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1.  Pilot Genome Wide Association Search Identifies Potential loci for Risk of Erectile Dysfunction in Type 1 Diabetes Using the DCCT/EDIC Study Cohort 
The Journal of urology  2012;188(2):514-520.
Purpose
To identify genetic predictors of diabetes-associated ED using genome wide and candidate gene approaches in a cohort of men with type I diabetes.
Methods
We examined 528 white men with T1D (125 with ED) from the DCCT and its observational follow up EDIC Study. ED was defined from a single item of the IIEF. An Illumina Human1M BeadChip was used for genotyping. 867,125 single nucleotide polymorphisms (SNPs) were subjected to analysis. Whole genome and candidate gene approaches tested the hypothesis that genetic polymorphisms may predispose men with T1D to ED. Univariate and multivariate models were used controlling for age, HbA1c, diabetes duration, and prior randomization to intensive or conventional insulin therapy during DCCT. A stratified false discovery rate was used to perform the candidate gene approach.
Results
Two SNPs located on chromosome 3 in one genomic loci were associated with ED with p < 1×10−6. rs9810233 had a p-value of 7 × 10−7 and rs1920201 had a p-value of 9×10−7 The nearest gene to these two SNPs is ALCAM. The genetic association results at these loci were similar in univariate and multivariate analysis. No candidate genes met criteria for statistical significance.
Conclusions
Two SNPs, rs9810233 and rs1920101, which are 25 kb apart, are both associated with ED, albeit not meeting the standard GWAS significance criteria of p < 5 × 10−8. Other studies with larger sample sizes will be required to determine whether ALCAM represents a novel gene in the pathogenesis of diabetes associated ED.
doi:10.1016/j.juro.2012.04.001
PMCID: PMC3764461  PMID: 22704111
Erectile Dysfunction; Diabetes; Genetics
2.  A Case Report and Genetic Characterization of a Massive Acinic Cell Carcinoma of the Parotid with Delayed Distant Metastases 
We describe the presentation, management, and clinical outcome of a massive acinic cell carcinoma of the parotid gland. The primary tumor and blood underwent exome sequencing which revealed deletions in CDKN2A as well as PPP1R13B, which induces p53. A damaging nonsynonymous mutation was noted in EP300, a histone acetylase which plays a role in cellular proliferation. This study provides the first insights into the genetic underpinnings of this cancer. Future large-scale efforts will be necessary to define the mutational landscape of salivary gland malignancies to identify therapeutic targets and biomarkers of treatment failure.
doi:10.1155/2013/270362
PMCID: PMC3638544  PMID: 23653877
3.  NanoStringNorm: an extensible R package for the pre-processing of NanoString mRNA and miRNA data 
Bioinformatics  2012;28(11):1546-1548.
Motivation: The NanoString nCounter Platform is a new and promising technology for measuring nucleic acid abundances. It has several advantages over PCR-based techniques, including avoidance of amplification, direct sequence interrogation and digital detection for absolute quantification. These features minimize aspects of experimental error and hold promise for dealing with challenging experimental conditions such as archival formalin-fixed paraffin-embedded samples. However, systematic inter-sample technical artifacts caused by variability in sample preservation, bio-molecular extraction and platform fluctuations must be removed to ensure robust data.
Results: To facilitate this process and to address these issues for NanoString datasets, we have written a pre-processing package called NanoStringNorm in the R statistical language. Key features include an extensible environment for method comparison and new algorithm development, integrated gene and sample diagnostics, and facilitated downstream statistical analysis. The package is open-source, is available through the CRAN package repository, includes unit-tests to ensure numerical accuracy, and provides visual and numeric diagnostics.
Availability: http://cran.r-project.org/web/packages/NanoStringNorm
Contact: paul.boutros@oicr.on.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/bts188
PMCID: PMC3356845  PMID: 22513995
4.  Genome-Wide Association Identifies the ABO Blood Group as a Major Locus Associated With Serum Levels of Soluble E-Selectin 
Background
Elevated serum soluble E-selectin levels have been associated with a number of diseases. Although E-selectin levels are heritable, little is known about the specific genetic factors involved. E-selectin levels have been associated with the ABO blood group phenotype.
Methods and Results
We performed a high-resolution genome-wide association study of serum soluble E-selectin levels in 685 white individuals with type 1 diabetes from the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Intervention and Complications (EDIC) study to identify major loci influencing levels. Highly significant evidence for association (P=10−29) was observed for rs579459 near the ABO blood group gene, accounting for 19% of the variance in E-selectin levels. Levels of E-selectin were higher in O/O than O/A heterozygotes, which were likewise higher than A/A genotypes. Analysis of subgroups of A alleles reveals heterogeneity in the association, and even after this was accounted for, an intron 1 SNP remained significantly associated. We replicate the ABO association in nondiabetic individuals.
