Advances and challenges in quantitative delineation of the genetic architecture of complex traits

Hua Tang , Zihuai He

Quant. Biol. ›› 2021, Vol. 9 ›› Issue (2) : 168 -184.

PDF (1088KB)
Quant. Biol. ›› 2021, Vol. 9 ›› Issue (2) : 168 -184. DOI: 10.15302/J-QB-021-0249
REVIEW
REVIEW

Advances and challenges in quantitative delineation of the genetic architecture of complex traits

Author information +
History +
PDF (1088KB)

Abstract

Background: Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.

Results: This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.

Conclusion: GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.

Graphical abstract

Keywords

genome-wide association study / heritability / rare variants / biobank / colocalization / eQTL / polygenic risk scores / transcriptome-wide association study

Cite this article

Download citation ▾
Hua Tang, Zihuai He. Advances and challenges in quantitative delineation of the genetic architecture of complex traits. Quant. Biol., 2021, 9(2): 168-184 DOI:10.15302/J-QB-021-0249

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

MacArthur, J., Bowler, E., Cerezo, M., Gil, L., Hall, P., Hastings, E., Junkins, H., McMahon, A., Milano, A., Morales, J., (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res., 45, D896–D901

[2]

Crawford, N. G., KellyD. E. , Hansen, M. E. B., Beltrame, M. H., Fan, S., Bowman, S. L., Jewett, E., Ranciaro, A., Thompson, S., Lo, Y., (2017) Loci associated with skin pigmentation identified in African populations. Science, 358, eaan8433

[3]

Denny, J. C., Bastarache, L., Ritchie, M. D., Carroll, R. J., Zink, R., Mosley, J. D., Field, J. R., Pulley, J. M., Ramirez, A. H., Bowton, E., (2013) Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol., 31, 1102–1110

[4]

Bush, W. S., Oetjens, M. T. and Crawford, D. C. (2016) Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet., 17, 129–145

[5]

Smith, G. D. and Hemani, G. (2014) Mendelian randomization: Geneticanchorsfor causal inference in epidemiological studies. Hum. Mol. Genet., 23, R89–R98

[6]

Pingault, J. B., O’Reilly, P. F., Schoeler, T., Ploubidis, G. B., Rijsdijk, F. and Dudbridge, F. (2018) Using genetic data to strengthen causal inference in observational research. Nat. Rev. Genet., 19, 566–580

[7]

Visscher, P. M., Brown, M. A., McCarthy, M. I. and Yang, J. (2012) Five years of GWAS discovery. Am. J. Hum. Genet., 90, 7–24

[8]

Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A. and Yang, J. (2017) 10 Years of GWAS Discovery: Biology, Function, and Translation. Am. J. Hum. Genet., 101, 5–22

[9]

McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, 2nd Ed. (Monographs on Statistics and Applied Probability). Chapman and Hall/CRC

[10]

Wellcome, T., Case, T. and Consortium, C. (2007) Genomewide association study of 14, 000 cases of seven common diseases and 3, 000 shared controls Supplementary Information. Nature, 447,661-78.

[11]

Pe’er, I., Yelensky, R., Altshuler, D. and Daly, M. J. (2008) Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol., 32, 381–385

[12]

Yang, J., Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., Weedon, M. N., Loos, R. J., (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet., 44, 369–375, S1–S3

[13]

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

[14]

Pickrell, J. K. (2014) Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet., 94, 559–573

[15]

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

[16]

He, X., Fuller, C. K., Song, Y., Meng, Q., Zhang, B., Yang, X. and Li, H. (2013) Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am. J. Hum. Genet., 92, 667–680

[17]

Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C. and Plagnol, V. (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet., 10, e1004383

[18]

Hormozdiari, F., van de Bunt, M., Segrè A. V., Li, X., Joo, J. W. J., Bilow, M., Sul, J. H., Sankararaman, S., Pasaniuc, B. and Eskin, E. (2016) Colocalization of GWAS and eQTL Signals Detects Target Genes. Am. J. Hum. Genet., 99, 1245–1260

[19]

