N Engl J Med. 2012 Sep 12. [Epub ahead of print]

Phenotypic Heterogeneity of Genomic Disorders and Rare Copy-Number Variants.

Girirajan S, Rosenfeld JA, Coe BP, Parikh S, Friedman N, Goldstein A, Filipink RA, McConnell JS, Angle B, Meschino WS, Nezarati MM, Asamoah A, Jackson KE, Gowans GC, Martin JA, Carmany EP, Stockton DW, Schnur RE, Penney LS, Martin DM, Raskin S, Leppig K, Thiese H, Smith R, Aberg E, Niyazov DM,Escobar LF, El-Khechen D, Johnson KD, Lebel RR, Siefkas K, Ball S, Shur N, McGuire M, Brasington CK, Spence JE, Martin LS, Clericuzio C, Ballif BC, Shaffer LG, Eichler EE.

Source

The authors' affiliations are listed in the Appendix.

Abstract

Background Some copy-number variants are associated with genomic disorders with extreme phenotypic heterogeneity. The cause of this variation is unknown, which presents challenges in genetic diagnosis, counseling, and management. Methods We analyzed the genomes of 2312 children known to carry a copy-number variant associated with intellectual disability and congenital abnormalities, using array comparative genomic hybridization. Results Among the affected children, 10.1% carried a second large copy-number variant in addition to the primary genetic lesion. We identified seven genomic disorders, each defined by a specific copy-number variant, in which the affected children were more likely to carry multiple copy-number variants than were controls. We found that syndromic disorders could be distinguished from those with extreme phenotypic heterogeneity on the basis of the total number of copy-number variants and whether the variants are inherited or de novo. Children who carried two large copy-number variants of unknown clinical significance were eight times as likely to have developmental delay as were controls (odds ratio, 8.16; 95% confidence interval, 5.33 to 13.07; P=2.11×10(-38)). Among affected children, inherited copy-number variants tended to co-occur with a second-site large copy-number variant (Spearman correlation coefficient, 0.66; ...

PMID:

 

22970919

 

[PubMed - as supplied by publisher] 

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[pub]: Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases

http://onlinelibrary.wiley.com/doi/10.1002/gepi.21675/abstract;jsessionid=0E67E391238867DA8CC7EDD1FAABCE88.d03t01

Genet Epidemiol. 2012 Sep 5. doi: 10.1002/gepi.21675. [Epub ahead of print]

Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases.

Mägi R, Asimit JL, Day-Williams AG, Zeggini E, Morris AP.

Source

Estonian Genome Centre, University of Tartu, Tartu, Estonia.

Abstract

Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome-wide genotype data up to high-density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re-sequencing experiments. By application of this approach to genome-wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome-wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.

© 2012 Wiley Periodicals, Inc.

PMID:

 

22951892

 

[PubMed - as supplied by publisher]

PLoS ONE: Adaptive Ridge Regression for Rare Variant Detection

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0044173

Abstract Top

It is widely believed that both common and rare variants contribute to the risks of common diseases or complex traits and the cumulative effects of multiple rare variants can explain a significant proportion of trait variances. Advances in high-throughput DNA sequencing technologies allow us to genotype rare causal variants and investigate the effects of such rare variants on complex traits. We developed an adaptive ridge regression method to analyze the collective effects of multiple variants in the same gene or the same functional unit. Our model focuses on continuous trait and incorporates covariate factors to remove potential confounding effects. The proposed method estimates and tests multiple rare variants collectively but does not depend on the assumption of same direction of each rare variant effect. Compared with the Bayesian hierarchical generalized linear model approach, the state-of-the-art method of rare variant detection, the proposed new method is easy to implement, yet it has higher statistical power. Application of the new method is demonstrated using the well-known data from the Dallas Heart Study.

