@article{
 title = {Polygenic disease: methods for mapping complex disease traits.},
 type = {article},
 year = {1995},
 identifiers = {[object Object]},
 keywords = {Autistic Disorder,Autistic Disorder: epidemiology,Autistic Disorder: genetics,Chromosome Mapping,Chromosomes, Human,Female,Genetic Diseases, Inborn,Genetic Diseases, Inborn: genetics,Genetic Linkage,Humans,Linkage Disequilibrium,Male,Models, Genetic,Nuclear Family,Pedigree},
 pages = {513-9},
 volume = {11},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/8533170},
 month = {12},
 id = {5b9e1c46-f952-30fe-ad2a-f5763a6fe064},
 created = {2017-06-19T13:41:12.539Z},
 file_attached = {true},
 profile_id = {de68dde1-2ff3-3a4e-a214-ef424d0c7646},
 group_id = {b2078731-0913-33b9-8902-a53629a24e83},
 last_modified = {2017-06-19T13:41:12.817Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {Improved genotyping technology has made it feasible to use a genetic approach to map genes involved in the etiology of common human diseases. We discuss here recent developments in several different statistical approaches to linkage analysis of these traits, including affected-sib-pair methods, the affected-pedigree-member method, regressive models and linkage-disequilibrium-based approaches. We discuss advantages and disadvantages of the various approaches, as well as factors influencing study design and the ability to detect loci. Statistical methodology in this area is advancing rapidly and will help enable the mapping and cloning of loci involved in susceptibility to common multifactorial diseases.},
 bibtype = {article},
 author = {Weeks, D E and Lathrop, G M},
 journal = {Trends in genetics : TIG},
 number = {12}
}

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