enzymol, 183 ,
281-306. doi:10.1016/0076-6879(90)83019-6
41 Zuker, M. (1994). Prediction of RNA secondary structure by energy
minimization. Methods Mol Biol, 25 , 267-294.
doi:10.1385/0-89603-276-0:267
42 Hofacker, I. L., & Stadler, P. F. (2006). Memory efficient folding
algorithms for circular RNA secondary structures. Bioinformatics,
22 (10), 1172-1176. doi:10.1093/bioinformatics/btl023
43 Lahti, J. L., Tang, G. W., Capriotti, E., Liu, T., & Altman, R. B.
(2012). Bioinformatics and variability in drug response: a protein
structural perspective. J R Soc Interface, 9 (72), 1409-1437.
doi:10.1098/rsif.2011.0843
44 Grant, B. J., Rodrigues, A. P., ElSawy, K. M., McCammon, J. A., &
Caves, L. S. (2006). Bio3d: an R package for the comparative analysis of
protein structures. Bioinformatics, 22 (21), 2695-2696.
doi:10.1093/bioinformatics/btl461
45 Jubb, H. C., Higueruelo, A. P., Ochoa-Montano, B., Pitt, W. R.,
Ascher, D. B., & Blundell, T. L. (2017). Arpeggio: A Web Server for
Calculating and Visualising Interatomic Interactions in Protein
Structures. J Mol Biol, 429 (3), 365-371.
doi:10.1016/j.jmb.2016.12.004
46 Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A.,
Katayama, T., & Kanehisa, M. (2008). AAindex: amino acid index
database, progress report 2008. Nucleic Acids Res, 36 (Database
issue), D202-205. doi:10.1093/nar/gkm998
47 Pires, D. E., Ascher, D. B., & Blundell, T. L. (2014). mCSM:
predicting the effects of mutations in proteins using graph-based
signatures. Bioinformatics, 30 (3), 335-342.
doi:10.1093/bioinformatics/btt691
48 Pires, D. E., Blundell, T. L., & Ascher, D. B. (2015). pkCSM:
Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using
Graph-Based Signatures. J Med Chem, 58 (9), 4066-4072.
doi:10.1021/acs.jmedchem.5b00104
49 Pires, D. E. V., & Ascher, D. B. (2020). mycoCSM: Using Graph-Based
Signatures to Identify Safe Potent Hits against Mycobacteria. J
Chem Inf Model, 60 (7), 3450-3456. doi:10.1021/acs.jcim.0c00362
50 Kaminskas, L. M., Pires, D. E. V., & Ascher, D. B. (2019).
dendPoint: a web resource for dendrimer pharmacokinetics investigation
and prediction. Sci Rep, 9 (1), 15465.
doi:10.1038/s41598-019-51789-3
51 Kedarisetti, K. D., Kurgan, L., & Dick, S. (2006). Classifier
ensembles for protein structural class prediction with varying homology.Biochem Biophys Res Commun, 348 (3), 981-988.
doi:10.1016/j.bbrc.2006.07.141
52 Kumari, B., Kumar, R., & Kumar, M. (2014). PalmPred: an SVM based
palmitoylation prediction method using sequence profile information.PLoS One, 9 (2), e89246. doi:10.1371/journal.pone.0089246
53 Xu, Y., Ding, J., & Wu, L. Y. (2016). iSulf-Cys: Prediction of
S-sulfenylation Sites in Proteins with Physicochemical Properties of
Amino Acids. PLoS One, 11 (4), e0154237.
doi:10.1371/journal.pone.0154237
54 Liang, Y., Liu, S., & Zhang, S. (2016). Detrended cross-correlation
coefficient: Application to predict apoptosis protein subcellular
localization. Math Biosci, 282 , 61-67.
doi:10.1016/j.mbs.2016.09.019
55 Liu, T., Tao, P., Li, X., Qin, Y., & Wang, C. (2015). Prediction of
subcellular location of apoptosis proteins combining tri-gram encoding
based on PSSM and recursive feature elimination. J Theor Biol,
366 , 8-12. doi:10.1016/j.jtbi.2014.11.010
56 Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2021).