Giambartolomei, C., Zhenli Liu, J., Zhang, W., Hauberg, M., Shi, H., Boocock, J., Pickrell, J., Jaffe, A. E., Pasaniuc, B. and Roussos, P., (2018) A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics, 34, 2538–2545

[20]

Wen, X., Pique-Regi, R. and Luca, F. (2017) Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization. PLoS Genet., 13, e1006646

[21]

Finucane, H. K., Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P. R., Anttila, V., Xu, H., Zang, C., Farh, K., (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet., 47, 1228–1235

[22]

Cordell, H. J. (2002) Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum. Mol. Genet., 11, 2463–2468

[23]

Noordam, R., Bos, M. M., Wang, H., Winkler, T. W., Bentley, A. R., Kilpeläinen, T. O., de Vries, P. S., Sung, Y. J., Schwander, K., Cade, B. E., (2019) Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration. Nat. Commun., 10, 5121

[24]

Bentley, A. R., Sung, Y. J., Brown, M. R., Winkler, T. W., Kraja, A. T., Ntalla, I., Schwander, K., Chasman, D. I., Lim, E., Deng, X., (2019) Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat. Genet., 51, 636–648

[25]

Wei, W. H., Hemani, G. and Haley, C. S. (2014) Detecting epistasis in human complex traits. Nat. Rev. Genet., 15, 722–733

[26]

Crawford, L., Zeng, P., Mukherjee, S. and Zhou, X. (2017) Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS Genet., 13, e1006869

[27]

Fang, G., Wang, W., Paunic, V., Heydari, H., Costanzo, M., Liu, X., Liu, X., VanderSluis, B., Oately, B., Steinbach, M., (2019) Discovering genetic interactions bridging pathways in genome-wide association studies. Nat. Commun., 10, 4274

[28]

Bis, J. C., Jian, X., Kunkle, B. W., Chen, Y., Hamilton-Nelson, K. L., Bush, W. S., Salerno, W. J., Lancour, D., Ma, Y., Renton, A. E., (2020) Whole exome sequencing study identifies novel rare and common Alzheimer’s-Associated variants involved in immune response and transcriptional regulation. Mol. Psychiatry, 25, 1859–1875

[29]

Marouli, E., Graff, M., Medina-Gomez, C., Lo, K. S., Wood, A. R., Kjaer, T. R., Fine, R. S., Lu, Y., Schurmann, C., Highland, H. M., (2017) Rare and low-frequency coding variants alter human adult height. Nature, 542, 186–190

[30]

Zhao, Z., Bi, W., Zhou, W., VandeHaar, P., Fritsche, L. G. and Lee, S. (2020) UK Biobank whole-exome sequence binary phenome analysis with robust region-based rare-variant test. Am. J. Hum. Genet., 106, 3–12

[31]

Altshuler, D. M., Gibbs, R. A., Peltonen, L., Altshuler, D. M., Gibbs, R. A., Peltonen, L., Dermitzakis, E., Schaffner, S. F., Yu, F., Peltonen, L., (2010) Integrating common and rare genetic variation in diverse human populations. Nature, 467, 52–58

[32]

Hoffmann, T. J., Zhan, Y., Kvale, M. N., Hesselson, S. E., Gollub, J., Iribarren, C., Lu, Y., Mei, G., Purdy, M. M., Quesenberry, C., (2011) Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm. Genomics, 98, 422–430

[33]

Hoffmann, T. J., Kvale, M. N., Hesselson, S. E., Zhan, Y., Aquino, C., Cao, Y., Cawley, S., Chung, E., Connell, S., Eshragh, J., (2011) Next generation genome-wide association tool: design and coverage of a high-throughput European-optimized SNP array. Genomics, 98, 79–89

[34]

Hunter-Zinck, H., Shi, Y., Li, M., Gorman, B. R., Ji, S. G., Sun, N., Webster, T., Liem, A., Hsieh, P., Devineni, P., (2020) Genotyping array design and data quality control in the Million Veteran Program. Am. J. Hum. Genet., 106, 535–548

[35]

