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A unified framework for multi-locus association analysis of both common and rare variants.

Shriner D, Vaughan LK - BMC Genomics (2011)

Bottom Line: We identified a single risk haplotype with a directionally consistent effect in both samples in the gene GAK, which is involved in clathrin-mediated membrane trafficking.We also found suggestive evidence that directionally inconsistent marginal effects from single marker analysis appeared to result from risk being driven by different haplotypes in the two samples for the genes SYN3 and NGLY1, which are involved in neurotransmitter release and proteasomal degradation, respectively.These results illustrate the utility of our unified framework for genome-wide association analysis of common, complex diseases.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA. shrinerda@mail.nih.gov

ABSTRACT

Background: Common, complex diseases are hypothesized to result from a combination of common and rare genetic variants. We developed a unified framework for the joint association testing of both types of variants. Within the framework, we developed a union-intersection test suitable for genome-wide analysis of single nucleotide polymorphisms (SNPs), candidate gene data, as well as medical sequencing data. The union-intersection test is a composite test of association of genotype frequencies and differential correlation among markers.

Results: We demonstrated by computer simulation that the false positive error rate was controlled at the expected level. We also demonstrated scenarios in which the multi-locus test was more powerful than traditional single marker analysis. To illustrate use of the union-intersection test with real data, we analyzed a publically available data set of 319,813 autosomal SNPs genotyped for 938 cases of Parkinson disease and 863 neurologically normal controls for which no genome-wide significant results were found by traditional single marker analysis. We also analyzed an independent follow-up sample of 183 cases and 248 controls for replication.

Conclusions: We identified a single risk haplotype with a directionally consistent effect in both samples in the gene GAK, which is involved in clathrin-mediated membrane trafficking. We also found suggestive evidence that directionally inconsistent marginal effects from single marker analysis appeared to result from risk being driven by different haplotypes in the two samples for the genes SYN3 and NGLY1, which are involved in neurotransmitter release and proteasomal degradation, respectively. These results illustrate the utility of our unified framework for genome-wide association analysis of common, complex diseases.

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Quality control. A) Sample processing for the discovery sample. B) SNP processing for the discovery sample. C) Sample processing for the replication sample. D) SNP processing for the replication sample.
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Figure 2: Quality control. A) Sample processing for the discovery sample. B) SNP processing for the discovery sample. C) Sample processing for the replication sample. D) SNP processing for the replication sample.

Mentions: Data processing for quality control for both samples is depicted in Figure 2. For the discovery sample, we retained 938 of the 1,073 cases, 863 of the 1,009 controls, and 319,813 of the 344,301 SNPs. For the replication sample, we retained 183 of the 270 cases, 248 of the 270 controls, and 379,017 of the 408,803 SNPs. To investigate the possibility of population stratification in the discovery and replication samples, we estimated the variance inflation factor of the genomic control method [14,15]. We estimated an inflation factor of 1.05 for the discovery sample and 1.01 for the replication sample, indicating a negligible inflation of the false positive error rate due to population stratification (Additional File 9).


A unified framework for multi-locus association analysis of both common and rare variants.

Shriner D, Vaughan LK - BMC Genomics (2011)

Quality control. A) Sample processing for the discovery sample. B) SNP processing for the discovery sample. C) Sample processing for the replication sample. D) SNP processing for the replication sample.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3040731&req=5

Figure 2: Quality control. A) Sample processing for the discovery sample. B) SNP processing for the discovery sample. C) Sample processing for the replication sample. D) SNP processing for the replication sample.
Mentions: Data processing for quality control for both samples is depicted in Figure 2. For the discovery sample, we retained 938 of the 1,073 cases, 863 of the 1,009 controls, and 319,813 of the 344,301 SNPs. For the replication sample, we retained 183 of the 270 cases, 248 of the 270 controls, and 379,017 of the 408,803 SNPs. To investigate the possibility of population stratification in the discovery and replication samples, we estimated the variance inflation factor of the genomic control method [14,15]. We estimated an inflation factor of 1.05 for the discovery sample and 1.01 for the replication sample, indicating a negligible inflation of the false positive error rate due to population stratification (Additional File 9).

Bottom Line: We identified a single risk haplotype with a directionally consistent effect in both samples in the gene GAK, which is involved in clathrin-mediated membrane trafficking.We also found suggestive evidence that directionally inconsistent marginal effects from single marker analysis appeared to result from risk being driven by different haplotypes in the two samples for the genes SYN3 and NGLY1, which are involved in neurotransmitter release and proteasomal degradation, respectively.These results illustrate the utility of our unified framework for genome-wide association analysis of common, complex diseases.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA. shrinerda@mail.nih.gov

ABSTRACT

Background: Common, complex diseases are hypothesized to result from a combination of common and rare genetic variants. We developed a unified framework for the joint association testing of both types of variants. Within the framework, we developed a union-intersection test suitable for genome-wide analysis of single nucleotide polymorphisms (SNPs), candidate gene data, as well as medical sequencing data. The union-intersection test is a composite test of association of genotype frequencies and differential correlation among markers.

Results: We demonstrated by computer simulation that the false positive error rate was controlled at the expected level. We also demonstrated scenarios in which the multi-locus test was more powerful than traditional single marker analysis. To illustrate use of the union-intersection test with real data, we analyzed a publically available data set of 319,813 autosomal SNPs genotyped for 938 cases of Parkinson disease and 863 neurologically normal controls for which no genome-wide significant results were found by traditional single marker analysis. We also analyzed an independent follow-up sample of 183 cases and 248 controls for replication.

Conclusions: We identified a single risk haplotype with a directionally consistent effect in both samples in the gene GAK, which is involved in clathrin-mediated membrane trafficking. We also found suggestive evidence that directionally inconsistent marginal effects from single marker analysis appeared to result from risk being driven by different haplotypes in the two samples for the genes SYN3 and NGLY1, which are involved in neurotransmitter release and proteasomal degradation, respectively. These results illustrate the utility of our unified framework for genome-wide association analysis of common, complex diseases.

Show MeSH
Related in: MedlinePlus