Family-based designs in the age of large-scale gene-association studies
Key Points Either population-based or family-based designs can be used in gene-association studies. Population-based designs use unrelated individuals; family-based designs use probands and their relatives, typically either parents or siblings. Genetic-association studies face the obstacles of popul...
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Veröffentlicht in: | Nature reviews. Genetics 2006-05, Vol.7 (5), p.385-394 |
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description | Key Points
Either population-based or family-based designs can be used in gene-association studies. Population-based designs use unrelated individuals; family-based designs use probands and their relatives, typically either parents or siblings.
Genetic-association studies face the obstacles of population substructures and multiple testing.
Family-based designs are favoured because they are robust against confounding due to population substructures and test both linkage and association.
Case–control designs are preferred for the relative ease of data collection. They have modest power advantages, depending on the prevalence of the disease.
Family-based designs can be extended to incorporate pedigrees and complex phenotypes.
Screening tools are available for family-based designs that allow the multiple-testing problem, which is an important issue in whole-genome association studies, to be handled.
Although they are sometimes overlooked, family-based designs provide important advantages for detecting genetic associations in studies of complex disease. In particular, they provide a means of overcoming the problems that arise when multiple hypotheses are tested in genome-wide association studies.
Both population-based and family-based designs are commonly used in genetic association studies to locate genes that underlie complex diseases. The simplest version of the family-based design — the transmission disequilibrium test — is well known, but the numerous extensions that broaden its scope and power are less widely appreciated. Family-based designs have unique advantages over population-based designs, as they are robust against population admixture and stratification, allow both linkage and association to be tested for and offer a solution to the problem of model building. Furthermore, the fact that family-based designs contain both within- and between-family information has substantial benefits in terms of multiple-hypothesis testing, especially in the context of whole-genome association studies. |
doi_str_mv | 10.1038/nrg1839 |
format | Article |
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Either population-based or family-based designs can be used in gene-association studies. Population-based designs use unrelated individuals; family-based designs use probands and their relatives, typically either parents or siblings.
Genetic-association studies face the obstacles of population substructures and multiple testing.
Family-based designs are favoured because they are robust against confounding due to population substructures and test both linkage and association.
Case–control designs are preferred for the relative ease of data collection. They have modest power advantages, depending on the prevalence of the disease.
Family-based designs can be extended to incorporate pedigrees and complex phenotypes.
Screening tools are available for family-based designs that allow the multiple-testing problem, which is an important issue in whole-genome association studies, to be handled.
Although they are sometimes overlooked, family-based designs provide important advantages for detecting genetic associations in studies of complex disease. In particular, they provide a means of overcoming the problems that arise when multiple hypotheses are tested in genome-wide association studies.
Both population-based and family-based designs are commonly used in genetic association studies to locate genes that underlie complex diseases. The simplest version of the family-based design — the transmission disequilibrium test — is well known, but the numerous extensions that broaden its scope and power are less widely appreciated. Family-based designs have unique advantages over population-based designs, as they are robust against population admixture and stratification, allow both linkage and association to be tested for and offer a solution to the problem of model building. Furthermore, the fact that family-based designs contain both within- and between-family information has substantial benefits in terms of multiple-hypothesis testing, especially in the context of whole-genome association studies.</description><identifier>ISSN: 1471-0056</identifier><identifier>EISSN: 1471-0064</identifier><identifier>DOI: 10.1038/nrg1839</identifier><identifier>PMID: 16619052</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>Agriculture ; Animal Genetics and Genomics ; Biological and medical sciences ; Biomedical and Life Sciences ; Biomedicine ; Cancer Research ; Case-Control Studies ; Disease ; Family ; Fundamental and applied biological sciences. Psychology ; Gene Function ; Genes ; Genetic Techniques ; Genetics of eukaryotes. Biological and molecular evolution ; Genetics, Population ; Genome, Human ; Genomes ; Haplotypes ; Human Genetics ; Humans ; Hypotheses ; Hypothesis testing ; Linkage Disequilibrium ; Models, Genetic ; Parents & parenting ; Pedigree ; Phenotype ; Population-based studies ; Quantitative Trait, Heritable ; review-article ; Statistical power</subject><ispartof>Nature reviews. Genetics, 2006-05, Vol.7 (5), p.385-394</ispartof><rights>Springer Nature Limited 2006</rights><rights>2006 INIST-CNRS</rights><rights>COPYRIGHT 2006 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group May 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c561t-883c3ffe69df2ada5314ec013b5fb928514a374bb130ec84e6bcc6c787609823</citedby><cites>FETCH-LOGICAL-c561t-883c3ffe69df2ada5314ec013b5fb928514a374bb130ec84e6bcc6c787609823</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/nrg1839$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/nrg1839$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,2725,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17685058$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16619052$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Laird, Nan M</creatorcontrib><creatorcontrib>Lange, Christoph</creatorcontrib><title>Family-based designs in the age of large-scale gene-association studies</title><title>Nature reviews. Genetics</title><addtitle>Nat Rev Genet</addtitle><addtitle>Nat Rev Genet</addtitle><description>Key Points
Either population-based or family-based designs can be used in gene-association studies. Population-based designs use unrelated individuals; family-based designs use probands and their relatives, typically either parents or siblings.
