Gene-level association analysis of bivariate ordinal traits with functional regressions.

In genetic studies, many phenotypes have multiple naturally ordered discrete values. The phenotypes can be correlated with each other. If multiple correlated ordinal traits are analyzed simultaneously, the power of analysis may increase significantly while the false positives can be controlled well. In this study, we propose bivariate functional ordinal linear regression (BFOLR) models using latent regressions with cumulative logit link or probit link to perform a gene-based analysis for bivariate ordinal traits and sequencing data. In the proposed BFOLR models, genetic variant data are viewed as stochastic functions of physical positions, and the genetic effects are treated as a function of physical positions. The BFOLR models take the correlation of the two ordinal traits into account via latent variables. The BFOLR models are built upon functional data analysis which can be revised to analyze the bivariate ordinal traits and high-dimension genetic data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Extensive simulation studies show that the likelihood ratio tests of the BFOLR models control type I errors well and have good power performance. The BFOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes, CFH and ARMS2, are found to strongly associate with eye drusen size, drusen area, age-related macular degeneration (AMD) categories, and AMD severity scale.

Deconvolution analysis of cell-type expression from bulk tissues by integrating with single-cell expression reference.

To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell-type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell-type-specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk-profiled samples by using reference single-cell (sc) RNA-sequencing (RNA-seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed-effect models provide similar results as an elegant machine learning algorithm named cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well-known and reliable procedure to reconstruct cell-type abundances and cell-type-specific gene expression profiles. In real data analysis, the mixed-effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA-seq data, it is better to use mixed models with heterogeneous mean and variance-covariance. As a byproduct, the mixed models provide fractions of covariance between subject-specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA-seq data.

Gene-level association analysis of ordinal traits with functional ordinal logistic regressions.

In this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene-based analysis of ordinal traits. In the proposed FOLR models, genetic variant data are viewed as stochastic functions of physical positions and the genetic effects are treated as a function of physical positions. The FOLR models are built upon functional data analysis which can be revised to analyze the ordinal traits and high dimension genetic data. The proposed methods are capable of dealing with dense genotype data which is usually encountered in analyzing the next-generation sequencing data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Simulation studies show that the likelihood ratio test statistics of the FOLR models control type I errors well and have good power performance. The proposed methods achieve the goals of analyzing ordinal traits directly, reducing high dimensionality of dense genetic variants, being computationally manageable, facilitating model convergence, properly controlling type I errors, and maintaining high power levels. The FOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes are found to strongly associate with four ordinal traits.

Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases.

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.

Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

Genome-wide analysis of disease progression in age-related macular degeneration.

Family- and population-based genetic studies have successfully identified multiple disease-susceptibility loci for Age-related macular degeneration (AMD), one of the first batch and most successful examples of genome-wide association study. However, most genetic studies to date have focused on case-control studies of late AMD (choroidal neovascularization or geographic atrophy). The genetic influences on disease progression are largely unexplored. We assembled unique resources to perform a genome-wide bivariate time-to-event analysis to test for association of time-to-late-AMD with ∼9 million variants on 2721 Caucasians from a large multi-center randomized clinical trial, the Age-Related Eye Disease Study. To our knowledge, this is the first genome-wide association study of disease progression (bivariate survival outcome) in AMD genetic studies, thus providing novel insights to AMD genetics. We used a robust Cox proportional hazards model to appropriately account for between-eye correlation when analyzing the progression time in the two eyes of each participant. We identified four previously reported susceptibility loci showing genome-wide significant association with AMD progression: ARMS2-HTRA1 (P = 8.1 × 10-43), CFH (P = 3.5 × 10-37), C2-CFB-SKIV2L (P = 8.1 × 10-10) and C3 (P = 1.2 × 10-9). Furthermore, we detected association of rs58978565 near TNR (P = 2.3 × 10-8), rs28368872 near ATF7IP2 (P = 2.9 × 10-8) and rs142450006 near MMP9 (P = 0.0006) with progression to choroidal neovascularization but not geographic atrophy. Secondary analysis limited to 34 reported risk variants revealed that LIPC and CTRB2-CTRB1 were also associated with AMD progression (P < 0.0015). Our genome-wide analysis thus expands the genetics in both development and progression of AMD and should assist in early identification of high risk individuals.