Conclusion
ABO is a major locus for serum soluble E-selectin levels. We excluded population stratification, fine-mapped the association to sub-A alleles, and also document association with additional variation in the ABO region.
doi:10.1161/ATVBAHA.109.192971
PMCID: PMC3147250  PMID: 19729612
E-selectin; ABO blood group; genome-wide association; SNP
5.  A Genome-Wide Association Study Identifies a Novel Major Locus for Glycemic Control in Type 1 Diabetes, as Measured by Both A1C and Glucose 
Diabetes  2009;59(2):539-549.
OBJECTIVE
Glycemia is a major risk factor for the development of long-term complications in type 1 diabetes; however, no specific genetic loci have been identified for glycemic control in individuals with type 1 diabetes. To identify such loci in type 1 diabetes, we analyzed longitudinal repeated measures of A1C from the Diabetes Control and Complications Trial.
RESEARCH DESIGN AND METHODS
We performed a genome-wide association study using the mean of quarterly A1C values measured over 6.5 years, separately in the conventional (n = 667) and intensive (n = 637) treatment groups of the DCCT. At loci of interest, linear mixed models were used to take advantage of all the repeated measures. We then assessed the association of these loci with capillary glucose and repeated measures of multiple complications of diabetes.
RESULTS
We identified a major locus for A1C levels in the conventional treatment group near SORCS1 (10q25.1, P = 7 × 10−10), which was also associated with mean glucose (P = 2 × 10−5). This was confirmed using A1C in the intensive treatment group (P = 0.01). Other loci achieved evidence close to genome-wide significance: 14q32.13 (GSC) and 9p22 (BNC2) in the combined treatment groups and 15q21.3 (WDR72) in the intensive group. Further, these loci gave evidence for association with diabetic complications, specifically SORCS1 with hypoglycemia and BNC2 with renal and retinal complications. We replicated the SORCS1 association in Genetics of Diabetes in Kidneys (GoKinD) study control subjects (P = 0.01) and the BNC2 association with A1C in nondiabetic individuals.
CONCLUSIONS
A major locus for A1C and glucose in individuals with diabetes is near SORCS1. This may influence the design and analysis of genetic studies attempting to identify risk factors for long-term diabetic complications.
doi:10.2337/db09-0653
PMCID: PMC2809960  PMID: 19875614
6.  Were Genome-Wide Linkage Studies a Waste of Time? Exploiting Candidate Regions Within Genome-Wide Association Studies 
Genetic epidemiology  2010;34(2):107-118.
A central issue in genome-wide association (GWA) studies is assessing statistical significance while adjusting for multiple hypothesis testing. An equally important question is the statistical efficiency of the GWA design as compared to the traditional sequential approach in which genome-wide linkage analysis is followed by region-wise association mapping. Nevertheless, GWA is becoming more popular due in part to cost efficiency: commercially available 1M chips are nearly as inexpensive as a custom-designed 10K chip. It is becoming apparent, however, that most of the on-going GWA studies with 2,000~5,000 samples are in fact underpowered. As a means to improve power, we emphasize the importance of utilizing prior information such as results of previous linkage studies via a stratified false discovery rate (FDR) control. The essence of the stratified FDR control is to prioritize the genome and maintain power to interrogate candidate regions within the GWA study. These candidate regions can be defined as, but are by no means limited to, linkage-peak regions. Furthermore, we theoretically unify the stratified FDR approach and the weighted p-value method, and we show that stratified FDR can be formulated as a robust version of weighted FDR. Finally, we demonstrate the utility of the methods in two GWA datasets: Type 2 Diabetes (FUSION) and an on-going study of long-term diabetic complications (DCCT/EDIC). The methods are implemented as a user-friendly software package, SFDR. The same stratification framework can be readily applied to other type of studies, for example, using GWA results to improve the power of sequencing data analyses.
doi:10.1002/gepi.20438
PMCID: PMC2811772  PMID: 19626703
genome-wide association; genome-wide linkage; statistical power; prior information; false discovery rate
7.  BR-squared: a practical solution to the winner’s curse in genome-wide scans 
Human Genetics  2011;129(5):545-552.
The detrimental effects of the winner’s curse, including overestimation of the genetic effects of associated variants and underestimation of sufficient sample sizes for replication studies are well-recognized in genome-wide association studies (GWAS). These effects can be expected to worsen as the field moves from GWAS into whole genome sequencing. To date, few studies have reported statistical adjustments to the naive estimates, due to the lack of suitable statistical methods and computational tools. We have developed an efficient genome-wide non-parametric method that explicitly accounts for the threshold, ranking, and allele frequency effects in whole genome scans. Here, we implement the method to provide bias-reduced estimates via bootstrap re-sampling (BR-squared) for association studies of both disease status and quantitative traits, and we report the results of applying BR-squared to GWAS of psoriasis and HbA1c. We observed over 50% reduction in the genetic effect size estimation for many associated SNPs. This translates into a greater than fourfold increase in sample size requirements for successful replication studies, which in part explains some of the apparent failures in replicating the original signals. Our analysis suggests that adjusting for the winner’s curse is critical for interpreting findings from whole genome scans and planning replication and meta-GWAS studies, as well as in attempts to translate findings into the clinical setting.
doi:10.1007/s00439-011-0948-2
PMCID: PMC3074069  PMID: 21246217
8.  Genome-Wide Association Scan for Diabetic Nephropathy Susceptibility Genes in Type 1 Diabetes 
Diabetes  2009;58(6):1403-1410.
