Dataset Information

Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.

ABSTRACT:

Background

Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.

Results

The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.

Conclusions

The proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/ .

SUBMITTER: Won S

PROVIDER: S-EPMC4339744 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Publications

Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.

Won Sungho S Kim Wonji W Lee Sungyoung S Lee Young Y Sung Joohon J Park Taesung T

BMC bioinformatics 20150215

<h4>Background</h4>Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.<h4>Results</h4>The proposed ...[more]

PMID: 25887481

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Project description:Suicidal behavior (SB) has a complex etiology of genes, environment or both. One of the genetic components in SB could be copy number variations (CNVs), since CNVs are implicated in a range of neurodevelopmental disorders. However, a recently published genome-wide and case-control study failed to observe a significant role of CNVs in SB (see E-MTAB-3519). Here we complement those initial observations by conducting a brief CNV-association study, for the first time in a family-based trio-sample with severe suicide attempt (SA) outcome in offspring (n=660 trios; the \GISS\ sample, see http://www.ncbi.nlm.nih.gov/pubmed/23422793 and refs therein). For association testing, we here used the FBAT-CNV methodology (http://www.ncbi.nlm.nih.gov/pubmed/18228561), which allows for CNV association testing directly on the raw intensity values (Illumina \log R ratio\), evaluating any type of CNVs (e.g. de novo or inherited) without reliance on CNV calling algorithms and robust to control selection biases. Here we have deposited these raw logR-values for 88,450 on-chip CNV-loci of the HumanOmni1-Quad_v1 chip, arranged in a standard pedigree format used as input for FBAT-CNV analysis in SVS software (goldenhelix.com): column 1 is the family ID, column 2 is the Subject type (1 = offspring, 2 = mother, 3 = father), column 3 is the father ID (? for missing), column 4 is the mother ID (? for missing), column 5 is the sex (1 = female, 0 = male), column 6 is the affection status (1 = suicide attempt, 0 = not affected) and columns 7 and onward are the logR-values for each CNV-loci as is listed in the header row. We observed experiment-wide significant association (P ≤ 5.6 x 10-7) of two proximal CNVs at chromosome 14 (Illumina markers cnvi0108946 and cnvi0118308, with NCBI 36.3 start positions 22794779 and 22582820). However, these CNV-associations mapped to T-cell receptor regions, and therefore most likely reflect inter-individual variation in somatic rearrangements occurring in white blood cells (as our source of DNA was blood), rather than association with SA. In conclusion, our results did not suggest any major role of CNVs in SA etiology, in line with the results of the recent case-control study (see E-MTAB-3519).

Dataset Information

Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.

Background

Results

Conclusions

Publications

Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.

Similar Datasets

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