Statistical analysis of rare sequence variants: an overview of collapsing methods

Dering, Carmen; Hemmelmann, Claudia; Pugh, Elizabeth; Ziegler, Andreas

doi:10.1002/gepi.20643

Cited by 144 publications

(148 citation statements)

References 33 publications

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“…This realization gave way to the search for missing heritability in the post-GWAS era wherein rare variants have been heralded to be an important key, as they were not genotyped in GWAS. This paradigm shift fueled with concurrent availability of the next-generation sequencing (NGS) data lead to burgeoning of methods for collectively analyzing rare single nucleotide variants (rSNV), typically referred as 'collapsing methods' [3,4]. Nonetheless, there are still many limitations associated with NGS data such as accuracy of genotype calling and cost, and as collapsing methods rely on these data solely, they inherit these limitations as well [5].…”

Section: Introductionmentioning

confidence: 99%

Comparison of haplotype-based statistical tests for disease association with rare and common variants

Datta

Biswas

2015

Brief Bioinform

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Recent literature has highlighted the advantages of haplotype association methods for detecting rare variants associated with common diseases. As several new haplotype association methods have been proposed in the past few years, a comparison of new and standard methods is important and timely for guidance to the practitioners. We consider nine methods-Haplo.score, Haplo.glm, Hapassoc, Bayesian hierarchical Generalized Linear Model (BhGLM), Logistic Bayesian LASSO (LBL), regularized GLM (rGLM), Haplotype Kernel Association Test, wei-SIMc-matching and Weighted Haplotype and Imputation-based Tests. These can be divided into two types-individual haplotype-specific tests and global tests depending on whether there is just one overall test for a haplotype region (global) or there is an individual test for each haplotype in the region. Haplo.score is the only method that tests for both; Haplo.glm, Hapassoc, BhGLM and LBL are individual haplotype-specific, while the rest are global tests. For comparison, we also apply a popular collapsing method-Sequence Kernel Association Test (SKAT) and its two variants-SKAT-O (Optimal) and SKAT-C (Combined). We carry out an extensive comparison on our simulated data sets as well as on the Genetic Analysis Workshop (GAW) 18 simulated data. Further, we apply the methods to GAW18 real hypertension data and Dallas Heart Study sequence data. We find that LBL, Haplo.score (global test) and rGLM perform well over the scenarios considered here. Also, haplotype methods are more powerful (albeit more computationally intensive) than SKAT and its variants in scenarios where multiple causal variants act interactively to produce haplotype effects.

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Section: Introductionmentioning

confidence: 99%

Comparison of haplotype-based statistical tests for disease association with rare and common variants

Datta

Biswas

2015

Brief Bioinform

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“…Collapsing-based methods, without a doubt, constitute the predominant class, composed of most of the rare variant association methods proposed to date. [21][22][23][24][25] However, recent works have suggested that haplotyping-based methods may offer advantages and can be more powerful than collapsing methods for some underlying disease settings. 7,8,10 It is in that vein that hapKL is proposed to further explore the benefits of haplotypingbased approaches using existing common SNP data for detecting rare variants that are associated with common diseases without the need to rely on newer sequencing data.…”

Section: Discussionmentioning

confidence: 99%

Kullback–Leibler divergence for detection of rare haplotype common disease association

Lin

2015

Eur J Hum Genet

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Rare haplotypes may tag rare causal variants of common diseases; hence, detection of such rare haplotypes may also contribute to our understanding of complex disease etiology. Because rare haplotypes frequently result from common single-nucleotide polymorphisms (SNPs), focusing on rare haplotypes is much more economical compared with using rare single-nucleotide variants (SNVs) from sequencing, as SNPs are available and 'free' from already amassed genome-wide studies. Further, associated haplotypes may shed light on the underlying disease causal mechanism, a feat unmatched by SNV-based collapsing methods. In recent years, data mining approaches have been adapted to detect rare haplotype association. However, as they rely on an assumed underlying disease model and require the specification of a null haplotype, results can be erroneous if such assumptions are violated. In this paper, we present a haplotype association method based on Kullback-Leibler divergence (hapKL) for case-control samples. The idea is to compare haplotype frequencies for the cases versus the controls by computing symmetrical divergence measures. An important property of such measures is that both the frequencies and logarithms of the frequencies contribute in parallel, thus balancing the contributions from rare and common, and accommodating both deleterious and protective, haplotypes. A simulation study under various scenarios shows that hapKL has well-controlled type I error rates and good power compared with existing data mining methods. Application of hapKL to age-related macular degeneration (AMD) shows a strong association of the complement factor H (CFH) gene with AMD, identifying several individual rare haplotypes with strong signals.

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“…However, indirect association testing assumes low-level allelic heterogeneity and assumes that the variants are common [21,44]. Rare variants have low MAF and low r 2 values and thus exhibit poor tagging properties with common variants (see Table 3) [21,51]. Inappropriate indirect SNP association testing runs a high risk of false-negative results because rare functional variants can be inadequately tagged.…”

Section: Current Methods To Analyze Low Frequency Variationmentioning

confidence: 99%

A Biologically Informed Method for Detecting Associations with Rare Variants

Buchanan

Wallace

Frase

et al. 2012

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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Statistical analysis of rare sequence variants: an overview of collapsing methods

Cited by 144 publications

References 33 publications

Comparison of haplotype-based statistical tests for disease association with rare and common variants

Comparison of haplotype-based statistical tests for disease association with rare and common variants

Kullback–Leibler divergence for detection of rare haplotype common disease association

A Biologically Informed Method for Detecting Associations with Rare Variants

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