DynaMut2: Assessing changes in stability and flexibility upon single and
multiple point missense mutations. Protein Sci, 30 (1), 60-69.
doi:10.1002/pro.3942
57 Topham, C. M., Srinivasan, N., & Blundell, T. L. (1997). Prediction
of the stability of protein mutants based on structural
environment-dependent amino acid substitution and propensity tables.Protein Eng, 10 (1), 7-21. doi:10.1093/protein/10.1.7
58 Worth, C. L., Preissner, R., & Blundell, T. L. (2011). SDM–a
server for predicting effects of mutations on protein stability and
malfunction. Nucleic Acids Res, 39 (Web Server issue), W215-222.
doi:10.1093/nar/gkr363
59 Teng, S., Srivastava, A. K., & Wang, L. (2010). Sequence
feature-based prediction of protein stability changes upon amino acid
substitutions. BMC Genomics, 11 Suppl 2 , S5.
doi:10.1186/1471-2164-11-S2-S5
60 Capriotti, E., Fariselli, P., Rossi, I., & Casadio, R. (2008). A
three-state prediction of single point mutations on protein stability
changes. BMC Bioinformatics, 9 Suppl 2 , S6.
doi:10.1186/1471-2105-9-S2-S6
61 Chen, C. W., Lin, J., & Chu, Y. W. (2013). iStable: off-the-shelf
predictor integration for predicting protein stability changes.BMC Bioinformatics, 14 Suppl 2 , S5.
doi:10.1186/1471-2105-14-S2-S5
62 Chen, C. W., Lin, M. H., Liao, C. C., Chang, H. P., & Chu, Y. W.
(2020). iStable 2.0: Predicting protein thermal stability changes by
integrating various characteristic modules. Comput Struct
Biotechnol J, 18 , 622-630. doi:10.1016/j.csbj.2020.02.021
63 Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M.,
Ronneberger, O., et al. (2021). Highly accurate protein structure
prediction with AlphaFold. Nature, 596 (7873), 583-589.
doi:10.1038/s41586-021-03819-2
64 Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S.,
Lee, G. R., et al. (2021). Accurate prediction of protein structures and
interactions using a three-track neural network. Science,
373 (6557), 871-876. doi:10.1126/science.abj8754
65 Bernhart, S. H., Hofacker, I. L., & Stadler, P. F. (2006). Local RNA
base pairing probabilities in large sequences. Bioinformatics,
22 (5), 614-615. doi:10.1093/bioinformatics/btk014
66 Sabarinathan, R., Tafer, H., Seemann, S. E., Hofacker, I. L.,
Stadler, P. F., & Gorodkin, J. (2013). The RNAsnp web server:
predicting SNP effects on local RNA secondary structure. Nucleic
Acids Res, 41 (Web Server issue), W475-479. doi:10.1093/nar/gkt291
67 Sabarinathan, R., Tafer, H., Seemann, S. E., Hofacker, I. L.,
Stadler, P. F., & Gorodkin, J. (2013). RNAsnp: efficient detection of
local RNA secondary structure changes induced by SNPs. Hum Mutat,
34 (4), 546-556. doi:10.1002/humu.22273
68 Salari, R., Kimchi-Sarfaty, C., Gottesman, M. M., & Przytycka, T. M.
(2013). Sensitive measurement of single-nucleotide polymorphism-induced
changes of RNA conformation: application to disease studies.Nucleic Acids Res, 41 (1), 44-53. doi:10.1093/nar/gks1009
69 Miladi, M., Raden, M., Diederichs, S., & Backofen, R. (2020).
MutaRNA: analysis and visualization of mutation-induced changes in RNA
structure. Nucleic Acids Res, 48 (W1), W287-W291.
doi:10.1093/nar/gkaa331
70 Xu, Y., Wu, T., Li, F., Dong, Q., Wang, J., Shang, D., et al. (2020).
Identification and comprehensive characterization of lncRNAs with copy
number variations and their driving transcriptional perturbed
subpathways reveal functional significance for cancer. Brief
Bioinform, 21 (6), 2153-2166. doi:10.1093/bib/bbz113
71 Niroula, A., & Vihinen, M. (2016). PON-mt-tRNA: a multifactorial
probability-based method for classification of mitochondrial tRNA
variations. Nucleic Acids Res, 44 (5), 2020-2027.
doi:10.1093/nar/gkw046
72 Darty, K., Denise, A., & Ponty, Y. (2009). VARNA: Interactive
drawing and editing of the RNA secondary structure.Bioinformatics, 25 (15), 1974-1975.
doi:10.1093/bioinformatics/btp250
73 Sato, K., Akiyama, M., & Sakakibara, Y. (2021). RNA secondary
structure prediction using deep learning with thermodynamic integration.Nat Commun, 12 (1), 941. doi:10.1038/s41467-021-21194-4
74 Singh, J., Hanson, J., Paliwal, K., & Zhou, Y. (2019). RNA secondary
structure prediction using an ensemble of two-dimensional deep neural
networks and transfer learning. Nat Commun, 10 (1), 5407.
