GeneScreen最新文献

{"title":"Regression models for association studies of quantitative trait loci in humans","authors":"Lon R. Cardon,&nbsp;Gonçalo R. Abecasis","doi":"10.1046/j.1466-9218.2000.00025.x","DOIUrl":"10.1046/j.1466-9218.2000.00025.x","url":null,"abstract":"<p>Regression analysis is a simple, computationally efficient and often robust method for assessment of genotype–phenotype relationships in quantitative traits. It has long been used in studies of familiality and selection, and recently has been further extended for linkage and association analysis, linkage disequilibrium mapping and population substructure assessment. We review and compare some of the most commonly used regression models and highlight some useful properties of one model via analysis of angiotensin-converting enzyme phenotype and marker data.</p>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 2","pages":"55-57"},"PeriodicalIF":0.0,"publicationDate":"2008-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2000.00025.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75940661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The genetics of type 2 diabetes: the consequences of complexity","authors":"Mark McCarthy","doi":"10.1046/j.1466-9218.2000.00017.x","DOIUrl":"10.1046/j.1466-9218.2000.00017.x","url":null,"abstract":"<p>Type 2 diabetes affects a significant proportion of the World’s population and shows strong familial association. A richer molecular understanding of the aetiological mechanisms is a prerequisite for the development of improved therapeutic and preventative options to ameliorate the burden of disease. The genome-scale analyses now possible should allow the major susceptibility genes for type 2 diabetes to be identified, provided due respect is paid to the inevitable complexities associated with the genetic analysis of multifactorial traits.</p>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 2","pages":"81-84"},"PeriodicalIF":0.0,"publicationDate":"2008-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2000.00017.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73815318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do placentation issues bias twin studies?","authors":"David Phillips","doi":"10.1046/j.1466-9218.2000.00018.x","DOIUrl":"10.1046/j.1466-9218.2000.00018.x","url":null,"abstract":"<p>Because monozygous twins are almost genetically identical whereas dizygous twins are genetically no more similar than siblings, greater similarity between members of a monozygotic twin pair than between a dizygotic pair is usually taken as evidence that the condition or trait being studied has a genetic contribution. However, besides greater similarity of genes, monozygous pairs may share more similar environments than do dizygotic pairs. This environmental sharing may occur in fetal life where close placental relationships lead to more similar fetal environments. It may also occur postnatally. If the twins are of the same sex and look alike, the parents may dress, feed and in other ways treat the twins more similarly. Whereas the effects of a more similar postnatal environment have been explored by comparisons of twins raised together or separately, remarkably little is known about the influence of prenatal exposures.</p>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 2","pages":"85-87"},"PeriodicalIF":0.0,"publicationDate":"2008-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2000.00018.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79441426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Some principles for modelling genotype–environment correlations and interactions with quantitative trait loci","authors":"Edwin J. C. G. Van Den Oord","doi":"10.1046/j.1466-9218.2000.00021.x","DOIUrl":"10.1046/j.1466-9218.2000.00021.x","url":null,"abstract":"<p>Genotype measurements will increasingly be used in scientific disciplines to study genotype–environment correlations and interactions, and strengthen research designs. This report summarizes some principles for using quantitative trait loci in statistical models. Special attention is paid to the importance of choosing a suitable parameterization, the use of parents and/or siblings to control for population stratification, and the modelling of heterogeneous and correlated error variances.</p>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 2","pages":"97-99"},"PeriodicalIF":0.0,"publicationDate":"2008-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2000.00021.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75636653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The power of genome-wide sib pair linkage scans for quantitative trait loci using the new Haseman–Elston regression method","authors":"P. C. Sham,&nbsp;J. H. Zhao","doi":"10.1046/j.1466-9218.2000.00031.x","DOIUrl":"10.1046/j.1466-9218.2000.00031.x","url":null,"abstract":"<p>Power calculations for linkage analysis are typically conducted on the assumption of a single locus that affects the trait. Here we report a simple procedure for conducting a power analysis for a genome-wide linkage scan of a quantitative trait under the influence of multiple loci. This procedure is designed for sib pair data analysed by the new Haseman–Elston regression method. The results show that samples as large as 10 000 sib pairs will often not allow quantitative trait loci (QTLs) to be clearly identified. Instead, linkage genome scan using sib pairs must be regarded as a blunt screening tool that will help to focus attention to 10%, or more, of the genome.