No CFH or ARMS2 Interaction with Omega-3 Fatty Acids, Low versus High Zinc, or ß-Carotene versus Lutein and Zeaxanthin on Progression of Age-Related Macular Degeneration in the Age-Related Eye Disease Study 2: Age-Related Eye Disease Study 2 Report No. 18.

PURPOSE: To assess whether genotypes at 2 major loci associated with age-related macular degeneration (AMD), complement factor H (CFH), or age-related maculopathy susceptibility 2 (ARMS2), modify the response to oral nutrients for the treatment of AMD in the Age-Related Eye Disease Study 2 (AREDS2). DESIGN: Post hoc analysis of a randomized trial. PARTICIPANTS: White AREDS2 participants. METHODS: AREDS2 participants (n = 4203) with bilateral large drusen or late AMD in 1 eye were assigned randomly to lutein and zeaxanthin, omega-3 fatty acids, both, or placebo, and most also received the AREDS supplements. A secondary randomization assessed modified AREDS supplements in 4 treatment arms: lower zinc dosage, omission of ß-carotene, both, or no modification. To evaluate the progression to late AMD, fundus photographs were obtained at baseline and annual study visits, and history of treatment for late AMD was obtained at study visits and 6-month interim telephone calls. Participants were genotyped for the single-nucleotide polymorphisms rs1061170 in CFH and rs10490924 in ARMS2. Bivariate frailty models using both eyes were conducted, including a gene-supplement interaction term and adjusting for age, gender, level of education, and smoking status. The main treatment effects, as well as the direct comparison between lutein plus zeaxanthin and ß-carotene, were assessed for genotype interaction. MAIN OUTCOME MEASURES: The interaction between genotype and the response to AREDS2 supplements regarding progression to late AMD, any geographic atrophy (GA), and neovascular AMD. RESULTS: Complete data were available for 2775 eyes without baseline late AMD (1684 participants). The participants (mean age ± standard deviation, 72.1±7.7 years; 58.5% female) were followed up for a median of 5 years. The ARMS2 risk allele was associated significantly with progression to late AMD and neovascular AMD (P = 2.40 × 10-5 and P = 0.002, respectively), but not any GA (P = 0.097). The CFH risk allele was not associated with AMD progression. Genotype did not modify significantly the response to any of the AREDS2 supplements. CONCLUSIONS: CFH and ARMS2 risk alleles do not modify the response to the AREDS2 nutrient supplements with respect to the progression to late AMD (GA and neovascular AMD).

A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.

A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data.

Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example.

A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits.

In association studies of complex traits, fixed-effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance-component tests based on mixed models were developed for region-based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C). Although there are some comparison studies to evaluate the performance of mixed and fixed models, there is no systematic analysis to determine when the mixed models perform better and when the fixed models perform better. Here we evaluated, based on extensive simulations, the performance of the fixed and mixed model statistics, using genetic variants located in 3, 6, 9, 12, and 15 kb simulated regions. We compared the performance of three models: (i) mixed models that lead to SKAT, SKAT-O, and SKAT-C, (ii) traditional fixed-effect additive models, and (iii) fixed-effect functional regression models. To evaluate the type I error rates of the tests of fixed models, we generated genotype data by two methods: (i) using all variants, (ii) using only rare variants. We found that the fixed-effect tests accurately control or have low false positive rates. We performed simulation analyses to compare power for two scenarios: (i) all causal variants are rare, (ii) some causal variants are rare and some are common. Either one or both of the fixed-effect models performed better than or similar to the mixed models except when (1) the region sizes are 12 and 15 kb and (2) effect sizes are small. Therefore, the assumption of mixed models could be satisfied and SKAT/SKAT-O/SKAT-C could perform better if the number of causal variants is large and each causal variant contributes a small amount to the traits (i.e., polygenes). In major gene association studies, we argue that the fixed-effect models perform better or similarly to mixed models in most cases because some variants should affect the traits relatively large. In practice, it makes sense to perform analysis by both the fixed and mixed effect models and to make a comparison, and this can be readily done using our R codes and the SKAT packages.

Copy-number variants and candidate gene mutations in isolated split hand/foot malformation.