doi:10.1038/s41467-019-13395-9
75 Fariselli, P., Martelli, P. L., Savojardo, C., & Casadio, R. (2015).
INPS: predicting the impact of non-synonymous variations on protein
stability from sequence. Bioinformatics, 31 (17), 2816-2821.
doi:10.1093/bioinformatics/btv291
76 Rodrigues, C. H. M., Myung, Y., Pires, D. E. V., & Ascher, D. B.
(2019). mCSM-PPI2: predicting the effects of mutations on
protein-protein interactions. Nucleic Acids Res, 47 (W1),
W338-W344. doi:10.1093/nar/gkz383
77 Ofoegbu, T. C., David, A., Kelley, L. A., Mezulis, S., Islam, S. A.,
Mersmann, S. F., et al. (2019). PhyreRisk: A Dynamic Web Application to
Bridge Genomics, Proteomics and 3D Structural Data to Guide
Interpretation of Human Genetic Variants. J Mol Biol, 431 (13),
2460-2466. doi:10.1016/j.jmb.2019.04.043
78 Buel, G. R., & Walters, K. J. (2022). Can AlphaFold2 predict the
impact of missense mutations on structure? Nat Struct Mol Biol,
29 (1), 1-2. doi:10.1038/s41594-021-00714-2
79 Shibata, A., Okuno, T., Rahman, M. A., Azuma, Y., Takeda, J., Masuda,
A., et al. (2016). IntSplice: prediction of the splicing consequences of
intronic single-nucleotide variations in the human genome. J Hum
Genet, 61 (7), 633-640. doi:10.1038/jhg.2016.23
80 Rehmat, N., Farooq, H., Kumar, S., Ul Hussain, S., & Naveed, H.
(2020). Predicting the pathogenicity of protein coding mutations using
Natural Language Processing. Annu Int Conf IEEE Eng Med Biol Soc,
2020 , 5842-5846. doi:10.1109/EMBC44109.2020.9175781
81 Inoue, I., Nakajima, T., Williams, C. S., Quackenbush, J., Puryear,
R., Powers, M., et al. (1997). A nucleotide substitution in the promoter
of human angiotensinogen is associated with essential hypertension and
affects basal transcription in vitro. J Clin Invest, 99 (7),
1786-1797. doi:10.1172/JCI119343
82 Ishigami, T., Umemura, S., Tamura, K., Hibi, K., Nyui, N., Kihara,
M., et al. (1997). Essential hypertension and 5’ upstream core promoter
region of human angiotensinogen gene. Hypertension, 30 (6),
1325-1330. doi:10.1161/01.hyp.30.6.1325
83 Cowell, J. K., Bia, B., & Akoulitchev, A. (1996). A novel mutation
in the promotor region in a family with a mild form of retinoblastoma
indicates the location of a new regulatory domain for the RB1 gene.Oncogene, 12 (2), 431-436.
84 Macias, M., Dean, M., Atkinson, A., Jimenez-Morales, S.,
Garcia-Vazquez, F. J., Saldana-Alvarez, Y., et al. (2008). Spectrum of
RB1 gene mutations and loss of heterozygosity in Mexican patients with
retinoblastoma: identification of six novel mutations. Cancer
Biomark, 4 (2), 93-99. doi:10.3233/cbm-2008-4205
85 Jankovic, L., Efremov, G. D., Petkov, G., Kattamis, C., George, E.,
Yang, K. G., et al. (1990). Two novel polyadenylation mutations leading
to beta(+)-thalassemia. Br J Haematol, 75 (1), 122-126.
doi:10.1111/j.1365-2141.1990.tb02627.x
86 Ho, P. J., Rochette, J., Fisher, C. A., Wonke, B., Jarvis, M. K.,
Yardumian, A., et al. (1996). Moderate reduction of beta-globin gene
transcript by a novel mutation in the 5’ untranslated region: a study of
its interaction with other genotypes in two families. Blood,
87 (3), 1170-1178.
87 Ho, P. J., Hall, G. W., Luo, L. Y., Weatherall, D. J., & Thein, S.
L. (1998). Beta-thalassaemia intermedia: is it possible consistently to
predict phenotype from genotype? Br J Haematol, 100 (1), 70-78.
doi:10.1046/j.1365-2141.1998.00519.x
88 Kazazian, H. H., Jr., & Boehm, C. D. (1988). Molecular basis and
prenatal diagnosis of beta-thalassemia. Blood, 72 (4), 1107-1116.
