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GenABEL: an R library for genome-wide association analysis
Abstract
Here we describe an R library for genome-wide association (GWA) analysis. It implements effective storage and handling of GWA data, fast procedures for genetic data quality control, testing of association of single nucleotide polymorphisms with binary or quantitative traits, visualization of results and also provides easy interfaces to standard statistical and graphical procedures implemented in base R and special R libraries for genetic analysis. We evaluated GenABEL using one simulated and two real data sets. We conclude that GenABEL enables the analysis of GWA data on desktop computers. Availability: http://cran.r-project.org Contact: i.aoultchenko@erasmusmc.nl
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“The genomic inflation factor lambda was computed with the GenABEL 1.8-0 R package63 and a median approach after exclusion of the APOE region (44–46 Mb on chromosome 19 in GRCh38).”
From New insights into the genetic etiology of Alzheimer’s disease and related dementias (2022) · cites this paper as [63]
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Generating samples under a Wright–Fisher neutral model of genetic variation
Hudson, R. R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.2.337 · Cited by 2,397
Score Tests for Association between Traits and Haplotypes when Linkage Phase Is Ambiguous
Schaid, D. J., Rowland, C. M., Tines, D. E., Jacobson, R. M., & Poland, G. A. (2002) · The American Journal of Human Genetics · doi:10.1086/338688 · Cited by 1,777
A Note on Exact Tests of Hardy-Weinberg Equilibrium
Wigginton, J. E., Cutler, D. J., & Abecasis, G. R. (2005) · The American Journal of Human Genetics · doi:10.1086/429864 · Cited by 1,492
Genome-wide association: a promising start to a long race
Evans, D. M., & Cardon, L. R. (2006) · Trends in Genetics · doi:10.1016/j.tig.2006.05.001 · Cited by 33
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Jombart, T., & Ahmed, I. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr521 · Cited by 3,233
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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., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507
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Anderson, C. A., Pettersson, F. H., Clarke, G. M., Cardon, L. R., Morris, A. P., & Zondervan, K. T. (2010) · Nature Protocols · doi:10.1038/nprot.2010.116 · Cited by 1,318
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“In addition, our group has been active in developing new software for GWA analyses [74].”
MethodZhou, W., Nielsen, J. B., Fritsche, L. G., Dey, R., Gabrielsen, M. E., Wolford, B. N., LeFaive, J., VandeHaar, P., Gagliano, S. A., Gifford, A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0184-y · Cited by 1,544
Yin, L., Zhang, H., Tang, Z., Xu, J., Yin, D., Zhang, Z., Yuan, X., Zhu, M., Zhao, S., Li, X., et al. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.10.007 · Cited by 1,302
“51 Various software packages have also been developed for the implementation of these 52 algorithms, including TASSEL[12], GAPIT[13, 14], GenABEL[15], EMMAX[16], 53”
MethodGAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction
Wang, J., & Zhang, Z. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2021.08.005 · Cited by 1,240
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Huffman, J. E., Albrecht, E., Teumer, A., Mangino, M., Kapur, K., Johnson, T., Kutalik, Z., Pirastu, N., Pistis, G., Lopez, L. M., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0119752 · Cited by 1,234
“g a score test mmscore [18] implemented in the GenABEL package [19]).”
MethodFeitosa, M. F., Kraja, A. T., Chasman, D. I., Sung, Y. J., Winkler, T. W., Ντάλλα, Ι., Guo, X., Franceschini, N., Cheng, C., Sim, X., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198166 · Cited by 1,169
Speliotes, E. K., Yerges-Armstrong, L. M., Wu, J., Hernaez, R., Kim, L. J., Palmer, C. D., Gudnason, V., Eiriksdottir, G., Garcia, M. E., Launer, L. J., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001324 · Cited by 984
“Liver attenuation controlled for an external phantom was inverse normally transformed and residuals created from a linear regression model in Proable [36]/R with covariates of age, age(2), gender and drinks along with the SNPs in an additive genetic model (See Tables S2, S3).”
