{"entity": "researcher", "timestamp": "2026-08-17T16:56:53.853Z", "family": "Clouard", "given": "Camille", "initials": "C", "orcid": "0009-0006-3654-6525", "affiliations": ["Division of Scientific Computing, Department of Information Technology, Uppsala University, L\u00e4gerhyddsv\u00e4gen 1, 75237, Uppsala, Sweden. camille.clouard@it.uu.se."], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/dd4bd5b6f553465d98cf8c3075b107fb.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/dd4bd5b6f553465d98cf8c3075b107fb"}}, "publications": [{"entity": "publication", "iuid": "5dd2c482172c4bfebbef112fd7f32a78", "links": {"self": {"href": "https://publications.scilifelab.se/publication/5dd2c482172c4bfebbef112fd7f32a78.json"}, "display": {"href": "https://publications.scilifelab.se/publication/5dd2c482172c4bfebbef112fd7f32a78"}}, "title": "Using feedback in pooled experiments augmented with imputation for high genotyping accuracy at reduced cost.", "authors": [{"family": "Clouard", "given": "Camille", "initials": "C", "orcid": "0009-0006-3654-6525", "researcher": {"href": "https://publications.scilifelab.se/researcher/dd4bd5b6f553465d98cf8c3075b107fb.json"}}, {"family": "Nettelblad", "given": "Carl", "initials": "C", "orcid": "0000-0003-0458-6902", "researcher": {"href": "https://publications.scilifelab.se/researcher/c300f2900aff429d8a3bbc72ee006df7.json"}}], "type": "journal article", "published": "2025-03-18", "journal": {"title": "G3 (Bethesda)", "issn": "2160-1836", "volume": "15", "issue": "3", "issn-l": "2160-1836"}, "abstract": "Conducting genomic selection (GS) in plant breeding programs can substantially speed up the development of new varieties. GS provides more reliable insights when it is based on dense marker data, in which the rare variants can be particularly informative. Despite the availability of new technologies, the cost of large-scale genotyping remains a major limitation to the implementation of GS. We suggest to combine pooled genotyping with population-based imputation as a cost-effective computational strategy for genotyping SNPs. Pooling saves genotyping tests and has proven to accurately capture the rare variants that are usually missed by imputation. In this study, we investigate adding iterative coupling to a joint model of pooling and imputation that we have previously proposed. In each iteration, the imputed genotype probabilities serve as feedback input for adjusting the per-sample prior genotype probabilities, before running a new imputation based on these adjusted data. This flexible setup indirectly imposes consistency between the imputed genotypes and the pooled observations. We demonstrate that repeated cycles of feedback can take advantage of the strengths in both pooling and imputation when an appropriate set of reference haplotypes is available for imputation. The iterations improve greatly upon the initial genotype predictions, achieving very high genotype accuracy for both low- and high-frequency variants. We enhance the average concordance from 94.5% to 98.4% at limited computational cost and without requiring any additional genotype testing.", "doi": "10.1093/g3journal/jkaf010", "pmid": "39847531", "labels": {"Bioinformatics Support for Computational Resources": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC11917477"}, {"db": "pii", "key": "7976924"}], "notes": [], "created": "2025-11-28T10:49:10.699Z", "modified": "2025-11-28T10:49:10.753Z"}, {"entity": "publication", "iuid": "cdfa6ce0b6d648569146efd8b266fe74", "links": {"self": {"href": "https://publications.scilifelab.se/publication/cdfa6ce0b6d648569146efd8b266fe74.json"}, "display": {"href": "https://publications.scilifelab.se/publication/cdfa6ce0b6d648569146efd8b266fe74"}}, "title": "Genotyping of SNPs in bread wheat at reduced cost from pooled experiments and imputation.", "authors": [{"family": "Clouard", "given": "Camille", "initials": "C", "orcid": "0009-0006-3654-6525", "researcher": {"href": "https://publications.scilifelab.se/researcher/dd4bd5b6f553465d98cf8c3075b107fb.json"}}, {"family": "Nettelblad", "given": "Carl", "initials": "C"}], "type": "journal article", "published": "2024-01-19", "journal": {"title": "Theor. Appl. Genet.", "issn": "1432-2242", "volume": "137", "issue": "1", "pages": "26", "issn-l": "0040-5752"}, "abstract": "Pooling and imputation are computational methods that can be combined for achieving cost-effective and accurate high-density genotyping of both common and rare variants, as demonstrated in a MAGIC wheat population. The plant breeding industry has shown growing interest in using the genotype data of relevant markers for performing selection of new competitive varieties. The selection usually benefits from large amounts of marker data, and it is therefore crucial to dispose of data collection methods that are both cost-effective and reliable. Computational methods such as genotype imputation have been proposed earlier in several plant science studies for addressing the cost challenge. Genotype imputation methods have though been used more frequently and investigated more extensively in human genetics research. The various algorithms that exist have shown lower accuracy at inferring the genotype of genetic variants occurring at low frequency, while these rare variants can have great significance and impact in the genetic studies that underlie selection. In contrast, pooling is a technique that can efficiently identify low-frequency items in a population, and it has been successfully used for detecting the samples that carry rare variants in a population. In this study, we propose to combine pooling and imputation and demonstrate this by simulating a hypothetical microarray for genotyping a population of recombinant inbred lines in a cost-effective and accurate manner, even for rare variants. We show that with an adequate imputation model, it is feasible to accurately predict the individual genotypes at lower cost than sample-wise genotyping and time-effectively. Moreover, we provide code resources for reproducing the results presented in this study in the form of a containerized workflow.", "doi": "10.1007/s00122-023-04533-5", "pmid": "38243086", "labels": {"Bioinformatics Support for Computational Resources": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC10799138"}, {"db": "pii", "key": "10.1007/s00122-023-04533-5"}], "notes": [], "created": "2024-11-25T10:10:44.083Z", "modified": "2025-02-28T14:10:48.361Z"}]}