Genetic-association studies face the obstacles of population substructures and multiple testing.
Family-based designs are favoured because they are robust against confounding due to population substructures and test both linkage and association.
Case–control designs are preferred for the relative ease of data collection. They have modest power advantages, depending on the prevalence of the disease.
Family-based designs can be extended to incorporate pedigrees and complex phenotypes.
Screening tools are available for family-based designs that allow the multiple-testing problem, which is an important issue in whole-genome association studies, to be handled.
Although they are sometimes overlooked, family-based designs provide important advantages for detecting genetic associations in studies of complex disease. In particular, they provide a means of overcoming the problems that arise when multiple hypotheses are tested in genome-wide association studies.
Both population-based and family-based designs are commonly used in genetic association studies to locate genes that underlie complex diseases. The simplest version of the family-based design — the transmission disequilibrium test — is well known, but the numerous extensions that broaden its scope and power are less widely appreciated. Family-based designs have unique advantages over population-based designs, as they are robust against population admixture and stratification, allow both linkage and association to be tested for and offer a solution to the problem of model building. Furthermore, the fact that family-based designs contain both within- and between-family information has substantial benefits in terms of multiple-hypothesis testing, especially in the context of whole-genome association studies.</description><subject>Agriculture</subject><subject>Animal Genetics and Genomics</subject><subject>Biological and medical sciences</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer Research</subject><subject>Case-Control Studies</subject><subject>Disease</subject><subject>Family</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Gene Function</subject><subject>Genes</subject><subject>Genetic Techniques</subject><subject>Genetics of eukaryotes. Biological and molecular evolution</subject><subject>Genetics, Population</subject><subject>Genome, Human</subject><subject>Genomes</subject><subject>Haplotypes</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Hypothesis testing</subject><subject>Linkage Disequilibrium</subject><subject>Models, Genetic</subject><subject>Parents & parenting</subject><subject>Pedigree</subject><subject>Phenotype</subject><subject>Population-based studies</subject><subject>Quantitative Trait, Heritable</subject><subject>review-article</subject><subject>Statistical power</subject><issn>1471-0056</issn><issn>1471-0064</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpl0VFr1TAUAOAiipub-AuUImzqQ2fSNGn6OIabg4Ew917S9KQ3o01mTgru3y-X1l2vkoeEnO8kOTlZ9o6SM0qY_OrCQCVrXmSHtKppQYioXj6vuTjI3iDeE0IFrdnr7IAKQRvCy8Ps6lJNdnwsOoXQ5z2gHRzm1uVxA7kaIPcmH1UYoECtRsgHcFAoRK-tita7HOPcW8Dj7JVRI8LbdT7K7i6_3V18L25-XF1fnN8UmgsaCymZZsaAaHpTql5xRivQhLKOm64pJaeVYnXVdZQR0LIC0WktdC1rQRpZsqPsdDn2IfhfM2BsJ4saxlE58DO2IkkqeZPgx3_gvZ-DS09ry5LVvKzJFp0taEiltdYZH4PSafQwWe0dGJv2z6mU6WEpKyV82UtIJsLvOKgZsb3-ebtvT_-yG1Bj3KAf5-2v4T78tEAdPGIA0z4EO6nw2FLSbrvbrt1N8sNa1NxN0O_c2s4ETlagtu0yQTltcedqITnhMrnPi8MUcgOE3e_8f-f7hToV5wDPZ_2JPwEkIsDO</recordid><startdate>20060501</startdate><enddate>20060501</enddate><creator>Laird, Nan M</creator><creator>Lange, Christoph</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QP</scope><scope>7QR</scope><scope>7RV</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20060501</creationdate><title>Family-based designs in the age of large-scale gene-association studies</title><author>Laird, Nan M ; Lange, Christoph</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c561t-883c3ffe69df2ada5314ec013b5fb928514a374bb130ec84e6bcc6c787609823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Agriculture</topic><topic>Animal Genetics and Genomics</topic><topic>Biological and medical sciences</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Cancer Research</topic><topic>Case-Control Studies</topic><topic>Disease</topic><topic>Family</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Gene