A LASSO-based approach to analyzing rare variants in genetic association studies

Brennan, Jennifer S.; He, Yunxiao; Calixte, Rose; Nyirabahizi, Epiphanie; Jiang, Yuan; Zhang, Heping

doi:10.1186/1753-6561-5-s9-s100

Cited by 6 publications

(7 citation statements)

References 14 publications

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“…Each of the 200 simulated data sets includes the following information for each individual: case-control status, three continuous quantitative traits (Q1, Q2, Q4), and three phenotypic features (Age, Smoking status, and Sex). We use a multidimensional scaling analysis based on genome-wide pairwise identity-by-state distances computed in PLINK [ 14 ] to determine three main continental population strata: African (Luhya, Luhya-additional, Yoruba-1, Yoruba-2, Yoruba-additional), Asian (Denver Chinese, Denver Chinese-additional, Han Chinese-1, Han Chinese-2, Han Chinese-additional, Japanese-1, Japanese-2, Japanese-additional), and European (CEPH-1, CEPH-2, Tuscan, and Tuscan-additional) [ 15 , 16 ]. We then generate three binary features to include in our model, assigning patients to their corresponding Asian, European, and African populations.…”

Section: Methodsmentioning

confidence: 99%

Using LASSO regression to detect predictive aggregate effects in genetic studies

Fontanarosa

Dai

2011

BMC Proc

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We use least absolute shrinkage and selection operator (LASSO) regression to select genetic markers and phenotypic features that are most informative with respect to a trait of interest. We compare several strategies for applying LASSO methods in risk prediction models, using the Genetic Analysis Workshop 17 exome simulation data consisting of 697 individuals with information on genotypic and phenotypic features (smoking, age, sex) in 5-fold cross-validated fashion. The cross-validated averages of the area under the receiver operating curve range from 0.45 to 0.63 for different strategies using only genotypic markers. The same values are improved to 0.69–0.87 when both genotypic and phenotypic information are used. The ability of the LASSO method to find true causal markers is limited, but the method was able to discover several common variants (e.g., FLT1) under certain conditions.

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Section: Methodsmentioning

confidence: 99%

Using LASSO regression to detect predictive aggregate effects in genetic studies

Fontanarosa

Dai

2011

BMC Proc

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“…Three work groups [Brennan et al, 2011; Niu et al, 2011; Wang et al, 2011] aimed to generate a linear combination of genotypes within a gene and relied on various regression techniques to test for association of the trait with individual genes. Jung et al [2011] used the count of rare variants within a gene as the surrogate for the gene, but in their association test this count became the response variable and the phenotype became the independent variable in a zero-inflated Poisson regression model.…”

Section: Methods For Collapsing Rare Variantsmentioning

confidence: 99%

“…Brennan et al [2011] considered a two-step approach of analyzing rare variant data by incorporating the least absolute shrinkage and selection operator (LASSO) technique (reviewed by Dasgupta et al [2011]). In the first step, Brennan and colleagues c nducted a gene-level screening on SNPs within each gene using the LASSO method.…”

Section: Methods For Collapsing Rare Variantsmentioning

confidence: 99%

Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini‐exome data

Bailey-Wilson¹,

Brennan²,

Bull³

et al. 2011

Genetic Epidemiology

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Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors.

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“…Commonly used methods include the collapsing, simple-sum, and weighted-sum methods [2–5]. They first collapse rare variants and then implement a LASSO (least absolute shrinkage and selection operator) [6–8], partial least squares regression (PLS) model [9], or other supporting statistical methods using the common variants and the collapsed rare variants [10]. …”

Section: Introductionmentioning

confidence: 99%

Identifying rare and common variants with Bayesian variable selection

2016

BMC Proc

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BackgroundRecent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Yet, the statistical aspect of testing disease association of rare variants is quite challenging as the typical assumptions fail to hold owing to low minor allele frequency (<0.5 or 1 %).MethodsI present a Bayesian variable selection approach to detect associations with both rare and common genetic variants for quantitative traits simultaneously. In my model, I frame the problem of identifying disease-associated variants as a problem of variable selection in a sparse space, that is, how best to model the relationship between phenotypes and a set of genetic variants. By constructing a risk index score for a group of rare variants, my method can effectively consider all variants in a multivariate model. I also use a within-chain permutation to generate the empirical thresholds to detect true-positive variants.ResultsI apply our method to study the association between increases in baseline systolic and diastolic blood pressure (SBP and DBP, respectively) and genetic variants in the data from Genetic Analysis Workshop 19 unrelated samples. I identify several rare and common variants in the gene MAP4 that are potentially associated with SBP and DBP.ConclusionsThe application shows that my method is powerful in identifying disease-associated variants even with the extreme rarity.

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A LASSO-based approach to analyzing rare variants in genetic association studies

Cited by 6 publications

References 14 publications

Using LASSO regression to detect predictive aggregate effects in genetic studies

Using LASSO regression to detect predictive aggregate effects in genetic studies

Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini‐exome data

Identifying rare and common variants with Bayesian variable selection

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