Bien, S. A., Wojcik, G. L., Zubair, N., Gignoux, C. R., Martin, A. R., Kocarnik, J. M., Martin, L. W., Buyske, S., Haessler, J., Walker, R. W., (2016) Strategies for enriching variant coverage in candidate disease loci on a multiethnic genotyping array. PLoS One, 11, e0167758

[36]

Das, S., Abecasis, G. R. and Browning, B. L. (2018) Genotype imputation from large reference panels. Annu. Rev. Genomics Hum. Genet., 19, 73–96

[37]

McCarthy, S., Das, S., Kretzschmar, W., Delaneau, O., Wood, A. R., Teumer, A., Kang, H. M., Fuchsberger, C., Danecek, P., Sharp, K., (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet., 48, 1279–1283

[38]

Howie, B., Marchini, J. and Stephens, M. (2011) Genotype imputation with thousands of genomes. G3- Genes, Genomes, Genet., 1, 457–470

[39]

Li, Y., Willer, C., Sanna, S. and Abecasis, G. (2009) Genotype imputation. Annu. Rev. Genomics Hum. Genet., 10, 387–406

[40]

Knowler, W. C., Williams, R. C., Pettitt, D. J. and Steinberg, A. G. (1988) Gm3;5,13,14 and type 2 diabetes mellitus: an association in American Indians with genetic admixture. Am. J. Hum. Genet. 43, 520–526

[41]

Campbell, C. D., Ogburn, E. L., Lunetta, K. L., Lyon, H. N., Freedman, M. L., Groop, L. C., Altshuler, D., Ardlie, K. G. and Hirschhorn, J. N. (2005) Demonstrating stratification in a European American population. Nat. Genet., 37, 868–872

[42]

Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S. Y., Freimer, N. B., Sabatti, C. and Eskin, E. (2010) Variance component model to account for sample structure in genome-wide association studies. Nat. Genet., 42, 348–354

[43]

Zhou, X. and Stephens, M. (2012) Genome-wide efficient mixed-model analysis for association studies. Nat. Genet., 44, 821–824

[44]

Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I. and Heckerman, D. (2011) FaST linear mixed models for genome-wide association studies. Nat. Methods, 8, 833–835

[45]

Listgarten, J., Lippert, C. and Heckerman, D. (2013) FaST-LMM-Select for addressing confounding from spatial structure and rare variants. Nat. Genet., 45, 470–471

[46]

Bulik-Sullivan, B. K., Loh, P. R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L. and Neale, B. M., (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet., 47, 291–295

[47]

Bacanu, S. A., Devlin, B. and Roeder, K. (2000) The power of genomic control. Am. J. Hum. Genet., 66, 1933–1944

[48]

Falconer, D. S. and Mackay, T. F. C. (1962) Introduction to Quantitative Genetics. Benjamin-Cummings Pub Co

[49]

Haseman, J. K. and Elston, R. C. (1972) The investigation of linkage between a quantitative trait and a marker locus. Behav. Genet., 2, 3–19

[50]

Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., (2009) Finding the missing heritability of complex diseases. Nature, 461, 747–753

[51]

Silventoinen, K., Sammalisto, S., Perola, M., Boomsma, D. I., Cornes, B. K., Davis, C., Dunkel, L., De Lange, M., Harris, J. R., Hjelmborg, J. V., (2003) Heritability of adult body height: a comparative study of twin cohorts in eight countries. Twin Res., 6, 399–408

[52]

Gudbjartsson, D. F., Walters, G. B., Thorleifsson, G., Stefansson, H., Halldorsson, B. V., Zusmanovich, P., Sulem, P., Thorlacius, S., Gylfason, A., Steinberg, S., (2008) Many sequence variants affecting diversity of adult human height. Nat. Genet., 40, 609–615

[53]

Lee, S. H., Wray, N. R., Goddard, M. E. and Visscher, P. M. (2011) Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet., 88, 294–305

[54]

Tenesa, A. and Haley, C. S. (2013) The heritability of human disease: estimation, uses and abuses. Nat. Rev. Genet., 14, 139–149

[55]

Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., (2010) Common SNPs explain a large proportion of the heritability for human height. Nat. Genet., 42, 565–569