Function</topic><topic>Genes</topic><topic>Genetic Techniques</topic><topic>Genetics of eukaryotes. Biological and molecular evolution</topic><topic>Genetics, Population</topic><topic>Genome, Human</topic><topic>Genomes</topic><topic>Haplotypes</topic><topic>Human Genetics</topic><topic>Humans</topic><topic>Hypotheses</topic><topic>Hypothesis testing</topic><topic>Linkage Disequilibrium</topic><topic>Models, Genetic</topic><topic>Parents & parenting</topic><topic>Pedigree</topic><topic>Phenotype</topic><topic>Population-based studies</topic><topic>Quantitative Trait, Heritable</topic><topic>review-article</topic><topic>Statistical power</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Laird, Nan M</creatorcontrib><creatorcontrib>Lange, Christoph</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Nature reviews. Genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Laird, Nan M</au><au>Lange, Christoph</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Family-based designs in the age of large-scale gene-association studies</atitle><jtitle>Nature reviews. Genetics</jtitle><stitle>Nat Rev Genet</stitle><addtitle>Nat Rev Genet</addtitle><date>2006-05-01</date><risdate>2006</risdate><volume>7</volume><issue>5</issue><spage>385</spage><epage>394</epage><pages>385-394</pages><issn>1471-0056</issn><eissn>1471-0064</eissn><abstract>Key Points
Either population-based or family-based designs can be used in gene-association studies. Population-based designs use unrelated individuals; family-based designs use probands and their relatives, typically either parents or siblings.
Genetic-association studies face the obstacles of population substructures and multiple testing.
Family-based designs are favoured because they are robust against confounding due to population substructures and test both linkage and association.
Case–control designs are preferred for the relative ease of data collection. They have modest power advantages, depending on the prevalence of the disease.
Family-based designs can be extended to incorporate pedigrees and complex phenotypes.
Screening tools are available for family-based designs that allow the multiple-testing problem, which is an important issue in whole-genome association studies, to be handled.
Although they are sometimes overlooked, family-based designs provide important advantages for detecting genetic associations in studies of complex disease. In particular, they provide a means of overcoming the problems that arise when multiple hypotheses are tested in genome-wide association studies.
Both population-based and family-based designs are commonly used in genetic association studies to locate genes that underlie complex diseases. The simplest version of the family-based design — the transmission disequilibrium test — is well known, but the numerous extensions that broaden its scope and power are less widely appreciated. Family-based designs have unique advantages over population-based designs, as they are robust against population admixture and stratification, allow both linkage and association to be tested for and offer a solution to the problem of model building. Furthermore, the fact that family-based designs contain both within- and between-family information has substantial benefits in terms of multiple-hypothesis testing, especially in the context of whole-genome association studies.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>16619052</pmid><doi>10.1038/nrg1839</doi><tpages>10</tpages></addata></record> |
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subjects | Agriculture Animal Genetics and Genomics Biological and medical sciences Biomedical and Life Sciences Biomedicine Cancer Research Case-Control Studies Disease Family Fundamental and applied biological sciences. Psychology Gene Function Genes Genetic Techniques Genetics of eukaryotes. Biological and molecular evolution Genetics, Population Genome, Human Genomes Haplotypes Human Genetics Humans Hypotheses Hypothesis testing Linkage Disequilibrium Models, Genetic Parents & parenting Pedigree Phenotype Population-based studies Quantitative Trait, Heritable review-article Statistical power |
title | Family-based designs in the age of large-scale gene-association studies |
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