{"entity": "researcher", "timestamp": "2026-09-30T02:26:52.286Z", "family": "Iotchkova", "given": "Valentina", "initials": "V", "orcid": "0000-0001-5057-0210", "affiliations": ["Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.", "European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/73d2b62fca6c401b958486044d422371.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/73d2b62fca6c401b958486044d422371"}}, "publications": [{"entity": "publication", "iuid": "4ae112d2e93c4dc196e8e7ff1c40adaa", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/4ae112d2e93c4dc196e8e7ff1c40adaa.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/4ae112d2e93c4dc196e8e7ff1c40adaa"}}, "title": "A multiple-phenotype imputation method for genetic studies.", "authors": [{"family": "Dahl", "given": "Andrew", "initials": "A"}, {"family": "Iotchkova", "given": "Valentina", "initials": "V", "orcid": "0000-0001-5057-0210", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/73d2b62fca6c401b958486044d422371.json"}}, {"family": "Baud", "given": "Amelie", "initials": "A", "orcid": "0000-0003-2448-0283", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5b5a03b2adc479aacf190e0127cf755.json"}}, {"family": "Johansson", "given": "\u00c5sa", "initials": "\u00c5"}, {"family": "Gyllensten", "given": "Ulf", "initials": "U"}, {"family": "Soranzo", "given": "Nicole", "initials": "N", "orcid": "0000-0003-1095-3852", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/751b4656807c42f9a668ebb98e8da987.json"}}, {"family": "Mott", "given": "Richard", "initials": "R"}, {"family": "Kranis", "given": "Andreas", "initials": "A"}, {"family": "Marchini", "given": "Jonathan", "initials": "J", "orcid": "0000-0003-0610-8322", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d0d3e23a76434098fa7731ac294cd1.json"}}], "type": "journal article", "published": "2016-04-00", "journal": {"title": "Nat. Genet.", "issn": "1546-1718", "volume": "48", "issue": "4", "pages": "466-472", "issn-l": "1061-4036"}, "abstract": "Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.", "doi": "10.1038/ng.3513", "pmid": "26901065", "labels": [], "xrefs": [{"db": "mid", "key": "EMS66922"}, {"db": "pmc", "key": "PMC4817234"}, {"db": "pii", "key": "ng.3513"}], "notes": [], "created": "2018-12-05T11:07:21.976Z", "modified": "2026-09-23T07:35:20.813Z"}]}