Human pedigrees are useful not only for locating disease genes, through linkage analysis, but also for verifying relationships among individuals, detection of genotyping errors and for identification of haplotypes. We propose to build tools and methods for much faster, memory-efficient pedigree analysis that will enable geneticists to analyze thousands of single-nucleotide polymorphism (SNP) markers and larger family datasets. Our preliminary investigations in this area have already enabled geneticists to tackle larger problems than was previously feasible. For example, Merlin, a program we developed, allowed investigators at the Sanger Center and the University of Oxford to characterize chromosome wide haplotypes for 1504 chromosome 22 SNP markers in 7 pedigrees.
Specific aims for this project include further improvements in the estimation of haplotypes in pedigrees, superior detection and modeling of genotype error, and a comprehensive simulation framework for evaluating linkage findings in situations where multiple phenotypes are considered. We propose to allow for differences between male-and-female recombination rates and for X-chromosome data in our methods and software. The advances we propose will be especially useful in projects that seek to identify the genetic basis of complex disease, such as cardiovascular disease, diabetes, obesity and asthma. These diseases are often described by multiple non-independent phenotypes, so that findings must be evaluated through computationally demanding simulation, and are also the most likely to rely on high-throughput SNP genotyping for fine mapping. Our tools will also be important for projects currently underway that seek to identify and catalog common haplotypes in the human genome. The advances we propose will turn many of the analyses that are now challenging into routine and allow investigators to extract the benefits of new high-throughput data-sources in the genetic dissection of complex traits.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
3R01HG002651-05S1
Application #
7659860
Study Section
Genome Study Section (GNM)
Program Officer
Brooks, Lisa
Project Start
2003-05-12
Project End
2010-08-31
Budget Start
2007-05-01
Budget End
2010-08-31
Support Year
5
Fiscal Year
2008
Total Cost
$200,740
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Justice, Anne E (see original citation for additional authors) (2017) Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat Commun 8:14977
Feng, Shuang; Pistis, Giorgio; Zhang, He et al. (2015) Methods for association analysis and meta-analysis of rare variants in families. Genet Epidemiol 39:227-38
Ozel, A Bilge; Moroi, Sayoko E; Reed, David M et al. (2014) Genome-wide association study and meta-analysis of intraocular pressure. Hum Genet 133:41-57
Meirelles, Osorio D; Ding, Jun; Tanaka, Toshiko et al. (2013) SHAVE: shrinkage estimator measured for multiple visits increases power in GWAS of quantitative traits. Eur J Hum Genet 21:673-9
Randall, Joshua C (see original citation for additional authors) (2013) Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet 9:e1003500
Naitza, Silvia; Porcu, Eleonora; Steri, Maristella et al. (2012) A genome-wide association scan on the levels of markers of inflammation in Sardinians reveals associations that underpin its complex regulation. PLoS Genet 8:e1002480
Liu, Dajiang J; Leal, Suzanne M (2012) A unified method for detecting secondary trait associations with rare variants: application to sequence data. PLoS Genet 8:e1003075
Yang, Jian; Loos, Ruth J F; Powell, Joseph E et al. (2012) FTO genotype is associated with phenotypic variability of body mass index. Nature 490:267-72
Howie, Bryan; Fuchsberger, Christian; Stephens, Matthew et al. (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44:955-9
Sanna, Serena; Li, Bingshan; Mulas, Antonella et al. (2011) Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet 7:e1002198

Showing the most recent 10 out of 51 publications