Split hand/foot malformation (SHFM) is a congenital limb deficiency with missing or shortened central digits. Some SHFM genes have been identified but the cause of many SHFM cases is unknown. We used single-nucleotide polymorphism (SNP) microarray analysis to detect copy-number variants (CNVs) in 25 SHFM cases without other birth defects from New York State (NYS), prioritized CNVs absent from population CNV databases, and validated these CNVs using quantitative real-time polymerase chain reaction (qPCR). We tested for the validated CNVs in seven cases from Iowa using qPCR, and also sequenced 36 SHFM candidate genes in all the subjects. Seven NYS cases had a potentially deleterious variant: two had a p.R225H or p.R225L mutation in TP63, one had a 17q25 microdeletion, one had a 10q24 microduplication and three had a 17p13.3 microduplication. In addition, one Iowa case had a de novo 10q24 microduplication. The 17q25 microdeletion has not been reported previously in SHFM and included two SHFM candidate genes (SUMO2 and GRB2), while the 10q24 and 17p13.3 CNVs had breakpoints within genomic regions that contained putative regulatory elements and a limb development gene. In SHFM pathogenesis, the microdeletion may cause haploinsufficiency of SHFM genes and/or deletion of their regulatory regions, and the microduplications could disrupt regulatory elements that control transcription of limb development genes.

Copy number variants in a population-based investigation of Klippel-Trenaunay syndrome.

Klippel-Trenaunay syndrome (KTS) is a rare congenital vascular disorder that is thought to occur sporadically; however, reports of familial occurrence suggest a genetic component. We examined KTS cases to identify novel, potentially causal copy number variants (CNVs). We identified 17 KTS cases from all live-births occurring in New York (1998-2010). Extracted DNA was genotyped using Illumina microarrays and CNVs were called using PennCNV software. CNVs selected for follow-up had ≥10 single nucleotide polymorphisms (SNPs) and minimal overlap with in-house controls or controls from the Database of Genomic Variants. We identified 15 candidate CNVs in seven cases; among them a deletion in two cases within transcripts of HDAC9, a histone deacetylase essential for angiogenic sprouting of endothelial cells. One of them also had a duplication upstream of SALL3, a transcription factor essential for embryonic development that inhibits DNMT3A, a DNA methyltransferase responsible for embryonic de novo DNA methylation. Another case had a duplication spanning ING5, a histone acetylation regulator active during embryogenesis. We identified rare genetic variants related to chromatin modification which may have a key role in regulating vascular development during embryogenesis. Further investigation of their implications in the pathogenesis of KTS is warranted. © 2016 Wiley Periodicals, Inc.

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case.

Copy-number variant analysis of classic heterotaxy highlights the importance of body patterning pathways.

Classic heterotaxy consists of congenital heart defects with abnormally positioned thoracic and abdominal organs. We aimed to uncover novel, genomic copy-number variants (CNVs) in classic heterotaxy cases. A microarray containing 2.5 million single-nucleotide polymorphisms (SNPs) was used to genotype 69 infants (cases) with classic heterotaxy identified from California live births from 1998 to 2009. CNVs were identified using the PennCNV software. We identified 56 rare CNVs encompassing genes in the NODAL (NIPBL, TBX6), BMP (PPP4C), and WNT (FZD3) signaling pathways, not previously linked to classic heterotaxy. We also identified a CNV involving FGF12, a gene previously noted in a classic heterotaxy case. CNVs involving RBFOX1 and near MIR302F were detected in multiple cases. Our findings illustrate the importance of body patterning pathways for cardiac development and left/right axes determination. FGF12, RBFOX1, and MIR302F could be important in human heterotaxy, because they were noted in multiple cases. Further investigation into genes involved in the NODAL, BMP, and WNT body patterning pathways and into the dosage effects of FGF12, RBFOX1, and MIR302F is warranted.

Serum Immune System Biomarkers Neopterin and Interleukin-10 Are Strongly Related to Tryptophan Metabolism in Healthy Young Adults.