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Aulchenko, Y. S., Janssens, A. C. J. W., Albert Hofman, D., Isaacs, A. J., Sijbrands, E. J. G., Witteman, J. C. M., van Duijn, C. M., Ripatti, S., Lindqvist, I., Perola, M., et al. (2008) · Nature Genetics · doi:10.1038/ng.269 · Cited by 860
Self-citationA tutorial on conducting genome‐wide association studies: Quality control and statistical analysis
Marees, A. T., de Kluiver, H., Stringer, S., Vorspan, F., Curis, E., Cynthia Marie-claire, A., & Derks, E. M. (2018) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1608 · Cited by 879
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Self-citationMapping cis- and trans-regulatory effects across multiple tissues in twins
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“Other analyses were performed in R, principally using GenABE”
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Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T., & Delaneau, O. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv722 · Cited by 580
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Aulchenko, Y., de Koning, D., & Haley, C. (2007) · Genetics · doi:10.1534/genetics.107.075614 · Cited by 460
Self-citationCommon variants near ATM are associated with glycemic response to metformin in type 2 diabetes
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Yang, J., Loos, R. J. F., Powell, J. E., Medland, S. E., Speliotes, E. K., Chasman, D. I., Rose, L. M., Þorleifsson, G., Steinthórsdóttir, V., Mägi, R., et al. (2012) · Nature · doi:10.1038/nature11401 · Cited by 418
Self-citationRandall, J. C., Winkler, T. W., Kutalik, Z., Berndt, S. I., Jackson, A. U., Monda, K. L., Kilpeläinen, T. O., Esko, T., Mägi, R., Li, S., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003500 · Cited by 420
Genetic Variants Influencing Circulating Lipid Levels and Risk of Coronary Artery Disease
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Self-citationChen, D., Neumann, K., Friedel, S., Kilian, B., Chen, M., Altmann, T., & Klukas, C. (2014) · The Plant Cell · doi:10.1105/tpc.114.129601 · Cited by 404
Grundberg, E., Meduri, E., Sandling, J. K., Hedman, Å. K., Keildson, S., Buil, A., Busche, S., Yuan, W., Nisbet, J., Sekowska, M., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.10.004 · Cited by 370
Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function
Artigas, M. S., Loth, D. W., Wain, L. V., Gharib, S. A., Obeidat, M., Tang, W., Zhai, G., Zhao, J. H., Smith, A. V., Huffman, J. E., et al. (2011) · Nature Genetics · doi:10.1038/ng.941 · Cited by 405
ProbABEL package for genome-wide association analysis of imputed data
Aulchenko, Y. S., Struchalin, M. V., & van Duijn, C. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-134 · Cited by 400
“This allows representation of each individual genotype using two-bit coding and consequently effective storage of the genotype data in RAM [3].”
MethodSelf-citationGenome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize
Riedelsheimer, C., Lisec, J., Czedik‐Eysenberg, A., Sulpice, R., Flis, A., Grieder, C., Altmann, T., Stitt, M., Willmitzer, L., & Melchinger, A. E. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120813109 · Cited by 378
“GWA models were fitted using the maximum likelihood implementation in the function polygenic of GenABEL (51).”
MethodInfluentialEfficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr
Privé, F., Aschard, H., Ziyatdinov, A., & Blum, M. G. B. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty185 · Cited by 366
Couch, F. J., Wang, X., McGuffog, L., Lee, A., Olswold, C., Kuchenbaecker, K. B., Soucy, P., Fredericksen, Z., Barrowdale, D., Dennis, J., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003212 · Cited by 378
Bryois, J., Skene, N., Hansen, T. F., Kogelman, L. J. A., Watson, H. J., Liu, Z., Adan, R. A. H., Alfredsson, L. S., Ando, T., Andreassen, O. A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0610-9 · Cited by 381
Lauc, G., Huffman, J. E., Pučić‐Baković, M., Zgaga, L., Adamczyk, B., Mužinić, A., Novokmet, M., Polašek, O., Gornik, O., Krištić, J., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003225 · Cited by 366
“The ‘‘mmscore’’ function of ProbABEL [87] was used for the association test under an additive model.”
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Liu, Y., Buil, A., Collins, B. C., Gillet, L. C., Blum, L. C., Cheng, L., Vitek, O., Mouritsen, J., Lachance, G., Spector, T. D., et al. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145728 · Cited by 328
Zhernakova, A., Stahl, E. A., Trynka, G., Raychaudhuri, S., Festen, E. A. M., Franke, L., Westra, H.-J., Fehrmann, R. S. N., Kurreeman, F. A. S., Thomson, B. P., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002004 · Cited by 362
“[20,38] To detect associations of the same and opposite directions, two tests were performed.”