Citation: Zhan H, Xu S (2012) Adaptive Ridge Regression for Rare Variant Detection. PLoS ONE 7(8): e44173. doi:10.1371/journal.pone.0044173

Editor: Momiao Xiong, University of Texas School of Public Health, United States of America

Received: May 28, 2012; Accepted: July 30, 2012; Published: August 28, 2012

We believe that the high power of the BhGLM is due to (1) appropriate combination of the individual rare variants (the new score) and (2) assignment of the hierarchical priors. After a thorough evaluation of these new methods, we found that there is still some room for improvement. The new score of the BhGLM method is a first moment parameter (shared effect). An alternative score may be a second moment parameter (shared variance). The Yi and Zhi's method requires prior distributions, and thus different priors may produce different results. The hyper-parameters involved in the prior distributions may also affect the results. In this study, we proposed to use a shared variance among rare variants as the new score. The method is originally called ridge regression [27]. It is further modified to discriminate against rare variants with small effects. This modified ridge regression is called the adaptive ridge regression [28]. The adaptive ridge regression (ARR) is performed under the maximum likelihood framework, and thus prior distributions of parameters are not required, equivalent to independent uniform priors for all parameters.

....

The method is called sequence kernel association test (SKAT). After reading this paper, we agreed that our approach is similar to SKAT. However, SKAT only gives the score test and no parameter estimation is provided. This explains why SKAT is fast computationally. There are three major advantages of the adaptive ridge regression. First, a high score test does not mean the effects are large. It may be caused by small effects but large sample size. The score test cannot tell the difference. Our method not only provides a test but also an estimate of the group variance. We can provide a total proportion of the phenotypic variance contributed by the rare variants. Secondly, we introduced an adaptive step to the original ridge regression. This step plays the role of "weighting" of the SKAT method but it can "homogenize" the effect of each rare variant within a group. The ridge regression performs better under the "homogenized" rare variant effect assumption. Thirdly, our method works for both rare and common variants. However, the SKAT method was particularly designed for rare variants because the "weights" for the common variants will be almost zero (excluded from the model), according to the authors of that paper. There is a possibility to use the score test under our adaptive ridge regression framework. The estimation procedure will remain the same, but we may simply replace the likelihood ratio test by the score test. The "weights" obtained from the adaptive ridge regression will be used in the score test. This needs to be further investigated.

The new method is developed for continuous traits under the linear mixed model framework [30]. In many situations, the trait of interest may be a binary trait. The generalized linear mixed model (GLMM), which is an extension of the linear mixed model, can be used to analyze the association of multiple rare variants and a binary trait. This extension is very straight forward because the methodology of GLMM has been well established. The simple extension includes the adaptive steps.
Finally, we performed all the analyses using an R program. The R package is called Adaptive Ridge Regression (ARR) which can be downloaded from the authors' personal website: www.statgen.ucr.edu.

PLoS One. 2012;7(8):e41694. Epub 2012 Aug 8.

Multiple regression methods show great potential for rare variant association tests.

Xu C, Ladouceur M, Dastani Z, Richards JB, Ciampi A, Greenwood CM.

Source

Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.

Abstract

The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few years, many new methods have been developed which associate genomic regions with phenotypes. However, classical methods for high-dimensional data have received little attention. Here we investigate whether several classical statistical methods for high-dimensional data: ridge regression (RR), principal components regression (PCR), partial least squares regression (PLS), a sparse version of PLS (SPLS), and the LASSO are able to detect associations with rare genetic variants. These approaches have been extensively used in statistics to identify the true associations in data sets containing many predictor variables. Using genetic variants identified in three genes that were Sanger sequenced in 1998 individuals, we simulated continuous phenotypes under several different models, and we show that these feature selection and feature extraction methods can substantially outperform several popular methods for rare variant analysis. Furthermore, these approaches can identify which variants are contributing most to the model fit, and therefore both goals of rare variant analysis can be achieved simultaneously with the use of regression regularization methods. These methods are briefly illustrated with an analysis of adiponectin levels and variants in the ADIPOQ gene.

PMID:

 

22916111

 

[PubMed - in process] 

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Bioinformatics. 2012 May 3. [Epub ahead of print]

SEQCHIP: A Powerful Method to Integrate Sequence and Genotype Data for the Detection of Rare Variant Associations.

Liu DJ, Leal SM.

Source

1. Department of Biostatistics, Center of Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109.

Abstract

MOTIVATION:

Next-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.

RESULTS:

It is demonstrated using both simulated and real datasets that the SEQCHIP method has controlled type-I errors, and is substantially more powerful than all other currently available methods.

AVAILABILITY:

SEQCHIP is implemented in an R-Package and is available at http://linkage.rockefeller.edu/suzanne/seqchip/Seqchip.htmContacts: dajiang@umich.edu (D.J.L.), sleal@bcm.edu (S.M.L.)

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:

 

22556370

 

[PubMed - as supplied by publisher]