OBJECTIVE
Despite extensive evidence for genetic susceptibility to diabetic nephropathy, the identification of susceptibility genes and their variants has had limited success. To search for genes that contribute to diabetic nephropathy, a genome-wide association scan was implemented on the Genetics of Kidneys in Diabetes collection.
RESEARCH DESIGN AND METHODS
We genotyped ∼360,000 single nucleotide polymorphisms (SNPs) in 820 case subjects (284 with proteinuria and 536 with end-stage renal disease) and 885 control subjects with type 1 diabetes. Confirmation of implicated SNPs was sought in 1,304 participants of the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, a long-term, prospective investigation of the development of diabetes-associated complications.
RESULTS
A total of 13 SNPs located in four genomic loci were associated with diabetic nephropathy with P < 1 × 10−5. The strongest association was at the FRMD3 (4.1 protein ezrin, radixin, moesin [FERM] domain containing 3) locus (odds ratio [OR] = 1.45, P = 5.0 × 10−7). A strong association was also identified at the CARS (cysteinyl-tRNA synthetase) locus (OR = 1.36, P = 3.1 × 10−6). Associations between both loci and time to onset of diabetic nephropathy were supported in the DCCT/EDIC study (hazard ratio [HR] = 1.33, P = 0.02, and HR = 1.32, P = 0.01, respectively). We demonstratedexpression of both FRMD3 and CARS in human kidney.
CONCLUSIONS
We identified genetic associations for susceptibility to diabetic nephropathy at two novel candidate loci near the FRMD3 and CARS genes. Their identification implicates previously unsuspected pathways in the pathogenesis of this important late complication of type 1 diabetes.
doi:10.2337/db08-1514
PMCID: PMC2682673  PMID: 19252134
9.  Follow-Up Analysis of Genome-Wide Association Data Identifies Novel Loci for Type 1 Diabetes 
Diabetes  2009;58(1):290-295.
OBJECTIVE—Two recent genome-wide association (GWA) studies have revealed novel loci for type 1 diabetes, a common multifactorial disease with a strong genetic component. To fully utilize the GWA data that we had obtained by genotyping 563 type 1 diabetes probands and 1,146 control subjects, as well as 483 case subject–parent trios, using the Illumina HumanHap550 BeadChip, we designed a full stage 2 study to capture other possible association signals.
RESEARCH DESIGN AND METHODS—From our existing datasets, we selected 982 markers with P < 0.05 in both GWA cohorts. Genotyping these in an independent set of 636 nuclear families with 974 affected offspring revealed 75 markers that also had P < 0.05 in this third cohort. Among these, six single nucleotide polymorphisms in five novel loci also had P < 0.05 in the Wellcome Trust Case-Control Consortium dataset and were further tested in 1,303 type 1 diabetes probands from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) plus 1,673 control subjects.
RESULTS—Two markers (rs9976767 and rs3757247) remained significant after adjusting for the number of tests in this last cohort; they reside in UBASH3A (OR 1.16; combined P = 2.33 × 10−8) and BACH2 (1.13; combined P = 1.25 × 10−6).
CONCLUSIONS—Evaluation of a large number of statistical GWA candidates in several independent cohorts has revealed additional loci that are associated with type 1 diabetes. The two genes at these respective loci, UBASH3A and BACH2, are both biologically relevant to autoimmunity.
doi:10.2337/db08-1022
PMCID: PMC2606889  PMID: 18840781
10.  Transmission-ratio distortion in the Framingham Heart Study 
BMC Proceedings  2009;3(Suppl 7):S51.
