Statistical analysis strategies for association studies involving rare variants

In the paper

“Rare variant effects can manifest as compound heterozygosity,90 the ‘unmasking’ of deleterious variants via deletions on a homologous chromosome12, and other haplotype context-dependent phenomena.”

What the sources say

• Not checked yetcites [90] Zschocke, J. Dominant versus recessive: molecular mechanisms in metabolic disease. J. Inherit. Metab. Dis. 31, 599–618 (2008). source
• Not checked yetcites [12] Cirulli, E. T. et al. Common genetic variation and performance on standardized cognitive tests. Eur. J. Hum. Genet. 18, 815–820 (2010). source

In the paper

“Controlling for false positive findings due to multiple testing is necessary.”

In the paper

“This strategy should increase the fraction of variation explained by the variants as a whole and hence increase the power to detect their collective, rather than individual, effects.”

What the sources say

• Not checked yetcites [33] Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010). source

In the paper

“Not all analysis methods proposed for rare variant studies, however, can accommodate additional factors in their formulations and hence leverage conditioning effects. In addition, not all models can accommodate quantitative trait analysis unless the phenotype is broken into quantiles and stratified analysis is pursued (Table 2).”

What the sources say

• Not checked yetcites Table 2

In the paper

“Although conceptually easier to accommodate in statistical analysis models, there is no reason to believe that the EAH setting is the rule rather than the exception with respect to rare variant influences on phenotypic expression.”

In the paper

“Although there are a number of ways to leverage functional annotations to guide the collapsing of rare variants in association studies, their use will only be as good as the science behind those annotations.”

In the paper

“Despite the success of genome wide association (GWA) studies in identifying common single nucleotide variants (SNVs) that contribute to complex diseases1, the vast majority of genetic variants contributing to disease susceptibility are yet to be discovered.”

What the sources say

• Not checked yetcites [1] Manolio, T. A., Brooks, L. D. & Collins, F. S. A HapMap harvest of insights into the genetics of common disease. J. Clin. Invest. 118, 1590–1605 (2008). source
• Not checked yetcites [2] Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009). This paper describes the motivation for considering alternative approaches to discovering the genes that influence common complex diseases. It essentially argues that current GWA study paradigms focusing on common variants have failed to identify the majority of genetic variants that influence particular phenotypes. source

In the paper

“In addition, flaws with measures such as Fst and Gst have been pointed out that may not allow them to reliably capture diversity, differences in diversity, or population differentiation in general in some of the most trivial settings, given their focus on heterozygosity.50”

What the sources say

• Not checked yetcites [50] Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008). source

In the paper

“Two such techniques, ridge regression74 and the LASSO75,76 have been considered in genetic association analysis contexts, and other methods have also been proposed as well.77–80, 81”

What the sources say

• Not checked yetcites [74] Malo, N., Libiger, O. & Schork, N. J. Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am. J. Hum. Genet. 82, 375–385 (2008). source
• Not checked yetcites [75] Hoggart, C. J., Whittaker, J. C., De Iorio, M. & Balding, D. J. Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet. 4, e1000130 (2008). Refs 74 and 75 introduced regularized regression techniques for accommodating a large number of predictors in a genetic association study and to separate causally associated from non-causally associated variants. source
• Not checked yetcites [76] Zhou, H., Sehl, M. E., Sinsheimer, J. S. & Lange, K. Association screening of common and rare genetic variants by penalized regression. Bioinformatics 6 Aug 2010 (doi:10.1093/bioinformatics/btq448). source
• Not checked yetcites [77] Clark, T. G., De Iorio, M., Griffiths, R. C. & Farrall, M. Finding associations in dense genetic maps: a genetic algorithm approach. Hum. Hered. 60, 97–108 (2005). source
• Not checked yetcites [78] Guo, W. & Lin, S. Generalized linear modeling with regularization for detecting common disease rare haplotype association. Genet. Epidemiol. 33, 308–316 (2009). source
• Not checked yetcites [79] Luan, Y. H. & Li, H. Z. Group additive regression models for genomic data analysis. Biostatistics 9, 100–113 (2008). source
• Not checked yetcites [80] Kwee, L. C., Liu, D. W., Lin, X. H., Ghosh, D. & Epstein, M. P. A powerful and flexible multilocus association test for quantitative traits. Am. J. Hum. Genet. 82, 386–397 (2008). source
• Not checked yetcites [81] Capanu, M. & Begg, C. B. Hierarchical modeling for estimating relative risks of rare genetic variants: properties of the pseudo-likelihood method. Biometrics 5 Aug 2010 (doi:10.1111/j.1541-0420.2010.01469.x). source

In the paper

“In addition, linkage analysis approaches not only come with the often difficult and expensive need to sample family members, but many phenotypes may not exhibit familial aggregation, undermining the motivation to consider family-based studies10.”

What the sources say

• Not checked yetcites [10] Bodmer, W. & Bonilla, C. Common and rare variants in multifactorial susceptibility to common diseases. Nature Genet. 40, 695–701 (2008). source

In the paper

“although transmission/disequilibrium tests may have merit in the analysis of rare variants.71”

What the sources say

• Not checked yetcites [71] Oexle, K. A remark on rare variants. J. Hum. Genet. 55, 219–226 (2010). source

In the paper

“However, they did not consider conditioning effects (Figure 1C) or problems associated with analyses involving many correlated predictor variables.33”

What the sources say

• Not checked yetcites [33] Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010). source

In the paper

“For example, collapsed sets of variants might be correlated due to LD with an additional common variant included in the model or due to the manner in which different subsets of variants are collapsed based on functional annotations”

In the paper

“Thus, the use of q values less than 1.0 produces a measure that emphasizes rare variants and the use of λ values greater than 1.0 produces a measure that emphasizes common variants.50, 129”

What the sources say

• Not checked yetcites [50] Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008). source
• Not checked yetcites [129] Hill, M. O. Diversity and evenness — unifying notation and its consequences. Ecology 54, 427–432 (1973). source

In the paper

“The CMC statistic has desirable properties in that it appropriately controls type I error rates even when non-functional variants are included in the set of variants to be tested, and has better power than the standard CAST method.”

