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A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program.

ABSTRACT: A combined analysis of genome scans for obesity was undertaken using the interim results from the National Heart, Lung, and Blood Institute Family Blood Pressure Program. In this research project, four multicenter networks of investigators conducted eight individual studies. Data were available on 6,849 individuals from four ethnic groups (white, black, Mexican American, and Asian). The sample represents the largest single collection of genomewide scan data that has been analyzed for obesity and provides a test of the reproducibility of linkage analysis for a complex phenotype. Body mass index (BMI) was used as the measure of adiposity. Genomewide linkage analyses were first performed separately in each of the eight ethnic groups in the four networks, through use of the variance-component method. Only one region in the analyses of the individual studies showed significant linkage with BMI: 3q22.1 (LOD 3.45, for the GENOA network black sample). Six additional regions were found with an associated LOD >2, including 3p24.1, 7p15.2, 7q22.3, 14q24.3, 16q12.2, and 17p11.2. Among these findings, the linkage at 7p15.2, 7q22.3, and 17p11.2 has been reported elsewhere. A modified Fisher's omnibus procedure was then used to combine the P values from each of the eight genome scans. A complimentary approach to the meta-analysis was undertaken, combining the average allele-sharing identity by descent (pi) for whites, blacks, and Mexican Americans. Using this approach, we found strong linkage evidence for a quantitative-trait locus at 3q27 (marker D3S2427; LOD 3.40, P=.03). The same location has been shown to be linked with obesity-related traits and diabetes in at least two other studies. These results (1) confirm the previously reported obesity-susceptibility locus on chromosomes 3, 7, and 17 and (2) demonstrate that combining samples from different studies can increase the power to detect common genes with a small-to-moderate effect, so long as the same gene has an effect in all samples considered.

SUBMITTER: Wu X

PROVIDER: S-EPMC447599 | biostudies-literature | 2002 May

REPOSITORIES: biostudies-literature

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A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program.

Wu Xiaodong X Cooper Richard S RS Borecki Ingrid I Hanis Craig C Bray Molly M Lewis Cora E CE Zhu Xiaofeng X Kan Donghui D Luke Amy A Curb David D

American journal of human genetics 20020328 5

A combined analysis of genome scans for obesity was undertaken using the interim results from the National Heart, Lung, and Blood Institute Family Blood Pressure Program. In this research project, four multicenter networks of investigators conducted eight individual studies. Data were available on 6,849 individuals from four ethnic groups (white, black, Mexican American, and Asian). The sample represents the largest single collection of genomewide scan data that has been analyzed for obesity and ...[more]

PMID: 11923912

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Project description:With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ~860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ~6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.

Dataset Information

A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program.

Publications

A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program.

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