PBAT: A comprehensive software package for genome-wide association analysis of complex family-based studies

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Abstract

The PBAT software package (v2.5) provides a unique set of tools for complex family-based association analysis at a genome-wide level. PBAT can handle nuclear families with missing parental genotypes, extended pedigrees with missing genotypic information, analysis of single nucleotide polymorphisms (SNPs), haplotype analysis, quantitative traits, multivariate/longitudinal data and time to onset phenotypes. The data analysis can be adjusted for covariates and gene/environment interactions. Haplotype-based features include sliding windows and the reconstruction of the haplotypes of the probands. PBAT's screening tools allow the user successfully to handle the multiple comparisons problem at a genome-wide level, even for 100,000 SNPs and more. Moreover, PBAT is computationally fast. A genome scan of 300,000 SNPs in 2,000 trios takes 4 central processing unit (CPU)-days. PBAT is available for Linux, Sun Solaris and Windows XP.

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Most cited references 13

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A general test of association for quantitative traits in nuclear families.

G Abecasis, L R Cardon, W Cookson (2000)

High-resolution mapping is an important step in the identification of complex disease genes. In outbred populations, linkage disequilibrium is expected to operate over short distances and could provide a powerful fine-mapping tool. Here we build on recently developed methods for linkage-disequilibrium mapping of quantitative traits to construct a general approach that can accommodate nuclear families of any size, with or without parental information. Variance components are used to construct a test that utilizes information from all available offspring but that is not biased in the presence of linkage or familiality. A permutation test is described for situations in which maximum-likelihood estimates of the variance components are biased. Simulation studies are used to investigate power and error rates of this approach and to highlight situations in which violations of multivariate normality assumptions warrant the permutation test. The relationship between power and the level of linkage disequilibrium for this test suggests that the method is well suited to the analysis of dense maps. The relationship between power and family structure is investigated, and these results are applicable to study design in complex disease, especially for late-onset conditions for which parents are usually not available. When parental genotypes are available, power does not depend greatly on the number of offspring in each family. Power decreases when parental genotypes are not available, but the loss in power is negligible when four or more offspring per family are genotyped. Finally, it is shown that, when siblings are available, the total number of genotypes required in order to achieve comparable power is smaller if parents are not genotyped.

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Implementing a unified approach to family-based tests of association.

N M Laird, S Horvath, X. Xu (2000)

We describe a broad class of family-based association tests that are adjusted for admixture; use either dichotomous or measured phenotypes; accommodate phenotype-unknown subjects; use nuclear families, sibships or a combination of the two, permit multiple nuclear families from a single pedigree; incorporate di- or multi-allelic marker data; allow additive, dominant or recessive models; and permit adjustment for covariates and gene-by-environment interactions. The test statistic is basically the covariance between a user-specified function of the genotype and a user-specified function of the trait. The distribution of the statistic is computed using the appropriate conditional distribution of offspring genotypes that adjusts for admixture.

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A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes.

Heather J. Cordell, G Clayton (2001)

A stepwise logistic-regression procedure is proposed for evaluation of the relative importance of variants at different sites within a small genetic region. By fitting statistical models with main effects, rather than modeling the full haplotype effects, we generate tests, with few degrees of freedom, that are likely to be powerful for detecting primary etiological determinants. The approach is applicable to either case/control or nuclear-family data, with case/control data modeled via unconditional and family data via conditional logistic regression. Four different conditioning strategies are proposed for evaluation of effects at multiple, closely linked loci when family data are used. The first strategy results in a likelihood that is equivalent to analysis of a matched case/control study with each affected offspring matched to three pseudocontrols, whereas the second strategy is equivalent to matching each affected offspring with between one and three pseudocontrols. Both of these strategies require you be able to infer parental phase (i.e., those haplotypes present in the parents). Families in which phase cannot be determined must be discarded, which can considerably reduce the effective size of a data set, particularly when large numbers of loci that are not very polymorphic are being considered. Therefore, a third strategy is proposed in which knowledge of parental phase is not required, which allows those families with ambiguous phase to be included in the analysis. The fourth and final strategy is to use conditioning method 2 when parental phase can be inferred and to use conditioning method 3 otherwise. The methods are illustrated using nuclear-family data to evaluate the contribution of loci in the HLA region to the development of type 1 diabetes.

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Author and article information

Contributors

Christoph Lange

Journal

Journal ID (nlm-ta): Hum Genomics

Journal ID (iso-abbrev): Hum. Genomics

Title: Human Genomics

Publisher: BioMed Central

ISSN (Print): 1473-9542

ISSN (Electronic): 1479-7364

Publication date Collection: 2005

Publication date (Electronic): 1 March 2005

Volume: 2

Issue: 1

Pages: 67-69

Affiliations

[1 ]Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

[2 ]Harvard Medical School, Channing Laboratory, Boston, MA 02115, USA

Article

Publisher ID: 1479-7364-2-1-67

DOI: 10.1186/1479-7364-2-1-67

PMC ID: 3525120

PubMed ID: 15814068

SO-VID: b3f69a98-581e-4d94-b45b-3e292af4cb9d

History

Date received : 7 December 2004

Date accepted : 7 December 2004

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PBAT: A comprehensive software package for genome-wide association analysis of complex family-based studies

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Most cited references 13

A general test of association for quantitative traits in nuclear families.

Implementing a unified approach to family-based tests of association.

A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes.

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