Dataset Information

On the association analysis of CNV data: a fast and robust family-based association method.

ABSTRACT:

Background

Copy number variation (CNV) is known to play an important role in the genetics of complex diseases and several methods have been proposed to detect association of CNV with phenotypes of interest. Statistical methods for CNV association analysis can be categorized into two different strategies. First, the copy number is estimated by maximum likelihood and association of the expected copy number with the phenotype is tested. Second, the observed probe intensity measurements can be directly used to detect association of CNV with the phenotypes of interest.

Results

For each strategy we provide a statistic that can be applied to extended families. The computational efficiency of the proposed methods enables genome-wide association analysis and we show with simulation studies that the proposed methods outperform other existing approaches. In particular, we found that the first strategy is always more efficient than the second strategy no matter whether copy numbers for each individual are well identified or not. With the proposed methods, we performed genome-wide CNV association analyses of hematological trait, hematocrit, on 521 Korean family samples.

Conclusions

We found that statistical analysis with the expected copy number is more powerful than the statistic with the probe intensity measurements regardless of the accuracy of the estimation of copy numbers.

SUBMITTER: Liu M

PROVIDER: S-EPMC5395793 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHaplotype-based approaches have been extensively studied for case-control association mapping in recent years. It has been shown that haplotype methods can provide more consistent results comparing to single-locus based approaches, especially in cases where causal variants are not typed. Improved power has been observed by clustering similar or rare haplotypes into groups to reduce the degrees of freedom of association tests. For family-based association studies, one commonly used strategy is Transmission Disequilibrium Tests (TDT), which examine the imbalanced transmission of alleles/haplotypes to affected and normal children. Many extensions have been developed to deal with general pedigrees and continuous traits.ResultsIn this paper, we propose a new haplotype-based association method for family data that is different from the TDT framework. Our approach (termed F_HapMiner) is based on our previous successful experiences on haplotype inference from pedigree data and haplotype-based association mapping. It first infers diplotype pairs of each individual in each pedigree assuming no recombination within a family. A phenotype score is then defined for each founder haplotype. Finally, F_HapMiner applies a clustering algorithm on those founder haplotypes based on their similarities and identifies haplotype clusters that show significant associations with diseases/traits. We have performed extensive simulations based on realistic assumptions to evaluate the effectiveness of the proposed approach by considering different factors such as allele frequency, linkage disequilibrium (LD) structure, disease model and sample size. Comparisons with single-locus and haplotype-based TDT methods demonstrate that our approach consistently outperforms the TDT-based approaches regardless of disease models, local LD structures or allele/haplotype frequencies.ConclusionWe present a novel haplotype-based association approach using family data. Experiment results demonstrate that it achieves significantly higher power than TDT-based approaches.