89 Waye, J. S., Eng, B., Patterson, M., Reis, M. D., Macdonald, D., &
Chui, D. H. (2001). Novel beta-thalassemia mutation in a
beta-thalassemia intermedia patient. Hemoglobin, 25 (1), 103-105.
doi:10.1081/hem-100103075
90 Morgado, A., Picanco, I., Gomes, S., Miranda, A., Coucelo, M.,
Seuanes, F., et al. (2007). Mutational spectrum of delta-globin gene in
the Portuguese population. Eur J Haematol, 79 (5), 422-428.
doi:10.1111/j.1600-0609.2007.00949.x
91 Hentze, M. W., & Kuhn, L. C. (1996). Molecular control of vertebrate
iron metabolism: mRNA-based regulatory circuits operated by iron, nitric
oxide, and oxidative stress. Proc Natl Acad Sci U S A, 93 (16),
8175-8182. doi:10.1073/pnas.93.16.8175
92 Martin, J. S., Halvorsen, M., Davis-Neulander, L., Ritz, J.,
Gopinath, C., Beauregard, A., et al. (2012). Structural effects of
linkage disequilibrium on the transcriptome. Rna, 18 (1), 77-87.
doi:10.1261/rna.029900.111
93 Millonig, G., Muckenthaler, M. U., & Mueller, S. (2010).
Hyperferritinaemia-cataract syndrome: worldwide mutations and phenotype
of an increasingly diagnosed genetic disorder. Hum Genomics,
4 (4), 250-262. doi:10.1186/1479-7364-4-4-250
94 Celma Nos, F., Hernandez, G., Ferrer-Cortes, X., Hernandez-Rodriguez,
I., Navarro-Almenzar, B., Fuster, J. L., et al. (2021). Hereditary
Hyperferritinemia Cataract Syndrome: Ferritin L Gene and Physiopathology
behind the Disease-Report of New Cases. Int J Mol Sci, 22 (11).
doi:10.3390/ijms22115451
95 Modell, B., & Darlison, M. (2008). Global epidemiology of
haemoglobin disorders and derived service indicators. Bull World
Health Organ, 86 (6), 480-487. doi:10.2471/blt.06.036673
96 Shriner, D., & Rotimi, C. N. (2018). Whole-Genome-Sequence-Based
Haplotypes Reveal Single Origin of the Sickle Allele during the Holocene
Wet Phase. Am J Hum Genet, 102 (4), 547-556.
doi:10.1016/j.ajhg.2018.02.003
97 Piccin, A., Murphy, C., Eakins, E., Rondinelli, M. B., Daves, M.,
Vecchiato, C., et al. (2019). Insight into the complex pathophysiology
of sickle cell anaemia and possible treatment. Eur J Haematol,
102 (4), 319-330. doi:10.1111/ejh.13212
98 Stiller, C. A., & Parkin, D. M. (1996). Geographic and ethnic
variations in the incidence of childhood cancer. Br Med Bull,
52 (4), 682-703. doi:10.1093/oxfordjournals.bmb.a011577
99 Zheng, J., Huang, X., Tan, W., Yu, D., Du, Z., Chang, J., et al.
(2016). Pancreatic cancer risk variant in LINC00673 creates a miR-1231
binding site and interferes with PTPN11 degradation. Nat Genet,
48 (7), 747-757. doi:10.1038/ng.3568
100 Pan, W., Zhou, L., Ge, M., Zhang, B., Yang, X., Xiong, X., et al.
(2016). Whole exome sequencing identifies lncRNA GAS8-AS1 and LPAR4 as
novel papillary thyroid carcinoma driver alternations. Hum Mol
Genet, 25 (9), 1875-1884. doi:10.1093/hmg/ddw056
101 Weiskopf, D., Schmitz, K. S., Raadsen, M. P., Grifoni, A., Okba, N.
M. A., Endeman, H., et al. (2020). Phenotype and kinetics of
SARS-CoV-2-specific T cells in COVID-19 patients with acute respiratory
distress syndrome. Sci Immunol, 5 (48).
doi:10.1126/sciimmunol.abd2071
102 Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., et al.
(2020). Clinical Characteristics of 138 Hospitalized Patients With 2019
Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA,
323 (11), 1061-1069. doi:10.1001/jama.2020.1585
103 Starr, T. N., Greaney, A. J., Hilton, S. K., Ellis, D., Crawford, K.
H. D., Dingens, A. S., et al. (2020). Deep Mutational Scanning of
SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and
ACE2 Binding. Cell, 182 (5), 1295-1310 e1220.