[56]

Lettre, G., Jackson, A. U., Gieger, C., Schumacher, F. R., Berndt, S. I., Sanna, S., Eyheramendy, S., Voight, B. F., Butler, J. L., Guiducci, C., (2008) Identification of ten loci associated with height highlights new biological pathways in human growth. Nat. Genet., 40, 584–591

[57]

Weedon, M. N., Lango, H., Lindgren, C. M., Wallace, C., Evans, D. M., Mangino, M., Freathy, R. M., Perry, J. R., Stevens, S., Hall, A. S., (2008) Genome-wide association analysis identifies 20 loci that influence adult height. Nat. Genet., 40, 575–583

[58]

Fisher, R. A. (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb., 53, 399–433

[59]

Yengo, L., Sidorenko, J., Kemper, K. E., Zheng, Z., Wood, A. R., Weedon, M. N., Frayling, T. M., Hirschhorn, J., Yang, J. and Visscher, P. M., (2018) Meta-analysis of genome-wide association studies for height and body mass index in ~700, 000 individuals of European ancestry. Hum. Mol. Genet., 27, 3641–3649

[60]

Loh, P. R., Bhatia, G., Gusev, A., Finucane, H. K., Bulik-Sullivan, B. K., Pollack, S. J., de Candia, T. R., Lee, S. H., Wray, N. R., Kendler, K. S., (2015) Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat. Genet., 47, 1385–1392

[61]

Boyle, E. A., Li, Y. I. and Pritchard, J. K. (2017) An expanded view of complex traits: from polygenic to omnigenic. Cell, 169, 1177–1186

[62]

Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J. and Visscher, P. M. (2018) Common disease is more complex than implied by the core gene omnigenic model. Cell, 173, 1573–1580

[63]

Pritchard, J. K. and Cox, N. J. (2002) The allelic architecture of human disease genes: common disease-common variant...or not? Hum. Mol. Genet., 11, 2417–2423

[64]

Botstein, D. and Risch, N. (2003) Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat. Genet., 33, 228–237

[65]

Schork, N. J., Murray, S. S., Frazer, K. A. and Topol, E. J. (2009) Common vs. rare allele hypotheses for complex diseases. Curr. Opin. Genet. Dev., 19, 212–219

[66]

Gibson, G. (2012) Rare and common variants: twenty arguments. Nat. Rev. Genet., 13, 135–145

[67]

Bomba, L., Walter, K. and Soranzo, N. (2017) The impact of rare and low-frequency genetic variants in common disease. Genome Biol., 18, 77

[68]

Wainschtein, P., Jain, D. P., Yengo, L., Zheng, Z., TOPMed Anthropometry Working Group, Trans-Omics for Precision Medicine Consortium, Adrienne Cupples, L., Shadyab, A. H., McKnight, B., Shoemaker, B. M., (2019) Recovery of trait heritability from whole genome sequence data. BioRxiv doi: 10.1101/588020

[69]

Young, A. I. (2019) Solving the missing heritability problem. PLoS Genet., 15, e1008222

[70]

Lindblad-Toh, K., Garber, M., Zuk, O., Lin, M. F., Parker, B. J., Washietl, S., Kheradpour, P., Ernst, J., Jordan, G., Mauceli, E., (2011) A high-resolution map of human evolutionary constraint using 29 mammals. Nature, 478, 476–482

[71]

Khurana, E., Fu, Y., Colonna, V., Mu, X., Kang, H. M., Lappalainen, T., Sboner, A., Lochovsky, L., Chen, J., Harmanci, A., (2013) Integrative annotation of variants from 1092 humans: Application to cancer genomics. Science, 342, 1235587

[72]

ENCODE Project Consortium. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57–74

[73]

Altshuler, D., Daly, M. J. and Lander, E. S. (2008) Genetic mapping in human disease. Science, 322, 881–888

[74]

Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S. and Manolio, T. A. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA, 106, 9362–9367

[75]

