Roles of statistical modeling in characterizing the genetic basis of human diseases and traits

References

[1] Klein J., R. Y., Zeiss Y., C. S., Chew K., E. P., Tsai M., J. T.. Complement factor H polymorphism in age-related macular degeneration. Science, 2005, 308 : 385– 389

[2] Yengo E., L. R., Sidorenko N., J. M., Kemper M.. Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry. Hum. Mol. Genet., 2018, 27 : 3641– 3649

[3] Hall J. M., Lee, M. K., Newman, B., Morrow, J. E., Anderson, L. A., Huey, B. , King, M. C.. Linkage of early-onset familial breast cancer to chromosome 17q21. Science, 1990, 250 : 1684– 1689

[4] Risch N. , Merikangas, K.. The future of genetic studies of complex human diseases. Science, 1996, 273 : 1516– 1517

[5] Spielman R. S., McGinnis, R. E. , Ewens, W. J.. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am. J. Hum. Genet., 1993, 52 : 506– 516

[6] Zhang S., Zhu, X. , Zhao, H.. On a semiparametric test to detect associations between quantitative traits and candidate genes using unrelated individuals. Genet. Epidemiol., 2003, 24 : 44– 56

[7] Price A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A. , Reich, D.. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet., 2006, 38 : 904– 909

[8] International HapMap Consortium. The International HapMap Project. Nature, 2003, 426 : 789– 796

[9] Manolio A., T. S., Collins J., F. B., Cox A., N. J., Goldstein I., D. M., Hindorff R.. Finding the missing heritability of complex diseases. Nature, 2009, 461 : 747– 753

[10] Yang P., J. K., Benyamin R., B. A., McEvoy C., B. G., Gordon W.. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet., 2010, 42 : 565– 569

[11] Fisher R. A.. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb., 1918, 52 : 399– 433

[12] Jiang J., Li, C., Paul, D., Yang, C. , Zhao, H.. On high-dimensional misspecified mixed model analysis in genome-wide association study. Ann. Stat., 2016, 44 : 2127– 2160

[13] Speed D., Cai, N., Johnson, M. R., Nejentsev, S. , Balding, D. J. , the UCLEB Consortium.. Reevaluation of SNP heritability in complex human traits. Nat. Genet., 2017, 49 : 986– 992

[14] Pasaniuc B. , Price, A. L.. Dissecting the genetics of complex traits using summary association statistics. Nat. Rev. Genet., 2017, 18 : 117– 127

[15] Bulik-Sullivan K., B. R., Loh K., P. J., Finucane L., H. M., Ripke Schizophrenia Working Group of the Psychiatric Genomics Consortium. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet., 2015, 47 : 291– 295

[16] Lu Q., Powles, R. L., Abdallah, S., Ou, D., Wang, Q., Hu, Y., Lu, Y., Liu, W., Li, B., Mukherjee, S.. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease. PLoS Genet., 2017, 13 : e1006933–

[17] Finucane K., H. R.. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet., 2015, 47 : 1228– 1235

[18] Lu Q., Powles, R. L., Wang, Q., He, B. J. , Zhao, H.. Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLoS Genet., 2016, 12 : e1005947–

[19] Lee S. H., Yang, J., Goddard, M. E., Visscher, P. M. , Wray, N. R.. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics, 2012, 28 : 2540– 2542

[20] Bulik-Sullivan K., B. R., Finucane R., H. R., Anttila B.. An atlas of genetic correlations across human diseases and traits. Nat. Genet., 2015, 47 : 1236– 1241

[21] van Rheenen W., Peyrot, W. J., Schork, A. J., Lee, S. H. , Wray, N. R.. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet., 2019, 20 : 567– 581

[22] Lu Q., Li, B., Ou, D., Erlendsdottir, M., Powles, R. L., Jiang, T., Hu, Y., Chang, D., Jin, C., Dai, W.. A powerful approach to estimating annotation-stratified genetic covariance via GWAS summary statistics. Am. J. Hum. Genet., 2017, 101 : 939– 964

[23] Zhang Y., Lu, Q., Ye, Y., Huang, K., Liu, W., Wu, Y., Zhong, X., Li, B., Yu, Z., Travers, B. G.. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol., 2021, 22 : 262–

[24] Guo C., H. P., Hou Q. ( 2021) Quantifying concordant genetic effects of de novo mutations on multiple disorders. bioRxiv, 2021.06.13.448234

[25] Turley K., P. J., Walters A., R. A., Maghzian A.. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet., 2018, 50 : 229– 237

[26] Xie Y., Li, M., Dong, W., Jiang, W. , Zhao, H.. M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits. medRxive, 2021,

[27] Kang J., Cho, J. , Zhao, H.. Practical issues in building risk-predicting models for complex diseases. J. Biopharm. Stat., 2010, 20 : 415– 440

