{"id":62994,"date":"2025-08-12T16:52:16","date_gmt":"2025-08-12T16:52:16","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/62994\/"},"modified":"2025-08-12T16:52:16","modified_gmt":"2025-08-12T16:52:16","slug":"ldak-kvik-performs-fast-and-powerful-mixed-model-association-analysis-of-quantitative-and-binary-phenotypes","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/62994\/","title":{"rendered":"LDAK-KVIK performs fast and powerful mixed-model association analysis of quantitative and binary phenotypes"},"content":{"rendered":"

Uffelmann, E. et al. Genome-wide association studies. Nat. Rev. Methods Prim. 1, 59 (2021).<\/p>\n

CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet 101, 5\u201322 (2017).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, Z. et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int J. Epidemiol. 40, 1652\u20131666 (2011).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Keaton, J. M. et al. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits. Nat. Genet. 56, 778\u2013791 (2024).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Haines, J. L. et al. Complement factor H variant increases the risk of age-related macular degeneration. Science 308, 419\u2013421 (2005).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508\u2013518 (2023).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Leitsalu, L. et al. Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int. J. Epidemiol. 44, 1137\u20131147 (2014).<\/p>\n

PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lippert, C. et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833\u2013835 (2011).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, X. & Stephens, M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat. Methods 11, 407\u2013409 (2014).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M. & Price, A. L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100\u2013106 (2014).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Svishcheva, G. R., Axenovich, T. I., Belonogova, N. M., van Duijn, C. M. & Aulchenko, Y. S. Rapid variance components\u2013based method for whole-genome association analysis. Nat. Genet. 44, 1166\u20131170 (2012).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284\u2013290 (2015).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097\u20131103 (2021).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335\u20131341 (2018).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Loh, P. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906\u2013908 (2018).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Campos, A. I. et al. Boosting the power of genome-wide association studies within and across ancestries by using polygenic scores. Nat. Genet. 55, 1769\u20131776 (2023).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature https:\/\/doi.org\/10.1038\/s41586-018-0579-z<\/a> (2018).<\/p>\n

Sudlow, C. et al. The UK Biobank resource with deep phenotyping and genomic data. PLoS Med. 12, e1001779 (2015).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jiang, L. et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat. Genet. 51, 1749\u20131755 (2019).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Berrandou, T., Balding, D. & Speed, D. LDAK-GBAT: fast and powerful gene-based association testing using summary statistics. Am. J. Hum. Genet. 110, 23\u201329 (2023).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

MacKay, D. J. Information Theory, Inference and Learning Algorithms (Cambridge Univ. Press, 2003).<\/p>\n

Dey, R., Schmidt, E. M., Abecasis, G. R. & Lee, S. A fast and accurate algorithm to test for binary phenotypes and its application to PheWAS. Am. J. Hum. Genet 101, 37\u201349 (2017).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ma, Y., Bi, W. & Zhang, J.-F. Empirical saddlepoint approximation and its application to genome-wide association studies. In 2021 40th Chinese Control Conference (CCC) 6380\u20136385 (IEEE, 2021).<\/p>\n

Bi, W., Fritsche, L. G., Mukherjee, B., Kim, S. & Lee, S. A fast and accurate method for genome-wide time-to-event data analysis and its application to UK Biobank. Am. J. Hum. Genet. 107, 222\u2013233 (2020).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Speed, D., Holmes, J. & Balding, D. Evaluating and improving heritability models using summary statistics. Nat. Genet. 52, 458\u2013462 (2020).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Q., Priv\u00e9, F., Vilhj\u00e1lmsson, B. & Speed, D. Improved genetic prediction of complex traits from individual-level data or summary statistics. Nat. Commun. 12, 4192 (2021).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986\u2013992 (2017).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Speed, D. & Balding, D. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat. Genet. 51, 277\u2013284 (2019).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301\u2013320 (2005).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines, Vol. 1 (World Health Organization, 1992).<\/p>\n

de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bakshi, A. et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci. Rep. 6, 32894 (2016).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Momin, M. M. et al. A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data. Nat. Commun. 14, 722 (2023).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zeng, J. et al. Signatures of negative selection in the genetic architecture of human complex traits. Nat. Genet. 50, 746\u2013753 (2018).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Schoech, A. et al. Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits. Nat. Commun. 10, 790 (2019).<\/p>\n

Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2\u2013S8 (2017).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61\u2013D65 (2007).<\/p>\n

PubMed<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pain, O. et al. Evaluation of polygenic prediction methodology within a reference-standardized framework. PLoS Genet. 17, e1009021 (2021).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pain, O., Al-Chalabi, A. & Lewis, C. M. The GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring. Bioinformatics 40, btae551 (2024).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mak, T., Porsch, R., Choi, S., Zhou, X. & Sham, P. Polygenic scores via penalized regression on summary statistics. Genet. Epidemiol. 41, 469\u2013480 (2017).<\/p>\n

PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, S. & Zhou, X. Accurate and scalable construction of polygenic scores in large biobank data sets. Am. J. Hum. Genet. 106, 679\u2013693 (2020).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Priv\u00e9, F., Arbel, J. & Vilhj\u00e1lmsson, B. LDpred2: better, faster, stronger. Bioinformatics 36, 5424\u20135431 (2021).<\/p>\n

Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pazokitoroudi, A. et al. Efficient variance components analysis across millions of genomes. Nat. Commun. 11, 4020 (2020).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tseng, P. Convergence of a block coordinate descent method for nondifferentiable minimization. J. Optim. Theory Appl. 109, 475\u2013494 (2001).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hof, J. P. & Speed, D. LDAK 6.1. Zenodo https:\/\/doi.org\/10.5281\/zenodo.15747229<\/a> (2025).<\/p>\n","protected":false},"excerpt":{"rendered":"Uffelmann, E. et al. Genome-wide association studies. Nat. Rev. Methods Prim. 1, 59 (2021). CAS\u00a0 Google Scholar\u00a0 Visscher,…\n","protected":false},"author":2,"featured_media":62995,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25],"tags":[6634,7003,64,63,5562,5563,7002,1325,336,1319,7001,128,8247],"class_list":["post-62994","post","type-post","status-publish","format-standard","has-post-thumbnail","category-genetics","tag-agriculture","tag-animal-genetics-and-genomics","tag-au","tag-australia","tag-biomedicine","tag-cancer-research","tag-gene-function","tag-general","tag-genetics","tag-genome-wide-association-studies","tag-human-genetics","tag-science","tag-software"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/62994","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/comments?post=62994"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/62994\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media\/62995"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media?parent=62994"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/categories?post=62994"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/tags?post=62994"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}