MethodEMBRACE, Antoniou, A. C., Collaborators, G. S., HEBON, kConFab, SWE-BRCA, SQUAD, M., GENICA, Wang, X., Fredericksen, Z. S., et al. (2010) · Nature Genetics · doi:10.1038/ng.669 · Cited by 331
“These analyses were performed in R using the GenABE”
Kuchenbaecker, K. B., McGuffog, L., Barrowdale, D., Lee, A. S., Soucy, P., Dennis, J., Domchek, S. M., Robson, M. E., Spurdle, A. B., Ramus, S. J., et al. (2016) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djw302 · Cited by 333
“Analyses were carried out in R using GenABEL (32) and in STATA v13.”
MethodCardiac Troponin T and Troponin I in the General Population
Welsh, P. I., Preiss, D., Hayward, C., Shah, A. S. V., McAllister, D., Briggs, A. H., Boachie, C., McConnachie, A., Padmanabhan, S., Welsh, C. E., et al. (2019) · Circulation · doi:10.1161/circulationaha.118.038529 · Cited by 330
A Genome-Wide Association Study Identifies Five Loci Influencing Facial Morphology in Europeans
Liu, F., van der Lijn, F., Schurmann, C., Zhu, G., Chakravarty, M. M., Hysi, P. G., Wollstein, A., Lao, O., de Bruijne, M., Ikram, M. A., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002932 · Cited by 326
“We tested 2,558,979 autosomal SNPs with linear regression (adjusted for sex, age, EIGENSTRAT-derived ancestry informative covariates [75], plus any additional ancestry informative covariates as appropriate) in GenABEL [76].”
MethodApplication of high-dimensional feature selection: evaluation for genomic prediction in man
Bermingham, M. L., Pong‐Wong, R., Spiliopoulou, A., Hayward, C., Rudan, I., Campbell, H., Wright, A. F., Wilson, J. F., Agakov, F. V., Navarro, P., et al. (2015) · Scientific Reports · doi:10.1038/srep10312 · Cited by 334
Roederer, M., Quaye, L., Mangino, M., Beddall, M. H., Mahnke, Y., Chattopadhyay, P., Tosi, I., Napolitano, L., Terranova Barberio, M., Menni, C., et al. (2015) · Cell · doi:10.1016/j.cell.2015.02.046 · Cited by 296
“Genome-wide Association Analysis Because of relatedness in the TwinsUK cohort, we utilized the GenABEL software package (Aulchenko et al., 2007), which is designed for GWAS analysis of family-based data by incorporating pairwise kinship matrix calculated using”
MethodSmall, K. S., Grundberg, E., Soranzo, N., Ahmadi, K. R., Spector, T. D., Shin, S., Deloukas, P., Hedman, Å. K., Lindgren, C. M., McCarthy, M. I., et al. (2011) · Nature Genetics · doi:10.1038/ng.833 · Cited by 328
“All MuTHER eQTL associations were performed with the GenABEL and ProbABEL package”
MethodKarlsson, T., Rask‐Andersen, M., Pan, G., Höglund, J., Wadelius, C., Ek, W. E., & Johansson, Å. (2019) · Nature Medicine · doi:10.1038/s41591-019-0563-7 · Cited by 325
A genome-wide association study of anorexia nervosa
Perica, V. B., Franklin, C. S., Floyd, J. A. B., Thornton, L. M., Huckins, L. M., Southam, L., Rayner, N. W., Tachmazidou, I., Klump, K. L., Treasure, J. L., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.187 · Cited by 331
The Meta-Analyses of Glucose- and Insulin-related traits Consortium (MAGIC), Horikoshi, M., Early Growth Genetics (EGG) Consortium, Yaghootkar, H., Mook-Kanamori, D. O., Sovio, U., Taal, H. R., Hennig, B. J., Bradfield, J. P., St Pourcain, B., et al. (2013) · Nature Genetics · doi:10.1038/ng.2477 · Cited by 309
Self-citationScheben, A., Batley, J., & Edwards, D. (2016) · Plant Biotechnology Journal · doi:10.1111/pbi.12645 · Cited by 323
“, 2007) and the R packages GenABEL (Aulchenko et al., 2007) and SNPassoc (Gonzalez et al.”
MethodPatin, É., Hasan, M., Bergstedt, J., Rouilly, V., Libri, V., Urrutia, A., Alanio, C., Šćepanović, P., Hammer, C., Jönsson, F., et al. (2018) · Nature Immunology · doi:10.1038/s41590-018-0049-7 · Cited by 305
den Hoed, M., Eijgelsheim, M., Esko, T., Brundel, B. J. J. M., Peal, D. S., Evans, D. M., Nolte, I. M., Segrè, A. V., Hólm, H., Handsaker, R. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2610 · Cited by 298
“235–251 (1987). 20.”