[28] Vilhjálmsson J., B. K., Yang R.. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet., 2015, 97 : 576– 592

[29] Hu Y., Lu, Q., Powles, R., Yao, X., Yang, C., Fang, F., Xu, X. , Zhao, H.. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Comput. Biol., 2017, 13 : e1005589–

[30] Li C., Yang, C., Gelernter, J. , Zhao, H.. Improving genetic risk prediction by leveraging pleiotropy. Hum. Genet., 2014, 133 : 639– 650

[31] Hu Y., Lu, Q., Liu, W., Zhang, Y., Li, M. , Zhao, H.. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genet., 2017, 13 : e1006836–

[32] Ge T., Chen, C. Y., Ni, Y., Feng, Y. A. , Smoller, J. W.. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun., 2019, 10 : 1776–

[33] Song S., Jiang, W., Hou, L. , Zhao, H.. Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies. PLoS Comput. Biol., 2020, 16 : e1007565–

[34] Zhou G. , Zhao, H.. A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics. PLoS Genet., 2021, 17 : e1009697–

[35] Consortium E. P. , the ENCODE Project Consortium. ( 2004) The ENCODE (ENCyclopedia Of DNA Elements) Project. Science, 306, 636– 640

[36] Bernstein E., B. A., Stamatoyannopoulos F., J. A., Costello L., J. R.. The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol., 2010, 28 : 1045– 1048

[37] Lonsdale . The genotype-tissue expression (GTEx) project. Nat. Genet., 2013, 45 : 580– 585

[38] Gusev W., A. Geus, Ko J., A. I., Shi A.. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet., 2016, 48 : 245– 252

[39] Gamazon R., E. E., Wheeler P., H. V., Shah J., K. E., Mozaffari C., S. L., Aquino-Michaels J.. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet., 2015, 47 : 1091– 1098

[40] Hu Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S. M., Yu, Z., Li, B., Gu, J., Muchnik, S.. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat. Genet., 2019, 51 : 568– 576

[41] Liu W., Li, M., Zhang, W., Zhou, G., Wu, X., Wang, J., Lu, Q. , Zhao, H.. Leveraging functional annotation to identify genes associated with complex diseases. PLoS Comput. Biol., 2020, 16 : e1008315–

[42] Li M., X. K.. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat. Genet., 2020, 52 : 969– 983

[43] Chung D., Yang, C., Li, C., Gelernter, J. , Zhao, H.. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet., 2014, 10 : e1004787–

[44] Chen M., Cho, J. , Zhao, H.. Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genet., 2011, 7 : e1001353–

[45] Hou L., Chen, M., Zhang, C. K., Cho, J. , Zhao, H.. Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies. Hum. Mol. Genet., 2014, 23 : 2780– 2790

[46] Lu Q., Yao, X., Hu, Y. , Zhao, H.. GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. Bioinformatics, 2016, 32 : 542– 548

[47] Weissbrod P., O. de Geijn. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet., 2020, 52 : 1355– 1363

[48] Hutchinson A., Asimit, J. , Wallace, C.. Fine-mapping genetic associations. Hum. Mol. Genet., 2020, 29 : R81– R88

[49] Rozenblatt-Rosen J. T., O. A. The Human Cell Atlas: from vision to reality. Nature, 2017, 550 : 451– 453

[50] Dekker S., J. O., Belmont T., A. A., Guttman C., M. J.. The 4D nucleome project. Nature, 2017, 549 : 219– 226

[51] Katan M. B.. Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet, 1986, 327 : 507– 508

[52] Smith G. D. , Ebrahim, S.. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol., 2003, 32 : 1– 22

[53] Slob E. A. W. , Burgess, S.. A comparison of robust Mendelian randomization methods using summary data. Genet. Epidemiol., 2020, 44 : 313– 329

[54] Bodmer W. , Bonilla, C.. Common and rare variants in multifactorial susceptibility to common diseases. Nat. Genet., 2008, 40 : 695– 701

[55] Crouch D. J. M. , Bodmer, W. F.. Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants. Proc. Natl. Acad. Sci. USA, 2020, 117 : 18924– 18933

[56] Zhao J., J. M., Akinsanmi M.. A burden of rare variants associated with extremes of gene expression in human peripheral blood. Am. J. Hum. Genet., 2016, 98 : 299– 309

[57] Li K., X. R., Kim N., Y. T., Tsang J., E. J.. The impact of rare variation on gene expression across tissues. Nature, 2017, 550 : 239– 243

[58] So H. C., Chau, C. K., Chiu, W. T., Ho, K. S., Lo, C. P., Yim, S. H. , Sham, P. C.. Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry. Nat. Neurosci., 2017, 20 : 1342– 1349

[59] Duncan L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., Peterson, R. , Domingue, B.. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun., 2019, 10 : 3328–

[60] Martin A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M. , Daly, M. J.. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet., 2019, 51 : 584– 591

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