Gusev, A., Lee, S. H., Trynka, G., Finucane, H., Vilhjálmsson, B. J., Xu, H., Zang, C., Ripke, S., Bulik-Sullivan, B., Stahl, E., (2014) Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet., 95, 535–552

[76]

Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S. and Sunyaev, S. R. (2010) A method and server for predicting damaging missense mutations. Nat. Methods, 7, 248–249

[77]

Ng, P. C. and Henikoff, S. (2003) SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res., 31, 3812–3814

[78]

Kircher, M., Witten, D. M., Jain, P., O’Roak, B. J., Cooper, G. M. and Shendure, J. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet., 46, 310–315

[79]

Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., Ziller, M. J., (2015) Integrative analysis of 111 reference human epigenomes. Nature, 518, 317–330

[80]

The GTEx Consortium. (2015) The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science,348, 648–660

[81]

Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., (2019) Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet., 51, 404–413

[82]

Auton, A., Brooks, L. D., Durbin, R. M., Garrison, E. P., Kang, H. M., Korbel, J. O., Marchini, J. L., McCarthy, S., McVean, G. A., Abecasis, G. R., (2015) A global reference for human genetic variation. Nature, 526, 68–74

[83]

Zhu, X. and Stephens, M. (2018) Large-scale genome-wide enrichment analyses identify new trait-associated genes and pathways across 31 human phenotypes. Nat. Commun., 9, 4361

[84]

Kichaev, G., Yang, W. Y., Lindstrom, S., Hormozdiari, F., Eskin, E., Price, A. L., Kraft, P. and Pasaniuc, B. (2014) Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet., 10, e1004722

[85]

Wen, X., Lee, Y., Luca, F. and Pique-Regi, R. (2016) Efficient integrative multi-SNP association analysis via deterministic approximation of posteriors. Am. J. Hum. Genet., 98, 1114–1129

[86]

Morley, M., Molony, C. M., Weber, T. M., Devlin, J. L., Ewens, K. G., Spielman, R. S. and Cheung, V. G. (2004) Genetic analysis of genome-wide variation in human gene expression. Nature, 430, 743–747

[87]

Lappalainen, T., Sammeth, M., Friedländer, M. R., ’t Hoen, P. A., Monlong, J., Rivas, M. A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., Ferreira, P. G., (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nature, 501, 506–511

[88]

GTEx Consortium (2017) Genetic effects on gene expression across human tissues. Nature, 550, 204–213

[89]

Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., (2015) A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet., 47, 1091–1098

[90]

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

[91]

Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W., Jansen, R., de Geus, E. J., Boomsma, D. I., Wright, F. A., (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet., 48, 245–252

[92]

Barbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., (2018) Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun., 9, 1825

[93]

Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A. N., Knowles, D. A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., (2019) Opportunities and challenges for transcriptome-wide association studies. Nat. Genet., 51, 592–599

[94]

Zhang, Y., Quick, C., Yu, K., Barbeira, A., Luca, F., Pique-Regi, R., Im, H. K. and Wen, X. (2019) Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis. bioRxiv, 808295

[95]

Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet., 50, 1219–1224

[96]

Schizophrenia Working Group of the Psychiatric Genomics Consortium. (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421–427

[97]

Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O’Donovan, M. C., Sullivan, P. F., Sklar, P., and the International Schizophrenia Consortium. (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460, 748–752

[98]

Stahl, E. A., Wegmann, D., Trynka, G., Gutierrez-Achury, J., Do, R., Voight, B. F., Kraft, P., Chen, R., Kallberg, H. J., Kurreeman, F. A., (2012) Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet., 44, 483–489

[99]

Lohmueller, K. E., Pearce, C. L., Pike, M., Lander, E. S. and Hirschhorn, J. N. (2003) Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat. Genet., 33, 177–182

[100]

Mak, T. S. H., Porsch, R. M., Choi, S. W., Zhou, X. and Sham, P. C. (2017) Polygenic scores via penalized regression on summary statistics. Genet. Epidemiol., 41, 469–480

[101]

So, H. C. and Sham, P. C. (2017) Improving polygenic risk prediction from summary statistics by an empirical Bayes approach. Sci. Rep., 7, 41262

[102]