InfluentialSelf-citationGender-specific pathway differences in the human serum metabolome
Krumsiek, J., Mittelstraß, K., Do, K. T., Stückler, F., Ried, J. S., Adamski, J., Peters, A., Illig, T., Kronenberg, F., Friedrich, N., et al. (2015) · Metabolomics · doi:10.1007/s11306-015-0829-0 · Cited by 293
“of the GenABEL (Aulchenko et al. 2007) suite.”
Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study
Kote‐Jarai, Z., The UK ProtecT Study Collaborators, T. A. P. C. B., Olama, A. A. A., Giles, G. G., Severi, G., Schleutker, J., Weischer, M., Campa, D., Ríboli, E., Key, T. J., et al. (2011) · Nature Genetics · doi:10.1038/ng.882 · Cited by 291
The Rotterdam Study: 2012 objectives and design update
Albert Hofman, D., van Duijn, C. M., Franco, O. H., Ikram, M. A., Janssen, H. L. A., Klaver, C. C. W., Kuipers, E. J., Nijsten, T. E. C., Stricker, B. H. C., Tiemeier, H. W., et al. (2011) · European Journal of Epidemiology · doi:10.1007/s10654-011-9610-5 · Cited by 290
“been active in developing new software for GWAs analyses [292].”
MethodHuyghe, J. R., Jackson, A., Fogarty, M. P., Buchkovich, M. L., Stančáková, A., Stringham, H. M., Sim, X., Yang, L., Fuchsberger, C., Cederberg, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2507 · Cited by 280
Heard‐Costa, N. L., Zillikens, M. C., Monda, K. L., Johansson, Å., Harris, T. B., Fu, M., Haritunians, T., Feitosa, M. F., Aspelund, T., Eiríksdóttir, G., et al. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000539 · Cited by 285
Self-citationPredictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
Mallick, H., Franzosa, E. A., Mclver, L. J., Banerjee, S., Sirota‐Madi, A., Kostic, A. D., Clish, C. B., Vlamakis, H. C., Xavier, R. J., & Huttenhower, C. (2019) · Nature Communications · doi:10.1038/s41467-019-10927-1 · Cited by 283
Richards, J. B., Kavvoura, F. K., Rivadeneira, F., Styrkársdóttir, U., Estrada, K., Halldórsson, B. V., Hsu, Y., Zillikens, M. C., Wilson, S. G., Mullin, B. H., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-8-200910200-00006 · Cited by 281
“Data on fracture were obtained from a prospective cohort (n = 5974) from the Netherlands.”
MethodInfluentialSelf-citationThe gut microbiota is largely independent of host genetics in regulating fat deposition in chickens
Wen, C., Yan, W., Sun, C., Ji, C., Zhou, Q., Zhang, D., Zheng, J., & Yang, N. (2019) · The ISME Journal · doi:10.1038/s41396-019-0367-2 · Cited by 284
PredictABEL: an R package for the assessment of risk prediction models
Kundu, S., Aulchenko, Y. S., van Duijn, C. M., & Janssens, A. C. J. W. (2011) · European Journal of Epidemiology · doi:10.1007/s10654-011-9567-4 · Cited by 276
“PredictABEL is a part of the GenABEL software suite for statistical genomics [14, 15] and for that reason written in R to enable easy transfer of data from gene discovery to genetic prediction studies.”
MethodSelf-citationChan, E. K. F., Rowe, H. C., Corwin, J., Joseph, B., & Kliebenstein, D. J. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001125 · Cited by 277
The gene cortex controls mimicry and crypsis in butterflies and moths
Nadeau, N. J., Pardo‐Díaz, C., Whibley, A. C., Supple, M. A., Saenko, S. V., Wallbank, R. W. R., Wu, G. C., Maroja, L. S., Ferguson, L., Hanly, J. J., et al. (2016) · Nature · doi:10.1038/nature17961 · Cited by 254
A thrifty variant in CREBRF strongly influences body mass index in Samoans
Minster, R. L., Hawley, N. L., Su, C.-T., Sun, G., Kershaw, E. E., Cheng, H., Buhule, O. D., Lin, J., Reupena, M. S., Viali, S., et al. (2016) · Nature Genetics · doi:10.1038/ng.3620 · Cited by 271
Ameur, A., Enroth, S., Johansson, Å., Zaboli, G., Igl, W., Johansson, A., Rivas, M. A., Daly, M. J., Schmitz, G., Hicks, A. A., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.03.014 · Cited by 265
“Meta-analyses of data from the five populations were carried out with MetABEL.(34) We used Haploview(36) to examine the association between high and low levels of the lipid PC 36:4 and all SNPs with a call rate above 75%.”