</p>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 2","pages":"103-106"},"PeriodicalIF":0.0,"publicationDate":"2008-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2000.00031.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75171678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adding power to Haseman and Elston’s (1972) method","authors":"Robert C. Elston,&nbsp;Sanjay S. Shete","doi":"10.1046/j.1466-9218.2000.00028.x","DOIUrl":"10.1046/j.1466-9218.2000.00028.x","url":null,"abstract":"<p>Haseman and Elston¹ proposed a model-free method for testing linkage between a polymorphic marker and a quantitative trait locus from data on a sample of independent sib pairs. In that method the squared sib-pair trait difference is regressed on the estimated proportion of alleles shared by the sibs at a marker locus, a negative regression coefficient suggesting linkage. It is possible to obtain more power by modelling the sib covariance, as in the variance component method of linkage analysis, and yet retain a method that is computationally fast, involving only linear regression. To do this it is only necessary to change the dependent variable from the squared trait difference to the difference between the squared mean-corrected sum and the squared trait difference. The method can accommodate sibships of arbitrary size by using generalized least squares and can be made more powerful by weighting the two components. The method is robust in large samples in the presence of any trait distribution, and, in the case of ascertained samples, the mean can be chosen to maximize power.</p>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 2","pages":"63-64"},"PeriodicalIF":0.0,"publicationDate":"2008-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2000.00028.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76772857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The validity of twin studies","authors":"David M. Evans,&nbsp;Nicholas G. Martin","doi":"10.1046/j.1466-9218.2000.00027.x","DOIUrl":"10.1046/j.1466-9218.2000.00027.x","url":null,"abstract":"<p>The classical twin study is the most popular method for assessing the relative contribution of genes and environment to traits in human populations. Critics argue that several assumptions of the twin method are unjustified, and therefore results from twin studies are misleading. Specifically, it has been suggested that twins differ in important aspects from singletons, that monozygotic (MZ) and dizygotic (DZ) twins are not matched in their degree of environmental similarity, and that MZ twins are neither matched genetically nor in their prenatal environments. These criticisms are addressed and it is suggested that they do not provide serious impediments to the validity of the twin study.</p>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 2","pages":"77-79"},"PeriodicalIF":0.0,"publicationDate":"2008-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2000.00027.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80694474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence for a quantitative trait locus for plasma fibrinogen from a family–based association study","authors":"B. M. Mayosi,&nbsp;M. A. Vickers,&nbsp;F. R. Green,&nbsp;P. J. Ratcliffe,&nbsp;C. Julier,&nbsp;G. M. Lathrop,&nbsp;H. Watkins,&nbsp;B. Keavney","doi":"10.1046/j.1466-920x.2001.00037.x","DOIUrl":"10.1046/j.1466-920x.2001.00037.x","url":null,"abstract":"<p>Studies in unrelated individuals have shown an association between fibrinogen gene polymorphisms and plasma fibrinogen levels, which is itself a risk factor for coronary heart disease. Family-based studies have complementary strengths in the investigation of such hypothesized genetic associations. Genotypes at the β-fibrinogen -455G/A promoter polymorphism, and a neighbouring highly polymorphic microsatellite in the α-fibrinogen gene were examined for evidence of genetic linkage and association with plasma fibrinogen in 568 members of 97 Caucasian families. The heritability of plasma fibrinogen was 0.22 ± 0.08, <i>P</i> = 0.0007. There was no significant evidence of genetic linkage between the fibrinogen locus and plasma fibrinogen with either marker. In contrast, there was evidence for association of plasma fibrinogen with genotype at the β-455G/A polymorphism (χ₁² = 11.12; <i>P</i> = 0.0009). Tests examining allelic transmission from heterozygous parents confirmed this association (Monk’s test, T = 2.17; <i>P</i> = 0.03). Genotype at the β-455G/A polymorphism accounted for 2% of the observed variation in fibrinogen. This is equivalent to about 10% of the heritable component, suggesting the presence of other quantitative trait loci (QTL) in unlinked genes. Confirmation of the association of plasma fibrinogen with genotype at the β-455G/A polymorphism in families indicates that the association is due to the physical proximity of this marker to a QTL, although the effect of this QTL was too small to be detected by linkage in this study. These findings are of potential importance for the design of genetic studies of multifactorial quantitative traits.