Song, S., Jiang, W., Hou, L. and Zhao, H. (2020) Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies. PLOS Comput. Biol., 16, e1007565

[103]

Zhu, X. and Stephens, M. (2017) Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. Ann. Appl. Stat., 11, 1561–1592

[104]

Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., Genovese, G., Loh, P. R., Bhatia, G., Do, R., (2015) Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet., 97, 576–592

[105]

Lloyd-Jones, L. R., Zeng, J., Sidorenko, J., Yengo, L., Moser, G., Kemper, K. E., Wang, H., Zheng, Z., Magi, R., Esko, T., (2019) Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun., 10, 5086

[106]

Hu, Y., Lu, Q., Powles, R., Yao, X., Yang, C., Fang, F., Xu, X. and Zhao, H. (2017) Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Comput. Biol., 13, e1005589

[107]

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

[108]

Li, C., Yang, C., Gelernter, J. and Zhao, H. (2014) Improving genetic risk prediction by leveraging pleiotropy. Hum. Genet., 133, 639–650

[109]

Maier, R. M., Zhu, Z., Lee, S. H., Trzaskowski, M., Ruderfer, D. M., Stahl, E. A., Ripke, S., Wray, N. R., Yang, J., Visscher, P. M., (2018) Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nat. Commun., 9, 989

[110]

Ginsburg, G. S., Burke, T. W. and Febbo, P. (2008) Centralized biorepositories for genetic and genomic research. JAMA, 299, 1359–1361

[111]

Gottesman, O., Kuivaniemi, H., Tromp, G., Faucett, W. A., Li, R., Manolio, T. A., Sanderson, S. C., Kannry, J., Zinberg, R., Basford, M. A., (2013) The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet. Med., 15, 761–771

[112]

Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature, 562, 203–209

[113]

http://www.nealelab.is/uk-biobank/. Accessed: September 1, 2020

[114]

Li, X., Li, Z., Zhou, H., Gaynor, S. M., Liu, Y., Chen, H., Sun, R., Dey, R., Arnett, D. K., Aslibekyan, S., (2020) Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat. Genet., 52, 969–983

[115]

Gogarten, S. M., Sofer, T., Chen, H., Yu, C., Brody, J. A., Thornton, T. A., Rice, K. M. and Conomos, M. P. (2019) Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics, 35, 5346–5348

[116]

Loh, P. R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet., 47, 284–290

[117]

Loh, P. R., Kichaev, G., Gazal, S., Schoech, A. P. and Price, A. L. (2018) Mixed-model association for biobank-scale datasets. Nat. Genet., 50, 906–908

[118]

Zhou, W., Nielsen, J. B., Fritsche, L. G., Dey, R., Gabrielsen, M. E., Wolford, B. N., LeFaive, J., VandeHaar, P., Gagliano, S. A., Gifford, A., (2018) Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet., 50, 1335–1341

[119]

Peng, J. and Siegmund, D. (2004) Mapping quantitative traits with random and with ascertained sibships. Proc. Natl. Acad. Sci. USA, 101, 7845–7850

[120]

Cirulli, E. T. and Goldstein, D. B. (2010) Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet., 11, 415–425

[121]

Marchini, J., Howie, B., Myers, S., McVean, G. and Donnelly, P. (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet., 39, 906–913

[122]

Li, Y., Willer, C. J., Ding, J., Scheet, P. and Abecasis, G. R. (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol., 34, 816–834

[123]

Browning, B. L. and Browning, S. R. (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet.,

[124]

Lee, S., Abecasis, G. R., Boehnke, M. and Lin, X. (2014) Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet., 95, 5–23

[125]

Van Hout, C. V., Tachmazidou, I., Backman, J. D., Hoffman, J. D., Liu, D., Pandey, A. K., Gonzaga-Jauregui, C., Khalid, S., Ye, B., Banerjee, N., (2020) Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. Nature, 586, 749–756

[126]

Leung, Y. Y., Valladares, O., Chou, Y. F., Lin, H. J., Kuzma, A. B., Cantwell, L., Qu, L., Gangadharan, P., Salerno, W. J., Schellenberg, G. D., (2019) VCPA: genomic variant calling pipeline and data management tool for Alzheimer’s Disease Sequencing Project. Bioinformatics, 35, 1768–1770

