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Detecting disease-related SNP loci based on GSP

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Abstract

A large number of studies have shown that susceptibility to some diseases may be related to some SNP (Single Nucleotide Polymorphism) loci. Accurate location of disease-related SNP loci can help people understand the pathogenesis of diseases and prevent them from happening. Based on Graph Signal Processing (GSP) theory, this paper proposes a novel method called GSP to detect disease-related SNP loci. GSP method is divided into five steps. The first step is to establish a graph signal model of all SNP loci. The second step is to extract the high-frequency components of graph signal with a high-pass filter. The third step is to transform the filtered graph signal with graph Fourier transformation. The fourth step is to extract the graph signal corresponding to high frequency with inverse graph Fourier transformation. The fifth step is to transform the extracted signal with Gaussian distribution and use Pauta criterion to screen out disease-related SNP loci. The experimental results show that GSP method can detect all disease-related SNP loci of simulation data and Genetic Disease A (GDA) and three of the four disease-related SNP loci of Age-related Macular Degeneration (AMD) with excellent performance. GSP method provides a new idea for the identification of pathogenicity loci.

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References

  • Chen LJ, Liu DTL, Tam POS, Chan WM, Liu K, Chong KKL, Pang CP (2007) Association of complement factor H polymorphisms with exudative age-related macular degeneration. Am J Ophthalmol 143(2):376

    Article  Google Scholar 

  • Chen S, Varma R, Sandryhaila A, Kovacevic J (2015) Discrete signal processing on graphs: sampling theory. IEEE Trans Signal Process 63(24):6510–6523

    Article  MathSciNet  MATH  Google Scholar 

  • Cordell HJ (2009) Detecting gene–gene interactions that underlie human diseases. Nat Rev Genet 10(6):392–404

    Article  Google Scholar 

  • Ding X, Wang J, Zelikovsky A, Guo X, Xie M, Pan Y (2014) Searching high-order SNP combinations for complex diseases based on energy distribution difference. IEEE/ACM Trans Comput Biol Bioinf 12(3):695–704

    Article  Google Scholar 

  • Dong L, Qu Y, Jiang H, Dai H, Zhou F, Xu X, Dang G (2011) Correlation of complement factor H gene polymorphisms with exudative age-related macular degeneration in a Chinese cohort. Neurosci Lett 488(3):283–287

    Article  Google Scholar 

  • Fang Y, Zaiqiang Ku (2019) Analysis of pathogenic genetic loci based on several machine learning algorithms. J Huang Normal Univ 39(3):1–5

    Google Scholar 

  • Feng ZZ, Yang X, Subedi S, McNicholas PD (2012) The LASSO and sparse least squares regression methods for SNP selection in predicting quantitative traits. IEEE/ACM Trans Comput Biol Bioinf 9(2):629–636

    Article  Google Scholar 

  • Guo X, Meng Y, Yu N, Pan Y (2014) Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering. BMC Bioinform 15(1):102

    Article  Google Scholar 

  • He J, Zelikovsky A (2006) Multiple linear regression for index SNP selection on unphased genotypes. In: Proceedings of the 2006 international conference of the IEEE engineering in medicine and biology society, New York City, USA, Aug 30–Sept 3, 2006. IEEE press, pp 5759–5762. https://doi.org/10.1109/iembs.2006.259408

  • Jian C et al (2017) The analysis of hereditary disease and characters. Math Pract Theory 47(14):78–88

    Google Scholar 

  • Kim D, Uhmn S, Kim J (2012) Finding relevant SNP sets and predicting disease risk using simulated annealing. Int J Softw Eng Its Appl 6(3):81

    Google Scholar 

  • Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Bracken MB (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308(5720):385–389

    Article  Google Scholar 

  • Kopplin LJ, Igo RP, Wang Y, Sivakumaran TA, Hagstrom SA, Peachey NS, Pauer GJ (2010) Genome-wide association identifies SKIV2L and MYRIP as protective factors for age-related macular degeneration. Genes Immun 11(8):609–621

    Article  Google Scholar 

  • Lin WY, Lee WC (2010) Incorporating prior knowledge to facilitate discoveries in a genome-wide association study on age-related macular degeneration. BMC Res Notes 3(1):26

    Article  Google Scholar 

  • Lin D, Li J, Calhoun VD, Wang YP (2015) Detection of genetic factors associated with multiple correlated imaging phenotypes by a sparse regression model. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE, pp 1368–1371

  • Liu Z, Lin X (2018) Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 74(1):165–175