MethodInfluentialA Genomic Background Based Method for Association Analysis in Related Individuals
Amin, N., van Duijn, C. M., & Aulchenko, Y. S. (2007) · PLoS ONE · doi:10.1371/journal.pone.0001274 · Cited by 262
“org); computations associated with GenGR-GC were facilitated by GenABEL package for R [19], implementing procedures to compute genomic kinship, maximize polygenic models and compute the residuals.”
MethodSelf-citationKayser, M., Liu, F., Janssens, A. C. J. W., Rivadeneira, F., Lao, O., van Duijn, K., Vermeulen, M., Arp, P., Jhamai, M. M., van IJcken, W. F. J., et al. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2007.10.003 · Cited by 260
Self-citationTsai, H., Hamilton, A., Tinch, A. E., Guy, D. R., Gharbi, K., Stear, M. J., Matika, O., Bishop, S., & Houston, R. D. (2015) · BMC Genomics · doi:10.1186/s12864-015-2117-9 · Cited by 259
A genome-wide association study of COPD identifies a susceptibility locus on chromosome 19q13
Cho, M. H., Castaldi, P. J., Wan, E. S., Siedliński, M., Hersh, C. P., DeMeo, D. L., Himes, B. E., Sylvia, J. S., Klanderman, B. J., Ziniti, J. P., et al. (2011) · Human Molecular Genetics · doi:10.1093/hmg/ddr524 · Cited by 253
Genome-Wide Association in Tomato Reveals 44 Candidate Loci for Fruit Metabolic Traits
Sauvage, C., Segura, V., Bauchet, G. J., Stevens, R. G., Do, P. T., Nikoloski, Z., Fernie, A. R., & Causse, M. (2014) · PLANT PHYSIOLOGY · doi:10.1104/pp.114.241521 · Cited by 251
“The minimal MAF was set according to the formula [number of chromosomes/(2 3 number of individuals)], as proposed by Aulchenko et al. (2007).”
MethodRask-Andersen, M., Karlsson, T., Ek, W. E., & Johansson, Å. (2019) · Nature Communications · doi:10.1038/s41467-018-08000-4 · Cited by 245
Lauc, G., Essafi, A., Huffman, J. E., Hayward, C., Knežević, A., Kattla, J. J., Polašek, O., Gornik, O., Vitart, V., Abrahams, J. L., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001256 · Cited by 244
“statistical software [53] was used for the association test under an”
MethodMeyer, T. E., Verwoert, G. C., Hwang, S., Glazer, N. L., Smith, A. V., van Rooij, F. J. A., Ehret, G., Boerwinkle, E., Felix, J. F., Leak, T. S., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001045 · Cited by 247
Huntington's disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels
Horvath, S., Langfelder, P., Kwak, S., Aaronson, J., Rosinski, J., Vogt, T., Eszes, M., Faull, R. L. M., Curtis, M. A., Waldvogel, H. J., et al. (2016) · Aging · doi:10.18632/aging.101005 · Cited by 250
Ng, M. C. Y., Shriner, D., Chen, B. H., Li, J., Chen, W., Guo, X., Liu, J., Bielinski, S. J., Yanek, L. R., Nalls, M. A., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004517 · Cited by 243
“8) within a gene or within 1 Mb of the gene transcription start or end site and normalized expression values were performed with the GenABEL/ProbABEL packages [61–62] using the polygenic linear model incorporating a kinship matrix in GenABEL followed by the ProbABEL mmscore score test with imputed genotypes.”
MethodIdentification of Novel Genetic Markers of Breast Cancer Survival
Guo, Q. J., Schmidt, M. K., Kraft, P. L., Canisius, S., Chen, C., Khan, S., Tyrer, J. P., Bolla, M. K., Wang, Q., Dennis, J., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv081 · Cited by 234
“Analysis of CGEMS and BPC3 data was conducted using ProbABEL (32).”
