Joint analysis of tightly linked SNPs in screening step of genome-wide association studies leads to increased power

Becker, Tim; Herold, Christine

doi:10.1038/ejhg.2009.7

Cited by 17 publications

(19 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Haplotype-based algorithms were developed to cope with multiple comparison problems in GWAS at multiple SNP loci in LD. 29 Permutation testing is another approach to confront the problem of multiple comparisons, 30 because this method accounts for the exact correlation between tests and does not overcorrect by assuming that all tests are independent; hence, it is more powerful than family-wise type I error and FDR methods of correction. Recent reports suggest that the application of both haplotypebased and individual-SNP testing to GWAS should be adopted as a routine procedure.…”

Section: Discussionmentioning

confidence: 99%

Genetic variants that affect length/height in infancy/early childhood in Vietnamese-Korean families

et al. 2010

View full text Add to dashboard Cite

To identify genetic factors that influence height in infancy/early childhood , a family-based genome-wide association study was conducted using 269 888 single-nucleotide polymorphisms (SNPs) in 165 families composed of a Korean father, a Vietnamese mother and Vietnamese-Korean offspring in the International Marriage-based Immigrant Cohort in Korea. In a single-SNP-based analysis, the six SNPs in or near genes MAF, MAGI2, BMP4 and PTPN7 showed consistent suggestive associations at all height standard deviation scores using Korean, World Health Organization and Vietnamese growth references. Analyzing the haplotypes for the genes, haplotype blocks were found to be significantly associated with height. Similar to the results of a contiguous haplotype analysis using tag SNPs as above, noncontiguous haplotypes of variable length also showed a significant association near the suspected loci. Our result suggests that height during infancy/early childhood may be regulated by genetic variations that differ from those of adults.

show abstract

Section: Discussionmentioning

confidence: 99%

Genetic variants that affect length/height in infancy/early childhood in Vietnamese-Korean families

et al. 2010

View full text Add to dashboard Cite

show abstract

“…We have previously reviewed that the presence of multiple independently associated SNPs within one gene is a common phenomenon [30] . None of the methods described above makes proper use of independent signals that remain after adjustment for LD.…”

Section: Gene and Pathway Scoresmentioning

confidence: 99%

Integrated Genome-Wide Pathway Association Analysis with INTERSNP

Herold

Mattheisen²,

Lacour

et al. 2012

Hum Hered

Self Cite

View full text Add to dashboard Cite

Objectives: Pathway association analysis (PAA) tests for an excess of moderately significant SNPs in genes from a common pathway. Methods: We present a Monte-Carlo simulation framework that allows to formulate the main ideas of existing PAA approaches using a self-contained rather than a competitive null hypothesis. A stand-alone implementation in INTERSNP makes time-consuming communication with standard GWAS software redundant. By additional parallelization with the OpenMP API, we achieve a reduction in running time for PAA by orders of magnitude, making a power simulation study for PAA feasible. Our approach properly accounts for linkage disequilibrium and is robust with respect to residual λ inflation. Results: We demonstrate that under simple, realistic disease models, PAA can actually strongly outperform the GWAS single-marker approach. PAA methods that make use of the strength of the SNP association (GenGen, Fisher’s combination test), in general, perform better than ratio-based methods (ALIGATOR, SNP ratio), whereas the relative performance of gene-based scoring (ALIGATOR, GenGen) and pathway-based scoring (SNP ratio, Fisher’s combination test) depends on the architecture of the assumed disease model. Finally, we present a new PAA score that models independent signals from the same gene in a regression framework and show that it is a reasonable compromise that combines the advantages of existing ideas.

show abstract

“…One would then expect a haplotypic test to have higher power than a single-marker test for detecting an ungenotyped low frequency causal variant. Indeed, a previous simulation study suggests that multimarker tests can have higher power than single-marker tests, 5 and a previous genome-wide haplotypic study found a gene cluster associated with coronary artery disease that was not found with genome-wide SNP testing. 6 One haplotype association test with appealing properties is the Beagle haplotype cluster test.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide haplotypic testing in a Finnish cohort identifies a novel association with low-density lipoprotein cholesterol

Zhang

Browning

2014

Eur J Hum Genet

View full text Add to dashboard Cite

We performed genome-wide tests for association between haplotype clusters and each of 9 metabolic traits in a cohort of 5402 Northern Finnish individuals genotyped for 330 000 single-nucleotide polymorphisms. The metabolic traits were body mass index, C-reactive protein, diastolic blood pressure, glucose, high-density lipoprotein (HDL), insulin, low-density lipoprotein (LDL), systolic blood pressure, and triglycerides. Haplotype clusters were determined using Beagle. There were LDL-associated clusters in the chromosome 4q13.3-q21.1 region containing the albumin (ALB) and platelet factor 4 (PF4) genes. This region has not been associated with LDL in previous genome-wide association studies. The most significant haplotype cluster in this region was associated with 0.488 mmol/l higher LDL (95% CI: 0.361-0.615 mmol/l, P-value: 6.4 Â 10 À14 ). We also observed three previously reported associations: Chromosome 16q13 with HDL, chromosome 1p32. INTRODUCTIONThe identification of genetic factors that influence quantitative traits such as low-density lipoprotein (LDL) has clinical importance. For example, higher LDL levels are associated with increased risk of cardiovascular health disease, and the discovery of the association between PCSK9 mutations and LDL led to the development of PCSK9 inhibitors as a novel class of LDL-reducing drugs. 1 Notably, in the cohort study that found the LDL-associated PCSK9 mutations, each mutation was present in fewer than 2% of study individuals. 2 The standard single-SNP analysis commonly employed in genome-wide association studies (GWAS) has low power for detecting such low frequency causal variants. By employing haplotypic analysis in combination with single-SNP analysis, we can improve power above that of single-SNP analysis alone for detecting causal variants with low minor allele frequency. 3 The standard approach for association analysis of genome-wide single-nucleotide polymorphism (SNP) array data in population samples is to test each SNP individually for association with the trait. This approach can have high power to detect an ungenotyped causal variant when the causal variant is common and correlated with one or more genotyped variants on the array. However, the single-SNP approach has lower power to detect low frequency ungenotyped causal variants. 3 To improve power for low frequency variants, one could impute them and test for association with the trait, but imputation of low frequency variants suffers from poor accuracy. 4 Moreover, variants that are unique to the population of interest

show abstract

Joint analysis of tightly linked SNPs in screening step of genome-wide association studies leads to increased power

Cited by 17 publications

References 79 publications

Genetic variants that affect length/height in infancy/early childhood in Vietnamese-Korean families

Genetic variants that affect length/height in infancy/early childhood in Vietnamese-Korean families

Integrated Genome-Wide Pathway Association Analysis with INTERSNP

Genome-wide haplotypic testing in a Finnish cohort identifies a novel association with low-density lipoprotein cholesterol

Contact Info

Product

Resources

About