[127]

Taliun, D., Harris, D. N., Kessler, M. D., Carlson, J., Szpiech, Z. A., Torres, R., Taliun, S. A. G., Corvelo, A., Gogarten, S. M., Kang, H. M., (2019) Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature, 590, 290–299

[128]

Kowalski, M. H., Qian, H., Hou, Z., Rosen, J. D., Tapia, A. L., Shan, Y., Jain, D., Argos, M., Arnett, D. K., Avery, C., , (2019) Use of€>100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet., 15, e1008500

[129]

Madsen, B. E. and Browning, S. R. (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet., 5, e1000384

[130]

Li, B. and Leal, S. M. (2008) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet., 83, 311–321

[131]

Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M. and Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet., 89, 82–93

[132]

Pan, W. (2009) Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genet. Epidemiol., 33, 497–507

[133]

Chen, H., Huffman, J. E., Brody, J. A., Wang, C., Lee, S., Li, Z., Gogarten, S. M., Sofer, T., Bielak, L. F., Bis, J. C., (2019) Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing Studies. Am. J. Hum. Genet., 104, 260–274

[134]

Morrison, A. C., Huang, Z., Yu, B., Metcalf, G., Liu, X., Ballantyne, C., Coresh, J., Yu, F., Muzny, D., Feofanova, E., (2017) Practical approaches for whole-genome sequence analysis of heart- and blood-related traits. Am. J. Hum. Genet., 100, 205–215

[135]

Sarnowski, C., Satizabal, C. L., DeCarli, C., Pitsillides, A. N., Cupples, L. A., Vasan, R. S., Wilson, J. G., Bis, J. C., Fornage, M., Beiser, A. S., (2018) Whole genome sequence analyses of brain imaging measures in the Framingham Study. Neurology, 90, e188–e196

[136]

Werling, D. M., Brand, H., An, J. Y., Stone, M. R., Zhu, L., Glessner, J. T., Collins, R. L., Dong, S., Layer, R. M., Markenscoff-Papadimitriou, E., (2018) An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. Nat. Genet., 50, 727–736

[137]

Li, Z., Li, X., Liu, Y., Shen, J., Chen, H., Zhou, H., Morrison, A. C., Boerwinkle, E. and Lin, X. (2019) Dynamic scan procedure for detecting rare-variant association regions in whole-genome sequencing studies. Am. J. Hum. Genet., 104, 802–814

[138]

He, Z., Xu, B., Buxbaum, J. and Ionita-Laza, I. (2019) A genome-wide scan statistic framework for whole-genome sequence data analysis. Nat. Commun., 10, 3018

[139]

Jakubosky, D., Smith, E. N., D’Antonio, M., Jan Bonder, M., Young Greenwald, W. W., D’Antonio-Chronowska, A., Matsui, H., Stegle, O., Montgomery, S. B., DeBoever, C., (2020) Discovery and quality analysis of a comprehensive set of structural variants and short tandem repeats. Nat. Commun., 11, 2928

[140]

Kosugi, S., Momozawa, Y., Liu, X., Terao, C., Kubo, M. and Kamatani, Y. (2019) Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing. Genome Biol., 20, 117

[141]

Mahmoud, M., Gobet, N., Cruz-Dávalos, D. I., Mounier, N., Dessimoz, C. and Sedlazeck, F. J. (2019) Structural variant calling: the long and the short of it. Genome Biol., 20, 246

[142]

Jakubosky, D., D’Antonio, M., Bonder, M. J., Smail, C., Donovan, M. K. R., Young Greenwald, W. W., Matsui, H., D’Antonio-Chronowska, A., Stegle, O., Smith, E. N., (2020) Properties of structural variants and short tandem repeats associated with gene expression and complex traits. Nat. Commun., 11, 2927

[143]

Chen, L., Abel, H. J., Das, I., Larson, D. E., Ganel, L., Kanchi, K. L., Regier, A. A., Young, E. P., Kang, C. J., Scott, A. J., (2020) Association of structural variation with cardiometabolic traits in Finns. Am. J. Hum. Genet., 108, 583–596

