{"doi":"10.1093/imammb/21.2.115","title":"Sequential genotyping within TDT families","abstract":null,"journal":"Mathematical Medicine and Biology","year":2004,"id":595218,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1524093,"name":"M. M. Iles","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Sequential genotyping within TDT families","abstract":"We demonstrate that, given a limited amount of genotyping resources, the power of a TDT study can be increased substantially by genotyping within families sequentially. By sequential genotyping we mean that one parent in a family should be typed and then the decision on whether to continue genotyping the family is based on this parent's genotype. If it is decided to continue genotyping then a further decision is made about whether to genotype the offspring once the second parent has been genotyped. We show that, for a given power, reductions in sample size of over 80% are possible, even for the most robust selection strategies. We discuss the practical application of such sequential genotyping and illustrate its potential using a real data set.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"15228102","pmcid":null,"openalex_id":"https://openalex.org/W2016691419","authors":[],"funders":[{"funder_name":"NIGMS NIH HHS","grant_id":"GM31575","title":null}],"total_grants":1,"fwci":0.0,"citation_percentile":0.1306651,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://academic.oup.com/imammb/article-pdf/21/2/115/2056907/210115.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/imammb/21.2.115","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/15228102","host_type":"repository"}],"fields_of_study":["Statistical Methods and Inference","Optimal Experimental Design Methods","Statistical Methods in Clinical Trials"],"mesh_terms":["Child","Diabetes Mellitus, Type 1","Female","Genotype","Humans","Male","Models, Genetic","Parents","Linkage Disequilibrium","Genetic Diseases, Inborn"],"keywords":["Genotyping","Genotype","Selection (genetic algorithm)","Genetics","Biology","Sample (material)","Computational biology","Computer science","Artificial intelligence","Gene","Chromatography","Chemistry"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-27T16:53:28.023736Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}