doi:10.1016/j.cell.2020.08.012
104 Makowski, L., Olson-Sidford, W., & J, W. W. (2021). Biological and
Clinical Consequences of Integrin Binding via a Rogue RGD Motif in the
SARS CoV-2 Spike Protein. Viruses, 13 (2). doi:10.3390/v13020146
105 Rizwan, T., Kothidar, A., Meghwani, H., Sharma, V., Shobhawat, R.,
Saini, R., et al. (2021). Comparative analysis of SARS-CoV-2 envelope
viroporin mutations from COVID-19 deceased and surviving patients
revealed implications on its ion-channel activities and correlation with
patient mortality. J Biomol Struct Dyn , 1-16.
doi:10.1080/07391102.2021.1944319
106 Lakshminarasimhan, M., Maldonado, M. T., Zhou, W., Fink, A. L., &
Wilson, M. A. (2008). Structural impact of three Parkinsonism-associated
missense mutations on human DJ-1. Biochemistry, 47 (5), 1381-1392.
doi:10.1021/bi701189c
107 Gorner, K., Holtorf, E., Waak, J., Pham, T. T., Vogt-Weisenhorn, D.
M., Wurst, W., et al. (2007). Structural determinants of the C-terminal
helix-kink-helix motif essential for protein stability and survival
promoting activity of DJ-1. J Biol Chem, 282 (18), 13680-13691.
doi:10.1074/jbc.M609821200
108 Bonifati, V., Rizzu, P., van Baren, M. J., Schaap, O., Breedveld, G.
J., Krieger, E., et al. (2003). Mutations in the DJ-1 gene associated
with autosomal recessive early-onset parkinsonism. Science,
299 (5604), 256-259. doi:10.1126/science.1077209
109 Hardiman, O., Al-Chalabi, A., Chio, A., Corr, E. M., Logroscino, G.,
Robberecht, W., et al. (2017). Amyotrophic lateral sclerosis. Nat
Rev Dis Primers, 3 , 17071. doi:10.1038/nrdp.2017.71
110 Cardoso, R. M., Thayer, M. M., DiDonato, M., Lo, T. P., Bruns, C.
K., Getzoff, E. D., et al. (2002). Insights into Lou Gehrig’s disease
from the structure and instability of the A4V mutant of human Cu,Zn
superoxide dismutase. J Mol Biol, 324 (2), 247-256.
doi:10.1016/s0022-2836(02)01090-2
111 Bruijn, L. I., Houseweart, M. K., Kato, S., Anderson, K. L.,
Anderson, S. D., Ohama, E., et al. (1998). Aggregation and motor neuron
toxicity of an ALS-linked SOD1 mutant independent from wild-type SOD1.Science, 281 (5384), 1851-1854. doi:10.1126/science.281.5384.1851
112 Bhattacharya, R., Rose, P. W., Burley, S. K., & Prlic, A. (2017).
Impact of genetic variation on three dimensional structure and function
of proteins. PLoS One, 12 (3), e0171355.
doi:10.1371/journal.pone.0171355
113 Saito, M., Ogasawara, M., Inaba, Y., Osawa, Y., Nishioka, M.,
Yamauchi, S., et al. (2022). Successful treatment of congenital
myasthenic syndrome caused by a novel compound heterozygous variant in
RAPSN. Brain Dev, 44 (1), 50-55.
doi:10.1016/j.braindev.2021.09.001
114 Estephan, E. P., Zambon, A. A., Thompson, R., Polavarapu, K., Jomaa,
D., Topf, A., et al. (2022). Congenital myasthenic syndrome: Correlation
between clinical features and molecular diagnosis. Eur J Neurol,
29 (3), 833-842. doi:10.1111/ene.15173
115 Sun, B., He, Z. Q., Li, Y. R., Bai, J. M., Wang, H. R., Wang, H. F.,
et al. (2022). Screening for SH3TC2 variants in Charcot-Marie-Tooth
disease in a cohort of Chinese patients. Acta Neurol Belg,
122 (5), 1169-1175. doi:10.1007/s13760-021-01605-5
116 Xie, W., Nangle, L. A., Zhang, W., Schimmel, P., & Yang, X. L.
(2007). Long-range structural effects of a Charcot-Marie-Tooth
disease-causing mutation in human glycyl-tRNA synthetase. Proc
Natl Acad Sci U S A, 104 (24), 9976-9981. doi:10.1073/pnas.0703908104
117 Gainza, P., Sverrisson, F., Monti, F., Rodola, E., Boscaini, D.,
Bronstein, M. M., et al. (2020). Deciphering interaction fingerprints
from protein molecular surfaces using geometric deep learning. Nat
Methods, 17 (2), 184-192. doi:10.1038/s41592-019-0666-6
118 Zhou, J. (2022). Sequence-based modeling of three-dimensional genome
architecture from kilobase to chromosome scale. Nat Genet, 54 (5),
725-734. doi:10.1038/s41588-022-01065-4