What the sources say

• Not checked yetcites [28] Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008). One of the first papers to comprehensively evaluate statistical methods for testing collapsed sets of rare variants to a trait. The paper discussed both distance-based and regression approaches. source

In the paper

“These methods are more or less agnostic to allele frequencies, but can provide insight into differences between groups over many rare variants. However, their utility and power have not been assessed in association analysis settings.”

What the sources say

• Not checked yetcites [47] Hartl, D. L. & Clark, A. G. Principles of Population Genetics (Sinauer Associates, Sunderland, Massachusetts, 2007).
• Not checked yetcites [48] Holsinger, K. E. & Weir, B. S. Genetics in geographically structured populations: defining, estimating and interpreting FST . Nature Rev. Genet. 10, 639–650 (2009). source
• Not checked yetcites [49] Nei, M. Molecular Evolutionary Genetics (Columbia Univ. Press, New York, 1987). source

In the paper

“The method as first proposed does not easily accommodate covariates, cannot be used with quantitative phenotypes, and does not consider weighting of the variants using, for example, variant frequency or functional annotations.”

What the sources say

• Not checked yetcites [30] Morgenthaler, S. & Thilly, W. G. A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat. Res. 615, 28–56 (2007). This paper introduced the notion of collapsing sets of variants into a single group whose collective frequency could be contrasted between groups. source

In the paper

“They applied 11 of the methods described in this review plus 9 high-dimensional regression procedures, and showed that the methods do not consistently agree on the most strongly associated regions of the genes or the most likely causal variants.”

What the sources say

• Not checked yetcites [86] Bansal, V., Libiger, O., Torkamani, A. & Schork, N. J. An application and empirical comparison of statistical analysis methods for associating rare variants to a complex phenotype. Pacific Symposium on Biocomputing Proceedings (in the press).

In the paper

“For example, almost all such studies consider comparisons between a proposed novel method and simple single locus analyses, which is an obvious comparison at some level, but does not reflect the sophistication and utility of the proposed method.”

In the paper

“Such an approach might mitigate a range of concerns, for example about accommodating confounding variables and the functional assessment of variants.”

In the paper

“In addition, almost all of the studies considered simulations under some version of the EAH model of rare variant effects and do not consider other scenarios (Figure 2) or the influence of LD structure among multiple common and rare variants (of the type that might create ‘synthetic associations’85).”

What the sources say

• Not checked yetcites [85] Dickson, S. P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D. B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010). source

In the paper

“The advantages of more sophisticated data analysis methods are therefore unclear from a practical and implementation standpoint.”

In the paper

“Thus, for example, methods which can accommodate covariates, previously identified genetic factors, allelic heterogeneity, and different sets of collapsed variants simultaneously, such as regression-based methods, are clearly advantageous.”

In the paper

“Finally, although many such published power studies simulate data assuming a population genetics model for the propagation of rare variants, the appropriateness of the assumptions of these models is unclear.”

In the paper

“In this light, Bansal et al.86 recently considered the analysis of sequencing data obtained on two genes, FAAH and MGLL, thought be associated with morbid obesity among 142 morbidly obese and 147 control subjects discussed in a previous study66.”

What the sources say

• Not checked yetcites [86] Bansal, V., Libiger, O., Torkamani, A. & Schork, N. J. An application and empirical comparison of statistical analysis methods for associating rare variants to a complex phenotype. Pacific Symposium on Biocomputing Proceedings (in the press).
• Not checked yetcites [66] Bhatia, G. et al. A covering method for detecting genetic associations between rare variants and common phenotypes. PLoS Genet. (in the press).

In the paper

“Their results emphasize the need for simulation and theoretical studies of different methodologies.”

In the paper

“Logic regression67 may be a particularly attractive regression-based approach, at least in theory, for the analysis of rare variants.”

What the sources say

• Not checked yetcites [67] Kooperberg, C., Ruczinski, I., LeBlanc, M. L. & Hsu, L. Sequence analysis using logic regression. Genet. Epidemiol. 21 (Suppl. 1), S626–S631 (2001). One of the first papers to consider statistical methods for identifying optimal sets of predictors of a phenotype from sequence data based purely on the strength of statistical association. This paper proposed a novel regression method for this task. source

In the paper

“For example, some methods do not take into account the possible direction of a rare variant effect, such as the methods described by Li and Leal28 and Madsen and Browning34 whereas other methods are designed to handle these situations40.”

What the sources say

• Not checked yetcites [28] Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008). One of the first papers to comprehensively evaluate statistical methods for testing collapsed sets of rare variants to a trait. The paper discussed both distance-based and regression approaches. source
• Not checked yetcites [34] Madsen, B. E. & Browning, S. R. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5, e1000384 (2009). source
• Not checked yetcites [40] Han, F. & Pan, W. A data-adaptive sum test for disease association with multiple common or rare variants. Hum. Hered. 70, 42–54 (2010). source

In the paper

“In addition, studies so far have not considered tests within a hierarchical collapsing framework that leverages functional annotations of genomic regions to separate truly causal variants from collections of rare variants that merely contain causal variants.”