[144]

Sirugo, G., Williams, S. M. and Tishkoff, S. A. (2019) The missing diversity in human genetic studies. Cell, 177, 26–31

[145]

Genovese, G.Friedman, D. J., Ross, M. D., Lecordier, L., Uzureau, P., Freedman, B. I., Bowden, D. W., Langefeld, C. D., Oleksyk, T. K., Uscinski Knob, A. L., (2010) Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science, 1193032

[146]

Belloy, M. E., Napolioni, V. and Greicius, M. D. (2019) A quarter century of APOE and Alzheimer’s disease: progress to date and the path forward. Neuron, 101, 820–838

[147]

Zhang, R., Wang, X., Tang, Z., Liu, J., Yang, S., Zhang, Y., Wei, Y., Luo, W., Wang, J., Li, J., (2014) Apolipoprotein E gene polymorphism and the risk of intracerebral hemorrhage: a meta-analysis of epidemiologic studies. Lipids Health Dis., 13, 47

[148]

H3Africa Consortium, Rotimi, C., Abayomi, A., Abimiku, A., Adabayeri, V. M., Adebamowo, C., Adebiyi, E., Ademola, A. D., Adeyemo, A., Adu, D., (2014) Enabling the genomic revolution in Africa. Science, 344, 1346–1348

[149]

Banda, Y., Kvale, M. N., Hoffmann, T. J., Hesselson, S. E., Ranatunga, D., Tang, H., Sabatti, C., Croen, L. A., Dispensa, B. P., Henderson, M., (2015) Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the genetic epidemiology research on adult health and aging (GERA) cohort. Genetics, 200, 1285–1295

[150]

Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., Highland, H. M., Patel, Y. M., Sorokin, E. P., Avery, C. L., (2019) Genetic analyses of diverse populations improves discovery for complex traits. Nature, 570, 514–518

[151]

Fang, H., Hui, Q., Lynch, J., Honerlaw, J., Assimes, T. L., Huang, J., Vujkovic, M., Damrauer, S. M., Pyarajan, S., Gaziano, J. M., (2019) Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet., 105, 763–772

[152]

Coram, M. A., Duan, Q., Hoffmann, T. J., Thornton, T., Knowles, J. W., Johnson, N. A., Ochs-Balcom, H. M., Donlon, T. A., Martin, L. W., Eaton, C. B., (2013) Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in ethnically diverse human populations. Am. J. Hum. Genet., 92, 904–916

[153]

Galinsky, K. J., Reshef, Y. A., Finucane, H. K., Loh, P. R., Zaitlen, N., Patterson, N. J., Brown, B. C. and Price, A. L. (2019) Estimating cross-population genetic correlations of causal effect sizes. Genet. Epidemiol., 43, 180–188

[154]

Coram, M. A., Candille, S. I., Duan, Q., Chan, K. H., Li, Y., Kooperberg, C., Reiner, A. P. and Tang, H. (2015) Leveraging multi-ethnic evidence for mapping complex traits in minority populations: An empirical Bayes approach. Am. J. Hum. Genet., 96, 740–752

[155]

Coram, M. A., Fang, H., Candille, S. I., Assimes, T. L. and Tang, H. (2017) Leveraging multi-ethnic evidence for risk assessment of quantitative traits in minority populations. Am. J. Hum. Genet., 101, 218–226

[156]

Willer, C. J., Schmidt, E. M., Sengupta, S., Peloso, G. M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., Buchkovich, M. L., Mora, S., (2013) Discovery and refinement of loci associated with lipid levels. Nat. Genet., 45, 1274–1283

[157]

Panjwani, N., Wang, F., Mastromatteo, S., Bao, A., Wang, C., He, G., Gong, J., Rommens, J. M., Sun, L. and Strug, L. J. (2020) LocusFocus: Web-based colocalization for the annotation and functional follow-up of GWAS. PLOS Comput. Biol., 16, e1008336

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (1088KB)

2933

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/