BACKGROUND: Changes in tryptophan metabolism through the vitamin B-6-dependent kynurenine pathway have been linked to activation of the immune system. OBJECTIVE: We hypothesized that blood concentrations of tryptophan and its catabolites were associated with biomarkers relevant to inflammatory processes in healthy noninflamed subjects. METHODS: Healthy young adults (n = 737) aged 18-28 y without any known diseases or clinical evidence of inflammation provided blood samples for analysis of serum tryptophan/kynurenine metabolites, neopterin, C-reactive protein (CRP), and plasma pyridoxal 5'-phosphate (PLP) with LC-tandem mass spectrometry methodologies. A panel of cytokines was measured in serum by using high-sensitivity ELISA assays. Anthropometric and lifestyle data were collected by questionnaire. Multiple linear regression analysis to determine the effect of measured serum cytokine concentrations as predictors of tryptophan metabolites was performed on inverse normal-rank transformations of the data, adjusted for sex, body mass index, smoking, alcohol intake, and contraceptive use in women. RESULTS: Median serum CRP and neopterin concentrations were well below established clinical cutoffs for inflammation. We observed significant positive associations between serum interleukin-10 (IL-10) and serum kynurenine (P = 0.0002), the kynurenine-to-tryptophan ratio (KTR) (P = 0.003), 3-hydroxykynurenine (P = 0.01), and 3-hydroxyanthranilic acid (P = 0.04). Serum neopterin was positively associated with kynurenine, the KTR (both P < 0.0001), and anthranilic acid (P = 0.004), and was negatively associated with serum tryptophan (P = 0.01) and PLP (P < 0.0001). Serum tumor necrosis factor α was also negatively associated with tryptophan (P < 0.001). CONCLUSIONS: In healthy young adults with no apparent inflammatory conditions, serum tryptophan metabolites are significantly associated with key immune system biomarkers. The observed association between IL-10 and kynurenine is unexpected and suggests that kynurenine-linked mechanisms promoting negative regulation of inflammatory responses are associated with normal immune homeostasis.

Rare copy number variants implicated in posterior urethral valves.

The cause of posterior urethral valves (PUV) is unknown, but genetic factors are suspected given their familial occurrence. We examined cases of isolated PUV to identify novel copy number variants (CNVs). We identified 56 cases of isolated PUV from all live-births in New York State (1998-2005). Samples were genotyped using Illumina HumanOmni2.5 microarrays. Autosomal and sex-linked CNVs were identified using PennCNV and cnvPartition software. CNVs were prioritized for follow-up if they were absent from in-house controls, contained ≥ 10 consecutive probes, were ≥ 20 Kb in size, had ≤ 20% overlap with variants detected in other birth defect phenotypes screened in our lab, and were rare in population reference controls. We identified 47 rare candidate PUV-associated CNVs in 32 cases; one case had a 3.9 Mb deletion encompassing BMP7. Mutations in BMP7 have been associated with severe anomalies in the mouse urethra. Other interesting CNVs, each detected in a single PUV case included: a deletion of PIK3R3 and TSPAN1, duplication/triplication in FGF12, duplication of FAT1--a gene essential for normal growth and development, a large deletion (>2 Mb) on chromosome 17q that involves TBX2 and TBX4, and large duplications (>1 Mb) on chromosomes 3q and 6q. Our finding of previously unreported novel CNVs in PUV suggests that genetic factors may play a larger role than previously understood. Our data show a potential role of CNVs in up to 57% of cases examined. Investigation of genes in these CNVs may provide further insights into genetic variants that contribute to PUV.

Generalized functional linear models for gene-based case-control association studies.

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses.

Novel copy-number variants in a population-based investigation of classic heterotaxy.

PURPOSE: Heterotaxy is a clinically and genetically heterogeneous disorder. We investigated whether screening cases restricted to a classic phenotype would result in the discovery of novel, potentially causal copy-number variants. METHODS: We identified 77 cases of classic heterotaxy from all live births in New York State during 1998-2005. DNA extracted from each infant's newborn dried blood spot was genotyped with a microarray containing 2.5 million single-nucleotide polymorphisms. Copy-number variants were identified with PennCNV and cnvPartition software. Candidates were selected for follow-up if they were absent in unaffected controls, contained 10 or more consecutive probes, and had minimal overlap with variants published in the Database of Genomic Variants. RESULTS: We identified 20 rare copy-number variants including a deletion of BMP2, which has been linked to laterality disorders in mice but not previously reported in humans. We also identified a large, terminal deletion of 10q and a microdeletion at 1q23.1 involving the MNDA gene; both are rare variants suspected to be associated with heterotaxy. CONCLUSION: Our findings implicate rare copy-number variants in classic heterotaxy and highlight several candidate gene regions for further investigation. We also demonstrate the efficacy of copy-number variant genotyping in blood spots using microarrays.