    Article  MathSciNet  MATH  Google Scholar 

  • Liu X, Zhao P, Tang S, Lu F, Hu J, Lei C, Zhang D (2010) Association study of complement factor H, C2, CFB, and C3 and age-related macular degeneration in a Han Chinese population. Retina 30(8):1177–1184

    Article  Google Scholar 

  • Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37(4):413–417

    Article  Google Scholar 

  • Marttinen P, Pirinen M, Sarin AP, Gillberg J, Kettunen J, Surakka I, Kähönen M (2014) Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression. Bioinformatics 30(14):2026–2034

    Article  Google Scholar 

  • Ng TK, Chen LJ, Liu DT, Tam PO, Chan WM, Liu K, Lam DS (2008) Multiple gene polymorphisms in the complement factor h gene are associated with exudative age-related macular degeneration in chinese. Invest Ophthalmol Vis Sci 49(8):3312–3317

    Article  Google Scholar 

  • Ray D, Boehnke M (2018) Methods for meta-analysis of multiple traits using GWAS summary statistics. Genet Epidemiol 42(2):134–145

    Article  Google Scholar 

  • Ried JS, Chu AY, Bragg-Gresham JL, Van Dongen J, Huffman JE, Ahluwalia TS, Feitosa MF (2016) A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape. Nat Commun 7(1):1–11

    Article  Google Scholar 

  • Sandryhaila A, Moura JM (2014) Discrete signal processing on graphs: frequency analysis. IEEE Trans Signal Process 62(12):3042–3054

    Article  MathSciNet  MATH  Google Scholar 

  • Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag 30(3):83–98

    Article  Google Scholar 

  • Sivakumaran TA, Igo RP Jr, Kidd JM, Itsara A, Kopplin LJ, Chen W, Hagstrom SA, Peachey NS, Francis PJ, Klein ML, Chew EY, Ramprasad VL, Tay W-T, Mitchell P, Seielstad M, Stambolian DE, Edwards AO, Lee KE, Leontiev DV, Jun G, Wang Y, Tian L, Qiu F, Henning AK, LaFramboise T, Sen P, Aarthi M, George R, Raman R, Das MK, Vijaya L, Kumaramanickavel G, Wong TY, Swaroop A, Abecasis GR, Klein R, Klein BEK, Nickerson DA, Eichler EE, Iyengar SK (2011) A 32 kb critical region excluding Y402H in CFH mediates risk for age-related macular degeneration. PLoS ONE 6(10):e25598. https://doi.org/10.1371/journal.pone.0025598

    Article  Google Scholar 

  • Songshan Y, Jiawei W, Scott TE, Yaqun W, Dajiang JL, Rongling W, Runze L, Xiang Z (2020) Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa229

    Article  Google Scholar 

  • Speed D, Balding DJ (2019) SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat Genet 51(2):277–284

    Article  Google Scholar 

  • Wang Y, Liu X, Robbins K, Rekaya R (2010) AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Res Notes 3(1):117

    Article  Google Scholar 

  • Wu TT, Chen YF, Hastie T, Sobel E, Lange K (2009) Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25(6):714–721

    Article  Google Scholar 

  • Wu SJ, Chuang LY, Lin YD, Ho WH, Chiang FT, Yang CH, Chang HW (2013) Particle swarm optimization algorithm for analyzing SNP–SNP interaction of renin-angiotensin system genes against hypertension. Mol Biol Rep 40(7):4227–4233

    Article  Google Scholar 

  • Yang CH, Lin YD, Chuang LY, Chang HW (2013) Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes. IEEE/ACM Trans Comput Biol Bioinf 10(2):361–371

    Article  Google Scholar 

  • Zhan X, Zhao N, Plantinga A, Thornton TA, Conneely KN, Epstein MP, Wu MC (2017) Powerful genetic association analysis for common or rare variants with high-dimensional structured traits. Genetics 206(4):1779–1790

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi, China

    Qinli Zhang, Jianhong Wang, Xuecai Yu & Xiulan Yang

  2. College of Sciences and Arts, North China Institute of Aerospace Engineering, Langfang, Hebei, China

    Zhichao Jiang

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  1. Qinli Zhang
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  2. Zhichao Jiang
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  3. Jianhong Wang
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  4. Xuecai Yu
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  5. Xiulan Yang
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Correspondence to Qinli Zhang.

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Zhang, Q., Jiang, Z., Wang, J. et al. Detecting disease-related SNP loci based on GSP. Netw Model Anal Health Inform Bioinforma 9, 47 (2020). https://doi.org/10.1007/s13721-020-00254-7

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  • DOI: https://doi.org/10.1007/s13721-020-00254-7

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