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A New Method for Detecting Associations with Rare Copy-Number Variants.

Tzeng JY, Magnusson PK, Sullivan PF, Swedish Schizophrenia ConsortiumSzatkiewicz JP - PLoS Genet. (2015)

Bottom Line: CCRET is applicable to variants measured on a multi-categorical scale, collectively modeling the effects of multiple CNV features, and is robust to etiological heterogeneity.Multiple confounders can be simultaneously corrected.We show that CCRET has powerful and robust performance under multiple types of etiological heterogeneity, and has performance comparable to or better than existing methods when there is no heterogeneity.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America; Department of Statistics, National Cheng-Kung University, Tainan, Taiwan.

ABSTRACT
Copy number variants (CNVs) play an important role in the etiology of many diseases such as cancers and psychiatric disorders. Due to a modest marginal effect size or the rarity of the CNVs, collapsing rare CNVs together and collectively evaluating their effect serves as a key approach to evaluating the collective effect of rare CNVs on disease risk. While a plethora of powerful collapsing methods are available for sequence variants (e.g., SNPs) in association analysis, these methods cannot be directly applied to rare CNVs due to the CNV-specific challenges, i.e., the multi-faceted nature of CNV polymorphisms (e.g., CNVs vary in size, type, dosage, and details of gene disruption), and etiological heterogeneity (e.g., heterogeneous effects of duplications and deletions that occur within a locus or in different loci). Existing CNV collapsing analysis methods (a.k.a. the burden test) tend to have suboptimal performance due to the fact that these methods often ignore heterogeneity and evaluate only the marginal effects of a CNV feature. We introduce CCRET, a random effects test for collapsing rare CNVs when searching for disease associations. CCRET is applicable to variants measured on a multi-categorical scale, collectively modeling the effects of multiple CNV features, and is robust to etiological heterogeneity. Multiple confounders can be simultaneously corrected. To evaluate the performance of CCRET, we conducted extensive simulations and analyzed large-scale schizophrenia datasets. We show that CCRET has powerful and robust performance under multiple types of etiological heterogeneity, and has performance comparable to or better than existing methods when there is no heterogeneity.

No MeSH data available.


Related in: MedlinePlus

Power comparison between CCRET and PLINK 2-sided tests for simulation I-B: within-locus heterogeneity of the dosage simulation, under 5 heterogeneity models as detailed in section “Simulation Design”.Black O line: CCRET; Blue Δ: PLINK 2-sided test analyzing deletions and duplications combined; Green +: PLINK 2-sided test analyzing only duplications; Magenta x: PLINK 2-sided test analyzing only deletions.
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pgen.1005403.g005: Power comparison between CCRET and PLINK 2-sided tests for simulation I-B: within-locus heterogeneity of the dosage simulation, under 5 heterogeneity models as detailed in section “Simulation Design”.Black O line: CCRET; Blue Δ: PLINK 2-sided test analyzing deletions and duplications combined; Green +: PLINK 2-sided test analyzing only duplications; Magenta x: PLINK 2-sided test analyzing only deletions.

Mentions: Table 1 shows that the type I error rates were around the nominal level for all methods. The power results (Fig 5) show that CCRET had power comparable to or better than the best PLINK test across all heterogeneity models considered. Again, the best PLINK test varied across heterogeneity models, but overall it focused on the CNV allele with risk-associated effects. The PLINK tests that focused on the protective (neutral) alleles had low (no) power. PLINK.all had power similar to and between PLINK.dup and PLINK.del, except in the case where within-locus heterogeneity did not exist, i.e., (R,R).


A New Method for Detecting Associations with Rare Copy-Number Variants.

Tzeng JY, Magnusson PK, Sullivan PF, Swedish Schizophrenia ConsortiumSzatkiewicz JP - PLoS Genet. (2015)

Power comparison between CCRET and PLINK 2-sided tests for simulation I-B: within-locus heterogeneity of the dosage simulation, under 5 heterogeneity models as detailed in section “Simulation Design”.Black O line: CCRET; Blue Δ: PLINK 2-sided test analyzing deletions and duplications combined; Green +: PLINK 2-sided test analyzing only duplications; Magenta x: PLINK 2-sided test analyzing only deletions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4592002&req=5

pgen.1005403.g005: Power comparison between CCRET and PLINK 2-sided tests for simulation I-B: within-locus heterogeneity of the dosage simulation, under 5 heterogeneity models as detailed in section “Simulation Design”.Black O line: CCRET; Blue Δ: PLINK 2-sided test analyzing deletions and duplications combined; Green +: PLINK 2-sided test analyzing only duplications; Magenta x: PLINK 2-sided test analyzing only deletions.
Mentions: Table 1 shows that the type I error rates were around the nominal level for all methods. The power results (Fig 5) show that CCRET had power comparable to or better than the best PLINK test across all heterogeneity models considered. Again, the best PLINK test varied across heterogeneity models, but overall it focused on the CNV allele with risk-associated effects. The PLINK tests that focused on the protective (neutral) alleles had low (no) power. PLINK.all had power similar to and between PLINK.dup and PLINK.del, except in the case where within-locus heterogeneity did not exist, i.e., (R,R).

Bottom Line: CCRET is applicable to variants measured on a multi-categorical scale, collectively modeling the effects of multiple CNV features, and is robust to etiological heterogeneity.Multiple confounders can be simultaneously corrected.We show that CCRET has powerful and robust performance under multiple types of etiological heterogeneity, and has performance comparable to or better than existing methods when there is no heterogeneity.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America; Department of Statistics, National Cheng-Kung University, Tainan, Taiwan.

ABSTRACT
Copy number variants (CNVs) play an important role in the etiology of many diseases such as cancers and psychiatric disorders. Due to a modest marginal effect size or the rarity of the CNVs, collapsing rare CNVs together and collectively evaluating their effect serves as a key approach to evaluating the collective effect of rare CNVs on disease risk. While a plethora of powerful collapsing methods are available for sequence variants (e.g., SNPs) in association analysis, these methods cannot be directly applied to rare CNVs due to the CNV-specific challenges, i.e., the multi-faceted nature of CNV polymorphisms (e.g., CNVs vary in size, type, dosage, and details of gene disruption), and etiological heterogeneity (e.g., heterogeneous effects of duplications and deletions that occur within a locus or in different loci). Existing CNV collapsing analysis methods (a.k.a. the burden test) tend to have suboptimal performance due to the fact that these methods often ignore heterogeneity and evaluate only the marginal effects of a CNV feature. We introduce CCRET, a random effects test for collapsing rare CNVs when searching for disease associations. CCRET is applicable to variants measured on a multi-categorical scale, collectively modeling the effects of multiple CNV features, and is robust to etiological heterogeneity. Multiple confounders can be simultaneously corrected. To evaluate the performance of CCRET, we conducted extensive simulations and analyzed large-scale schizophrenia datasets. We show that CCRET has powerful and robust performance under multiple types of etiological heterogeneity, and has performance comparable to or better than existing methods when there is no heterogeneity.

No MeSH data available.


Related in: MedlinePlus