Transmission-ratio distortion (TRD) is a phenomenon in which the segregation of alleles does not obey Mendel's laws. As a simple example, a recessive locus that results in fetal lethality will result in live-born individuals sharing more alleles at this locus than expected under Mendel's laws. This could result in apparent linkage of the phenotype of 'being alive' to such a chromosomal regions. Further, this could result in false-positive linkage when 'affected-only' parametric or non-parametric linkage analysis is performed. Similarly, loci demonstrating TRD may be detectable in family-based association tests as deviant transmission of alleles. Therefore, TRD could result in confounding of family-based association studies of diseases. The Framingham Heart Study data available for Genetic Analysis Workshop 16 is a suitable dataset to determine whether there are loci in the genome that reveal TRD because of the large number of individuals from families, the high-resolution genotyping, and the population-based nature of the study. We have used both genome-wide linkage and family-based association methods to determine whether there are loci that demonstrate TRD in the Framingham Heart Study. Family-based association analysis identified thousands of loci with apparent TRD. However, the vast majority of these are likely the result of genotyping errors with application of strict quality control criteria to the genotype data, and automated inspection of the intensity plots, we identify a small number of loci that may show true TRD, including rs1000548 in intron 6 of S-antigen (arrestin, SAG) on chromosome 2 (p = 7 × 10-10).
PMCID: PMC2795951  PMID: 20018044
11.  Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes 
BMC Proceedings  2009;3(Suppl 7):S127.
Due to the high-dimensionality of single-nucleotide polymorphism (SNP) data, region-based methods are an attractive approach to the identification of genetic variation associated with a certain phenotype. A common approach to defining regions is to identify the most significant SNPs from a single-SNP association analysis, and then use a gene database to obtain a list of genes proximal to the identified SNPs. Alternatively, regions may be defined statistically, via a scan statistic. After categorizing SNPs as significant or not (based on the single-SNP association p-values), a scan statistic is useful to identify regions that contain more significant SNPs than expected by chance. Important features of this method are that regions are defined statistically, so that there is no dependence on a gene database, and both gene and inter-gene regions can be detected. In the analysis of blood-lipid phenotypes from the Framingham Heart Study (FHS), we compared statistically defined regions with those formed from the top single SNP tests. Although we missed a number of single SNPs, we also identified many additional regions not found as SNP-database regions and avoided issues related to region definition. In addition, analyses of candidate genes for high-density lipoprotein, low-density lipoprotein, and triglyceride levels suggested that associations detected with region-based statistics are also found using the scan statistic approach.
PMCID: PMC2795900  PMID: 20017993
12.  Genome-wide association analyses of North American Rheumatoid Arthritis Consortium and Framingham Heart Study data utilizing genome-wide linkage results 
BMC Proceedings  2009;3(Suppl 7):S103.
The power of genome-wide association studies can be improved by incorporating information from previous study findings, for example, results of genome-wide linkage analyses. Weighted false-discovery rate (FDR) control can incorporate genome-wide linkage scan results into the analysis of genome-wide association data by assigning single-nucleotide polymorphism (SNP) specific weights. Stratified FDR control can also be applied by stratifying the SNPs into high and low linkage strata. We applied these two FDR control methods to the data of North American Rheumatoid Arthritis Consortium (NARAC) study and the Framingham Heart Study (FHS), combining both association and linkage analysis results. For the NARAC study, we used linkage results from a previous genome scan of rheumatoid arthritis (RA) phenotype. For the FHS study, we obtained genome-wide linkage scores from the same 550 k SNP data used for the association analyses of three lipids phenotypes (HDL, LDL, TG). We confirmed some genes previously reported for association with RA and lipid phenotypes. Stratified and weighted FDR methods appear to give improved ranks to some of the replicated SNPs for the RA data, suggesting linkage scan results could provide useful information to improve genome-wide association studies.
PMCID: PMC2795874  PMID: 20017967
13.  Common Genetic Variation Near the Phospholamban Gene Is Associated with Cardiac Repolarisation: Meta-Analysis of Three Genome-Wide Association Studies 
PLoS ONE  2009;4(7):e6138.
To identify loci affecting the electrocardiographic QT interval, a measure of cardiac repolarisation associated with risk of ventricular arrhythmias and sudden cardiac death, we conducted a meta-analysis of three genome-wide association studies (GWAS) including 3,558 subjects from the TwinsUK and BRIGHT cohorts in the UK and the DCCT/EDIC cohort from North America. Five loci were significantly associated with QT interval at P<1×10−6. To validate these findings we performed an in silico comparison with data from two QT consortia: QTSCD (n = 15,842) and QTGEN (n = 13,685). Analysis confirmed the association between common variants near NOS1AP (P = 1.4×10−83) and the phospholamban (PLN) gene (P = 1.9×10−29). The most associated SNP near NOS1AP (rs12143842) explains 0.82% variance; the SNP near PLN (rs11153730) explains 0.74% variance of QT interval duration. We found no evidence for interaction between these two SNPs (P = 0.99). PLN is a key regulator of cardiac diastolic function and is involved in regulating intracellular calcium cycling, it has only recently been identified as a susceptibility locus for QT interval. These data offer further mechanistic insights into genetic influence on the QT interval which may predispose to life threatening arrhythmias and sudden cardiac death.
doi:10.1371/journal.pone.0006138
PMCID: PMC2704957  PMID: 19587794

Results 1-13 (13)