</p>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 3","pages":"151-155"},"PeriodicalIF":0.0,"publicationDate":"2002-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-920x.2001.00037.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72906706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic control of lipoprotein phenotypes in the laboratory opossum, Monodelphis domestica","authors":"David L. Rainwater,&nbsp;Candace M. Kammerer,&nbsp;Amareshwar T. K. Singh,&nbsp;Perry H. Moore Jr.,&nbsp;Mahmood Poushesh,&nbsp;Wendy R. Shelledy,&nbsp;Jane F. VandeBerg,&nbsp;Edward S. Robinson,&nbsp;John L. VandeBerg","doi":"10.1046/j.1466-9218.2001.00009.x","DOIUrl":"10.1046/j.1466-9218.2001.00009.x","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 \u0000 <p><b>Introduction </b> Monodelphis (<i>Monodelphis domestica</i>) shows 20-fold differences, that may be under genetic control, in individual responses to a high-fat challenge diet.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and methods</h3>\u0000 \u0000 <p>Two partially inbred lines of animals were derived by selectively breeding among high responders and low responders and we analysed data from these pedigreed animals and their F₁ progeny. A blood sample was taken from each animal while consuming the basal diet (basal sample). Each animal was then fed a high-fat, high-cholesterol challenge diet for 8 weeks prior to collection of a second blood sample (challenge sample). Lipoprotein measurements included cholesterol concentrations of high-density lipoproteins (HDL-C) and low-density lipoproteins (non-HDL-C) and particle size phenotypes for both.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Quantitative genetic analyses indicated strong heritabilities (range, 0.382–0.827) for each of the eight traits. We also tested for single genes with large effects on each trait (major genes). Segregation analyses provided evidence of major genes for three traits: basal non-HDL-C, challenge non-HDL-C, and challenge HDL-C; no major genes were detected for the lipoprotein size traits. Tests for pleiotropy, using bivariate one-locus segregation analyses, showed that the major locus for challenge HDL-C had no effect on the basal HDL-C and that the major locus for challenge non-HDL-C had no effect on basal non-HDL-C. However, the major gene for basal non-HDL-C did significantly influence challenge non-HDL-C.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We have found evidence for at least three genes influencing lipoprotein phenotypes under two dietary regimes. Identification of these genes may provide valuable insights into lipoprotein metabolism in other species, including man.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 3","pages":"117-124"},"PeriodicalIF":0.0,"publicationDate":"2002-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2001.00009.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86788115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods to identify population outliers using genetic markers","authors":"Sheila A. Fisher,&nbsp;Cathryn M. Lewis","doi":"10.1046/j.1466-9218.2001.00012.x","DOIUrl":"10.1046/j.1466-9218.2001.00012.x","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 \u0000 <p><b>Introduction </b> Genetic studies to identify linkage or association usually assume participants are sampled from a genetically homogeneous population, so that a single set of marker allele frequencies is appropriate for all individuals in the study.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We have developed a method to identify individuals who are population outliers, because the marker allele frequency distributions from which their genotypes arise differ from the distributions of the remaining individuals in the study. Using allele frequencies estimated from an independent sample, the genotype log likelihood (GLL) test statistic calculates the likelihood of each individual’s genotypes across all markers. Extreme values of the statistic indicate that the individual arises from a different population. The distribution of the test statistic is derived and its convergence under the central limit theorem discussed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Discussion</h3>\u0000 \u0000 <p>This method was applied to genome search data from rheumatoid arthritis which identified a single population outlier family. We used allele frequencies from different populations to show that 100 markers provides high power to identify outliers across a range of populations. The GLL test statistic can be used as a screening tool to identify outlier families in any genetic study with genotyping at independent markers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100575,"journal":{"name":"GeneScreen","volume":"1 3","pages":"125-129"},"PeriodicalIF":0.0,"publicationDate":"2002-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1046/j.1466-9218.2001.00012.x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84995121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}