MethodCUBN Is a Gene Locus for Albuminuria
Böger, C. A., Chen, M., Tin, A., Olden, M., Köttgen, A., de Boer, I. H., Fuchsberger, C., O’Seaghdha, C. M., Pattaro, C., Teumer, A., et al. (2011) · Journal of the American Society of Nephrology · doi:10.1681/asn.2010060598 · Cited by 236
Timofeeva, M. N., Hung, R. J., Rafnar, Þ., Christiani, D. C., Field, J. K., Bickeböller, H., Risch, A., McKay, J., Wang, Y., Dai, J., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds334 · Cited by 235
The Rotterdam Study: 2010 objectives and design update
Hofman, A., Breteler, M. M. B., van Duijn, C. M., Janssen, H. L. A., Krestin, G. P., Kuipers, E. J., Stricker, B. H. C., Tiemeier, H., Uitterlinden, A. G., Vingerling, J. R., et al. (2009) · European Journal of Epidemiology · doi:10.1007/s10654-009-9386-z · Cited by 244
“in developing new software for GWAs analyses [197].”
MethodMangino, M., Hwang, S., Spector, T. D., Hunt, S. C., Kimura, M., Fitzpatrick, A. L., Christiansen, L., Petersen, I., Elbers, C. C., Harris, T. B., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds382 · Cited by 228
“The GenAbel (60) package in R was used for analysis to allow for the pedigree structures to be modeled appropriately.”
InfluentialPan-cancer proteomic map of 949 human cell lines
Gonçalves, E., Poulos, R. C., Cai, Z., Barthorpe, S., Manda, S. S., Lucas, N., Beck, A., Bucio-Noble, D., Dausmann, M., Hall, C., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.06.010 · Cited by 239
“For each group, the p value distribution inflation factor lambda, l, was calculated using the median method (Aulchenko et al., 2007).”
Cousminer, D. L., Berry, D. J., Timpson, N. J., Ang, W., Thiering, E., Byrne, E. M., Taal, H. R., Huikari, V., Bradfield, J. P., Kerkhof, M., et al. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt104 · Cited by 209
Huan, T., Joehanes, R., Song, C., Peng, F., Guo, Y., Mendelson, M., Yao, C., Liu, C., Ma, J., Richard, M. A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12228-z · Cited by 224
Chromatin three-dimensional interactions mediate genetic effects on gene expression
Delaneau, O., Zazhytska, M., Borel, C., Giannuzzi, G., Rey, G., Howald, C., Kumar, S., Ongen, H., Popadin, K. Y., Marbach, D., et al. (2019) · Science · doi:10.1126/science.aat8266 · Cited by 212
Solouki, A. M., Verhoeven, V. J. M., van Duijn, C. M., Verkerk, A. J. M. H., Ikram, M. K., Hysi, P. G., Despriet, D. D. G., van Koolwijk, L. M. E., Ho, L., Ramdas, W. D., et al. (2010) · Nature Genetics · doi:10.1038/ng.663 · Cited by 215
“To adjust for family relationships, the GenABEL packag”
Self-citationthe CARDIoGRAM Consortium, Bis, J. C., Kavousi, M., Franceschini, N., Isaacs, A., Abecasis, G. R., Schminke, U., Post, W. S., Smith, A. V., Cupples, L. A., et al. (2011) · Nature Genetics · doi:10.1038/ng.920 · Cited by 212
Self-citation
Metadata
- Authors
- Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M.
- Affiliations
- Erasmus MC (NL)
- 1 Department of Epidemiology and Biostatistics, Erasmus MC Rotterdam, Postbus 2040, 3000 CA Rotterdam, The Netherlands and 2Statistical Genetics Group, Max-Planck-Institute of Psychiatry, Kraepelinstr. 10, D-80804 Munich, Germany
- Corresponding author
- Aulchenko, Yurii S.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-03-23
- Volume
- 23
- Issue
- 10
- Pages
- 1294-1296
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2161245845
- Cited by
- 1,836
- References
- 4
- Citations by year
- 2026: 22 · 2025: 39 · 2024: 91 · 2023: 72 · 2022: 65 · 2021: 129 · 2020: 113 · 2019: 161 · 2018: 178 · 2017: 153 · 2016: 139 · 2015: 143 · 2014: 140 · 2013: 116 · 2012: 89
- FWCI
- 19.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- genome-wide association study, R library, single nucleotide polymorphisms, association testing, binary traits, quantitative traits, statistical software
- MeSH terms
- Computer Simulation, Haplotypes, Humans, Software, Gene Library, Genome, Human, Information Storage and Retrieval, Genome, Computational Biology, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide
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