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Performance of statistical methods on CHARGE targeted sequencing data.

Xing C, Dupuis J, Cupples LA - BMC Genet. (2014)

Bottom Line: Type I error is conservative when we consider variants with minor allele frequency (MAF) < 1%.Power is generally low, although it is relatively larger for Score-Seq.Greater numbers of causal variants and a greater proportion of variance improve the power, but it tends to be lower in the presence of bi-directionality of effects of causal genotypes, especially for Score-Seq.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Boston University, Boston, MA, USA. chuanhua.xing@gmail.com.

ABSTRACT

Background: The CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Sequencing Project is a national, collaborative effort from 3 studies: Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), and Atherosclerosis Risk in Communities (ARIC). It uses a case-cohort design, whereby a random sample of study participants is enriched with participants in extremes of traits. Although statistical methods are available to investigate the role of rare variants, few have evaluated their performance in a case-cohort design.

Results: We evaluate several methods, including the sequence kernel association test (SKAT), Score-Seq, and weighted (Madsen and Browning) and unweighted burden tests. Using genotypes from the CHARGE targeted-sequencing project for FHS (n = 1096), we simulate phenotypes in a large population for 11 correlated traits and then sample individuals to mimic the CHARGE Sequencing study design. We evaluate type I error and power for 77 targeted regions.

Conclusions: We provide some guidelines on the performance of these aggregate-based tests to detect associations with rare variants when applied to case-cohort study designs, using CHARGE targeted sequencing data. Type I error is conservative when we consider variants with minor allele frequency (MAF) < 1%. Power is generally low, although it is relatively larger for Score-Seq. Greater numbers of causal variants and a greater proportion of variance improve the power, but it tends to be lower in the presence of bi-directionality of effects of causal genotypes, especially for Score-Seq.

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Related in: MedlinePlus

R2over target regions.
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Related In: Results  -  Collection

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Fig4: R2over target regions.

Mentions: We further investigated the variation in power across the regions, using the proportion of variance explained by the variants for each region, where and yp are from equation (1). The calculated R2 over regions is given in Figure 4, with the overall mean R2 equal to 0.0040. Looking at Figures 3 and 4, both methods tended to have larger power when R2 is larger, particularly Score-Seq with a larger increase (Additional file 1: Figure S3 (d) in the Supplement).Figure 4


Performance of statistical methods on CHARGE targeted sequencing data.

Xing C, Dupuis J, Cupples LA - BMC Genet. (2014)

R2over target regions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4197341&req=5

Fig4: R2over target regions.
Mentions: We further investigated the variation in power across the regions, using the proportion of variance explained by the variants for each region, where and yp are from equation (1). The calculated R2 over regions is given in Figure 4, with the overall mean R2 equal to 0.0040. Looking at Figures 3 and 4, both methods tended to have larger power when R2 is larger, particularly Score-Seq with a larger increase (Additional file 1: Figure S3 (d) in the Supplement).Figure 4

Bottom Line: Type I error is conservative when we consider variants with minor allele frequency (MAF) < 1%.Power is generally low, although it is relatively larger for Score-Seq.Greater numbers of causal variants and a greater proportion of variance improve the power, but it tends to be lower in the presence of bi-directionality of effects of causal genotypes, especially for Score-Seq.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Boston University, Boston, MA, USA. chuanhua.xing@gmail.com.

ABSTRACT

Background: The CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Sequencing Project is a national, collaborative effort from 3 studies: Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), and Atherosclerosis Risk in Communities (ARIC). It uses a case-cohort design, whereby a random sample of study participants is enriched with participants in extremes of traits. Although statistical methods are available to investigate the role of rare variants, few have evaluated their performance in a case-cohort design.

Results: We evaluate several methods, including the sequence kernel association test (SKAT), Score-Seq, and weighted (Madsen and Browning) and unweighted burden tests. Using genotypes from the CHARGE targeted-sequencing project for FHS (n = 1096), we simulate phenotypes in a large population for 11 correlated traits and then sample individuals to mimic the CHARGE Sequencing study design. We evaluate type I error and power for 77 targeted regions.

Conclusions: We provide some guidelines on the performance of these aggregate-based tests to detect associations with rare variants when applied to case-cohort study designs, using CHARGE targeted sequencing data. Type I error is conservative when we consider variants with minor allele frequency (MAF) < 1%. Power is generally low, although it is relatively larger for Score-Seq. Greater numbers of causal variants and a greater proportion of variance improve the power, but it tends to be lower in the presence of bi-directionality of effects of causal genotypes, especially for Score-Seq.

Show MeSH
Related in: MedlinePlus