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1.  Association of RASGRP1 with type 1 diabetes is revealed by combined follow-up of two genome-wide studies 
Journal of Medical Genetics  2009;46(8):553-554.
Background
The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.
Methods
We analyzed data from two sources: 1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1,046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); 2) The RR2 project of the T1DGC, which genotyped 4,417 individuals from 1,062 non-overlapping families, including 2,059 affected individuals (mostly sibling pairs) for the 1,536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.
Results
One locus, mapping to an LD block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect linkage disequilibrium (LD) with each other (r2=1). We obtained a joint p value of 1.3 ×10−6, which exceeds by an order of magnitude the conservative threshold of 3.26×10−5 obtained by correcting for the 1,536 SNPs tested in our study. Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83×10−9.
Conclusions
A novel type 1 diabetes locus was discovered. It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and TCR signaling by activating the Ras signaling pathway.
doi:10.1136/jmg.2009.067140
PMCID: PMC3272492  PMID: 19465406
Etiology; Genetic susceptibility; Type 1 diabetes; RASGRP1
2.  Examination of All Type 2 Diabetes GWAS Loci Reveals HHEX-IDE as a Locus Influencing Pediatric BMI 
Diabetes  2009;59(3):751-755.
OBJECTIVE
A number of studies have found that BMI in early life influences the risk of developing type 2 diabetes later in life. Our goal was to investigate if any type 2 diabetes variants uncovered through genome-wide association studies (GWAS) impact BMI in childhood.
RESEARCH DESIGN AND METHODS
Using data from an ongoing GWAS of pediatric BMI in our cohort, we investigated the association of pediatric BMI with 20 single nucleotide polymorphisms at 18 type 2 diabetes loci uncovered through GWAS, consisting of ADAMTS9, CDC123-CAMK1D, CDKAL1, CDKN2A/B, EXT2, FTO, HHEX-IDE, IGF2BP2, the intragenic region on 11p12, JAZF1, KCNQ1, LOC387761, MTNR1B, NOTCH2, SLC30A8, TCF7L2, THADA, and TSPAN8-LGR5. We randomly partitioned our cohort exactly in half in order to have a discovery cohort (n = 3,592) and a replication cohort (n = 3,592).
RESULTS
Our data show that the major type 2 diabetes risk–conferring G allele of rs7923837 at the HHEX-IDE locus was associated with higher pediatric BMI in both the discovery (P = 0.0013 and survived correction for 20 tests) and replication (P = 0.023) sets (combined P = 1.01 × 10−4). Association was not detected with any other known type 2 diabetes loci uncovered to date through GWAS except for the well-established FTO.
CONCLUSIONS
Our data show that the same genetic HHEX-IDE variant, which is associated with type 2 diabetes from previous studies, also influences pediatric BMI.
doi:10.2337/db09-0972
PMCID: PMC2828649  PMID: 19933996
3.  Examination of Type 2 Diabetes Loci Implicates CDKAL1 as a Birth Weight Gene 
Diabetes  2009;58(10):2414-2418.
OBJECTIVE
A number of studies have found that reduced birth weight is associated with type 2 diabetes later in life; however, the underlying mechanism for this correlation remains unresolved. Recently, association has been demonstrated between low birth weight and single nucleotide polymorphisms (SNPs) at the CDKAL1 and HHEX-IDE loci, regions that were previously implicated in the pathogenesis of type 2 diabetes. In order to investigate whether type 2 diabetes risk–conferring alleles associate with low birth weight in our Caucasian childhood cohort, we examined the effects of 20 such loci on this trait.
RESEARCH DESIGN AND METHODS
Using data from an ongoing genome-wide association study in our cohort of 5,465 Caucasian children with recorded birth weights, we investigated the association of the previously reported type 2 diabetes–associated variation at 20 loci including TCF7L2, HHEX-IDE, PPARG, KCNJ11, SLC30A8, IGF2BP2, CDKAL1, CDKN2A/2B, and JAZF1 with birth weight.
RESULTS
Our data show that the minor allele of rs7756992 (P = 8 × 10−5) at the CDKAL1 locus is strongly associated with lower birth weight, whereas a perfect surrogate for variation previously implicated for the trait at the same locus only yielded nominally significant association (P = 0.01; r2 rs7756992 = 0.677). However, association was not detected with any of the other type 2 diabetes loci studied.
CONCLUSIONS
We observe association between lower birth weight and type 2 diabetes risk–conferring alleles at the CDKAL1 locus. Our data show that the same genetic locus that has been identified as a marker for type 2 diabetes in previous studies also influences birth weight.
doi:10.2337/db09-0506
PMCID: PMC2750235  PMID: 19592620
4.  The role of obesity-associated loci identified in genome wide association studies in the determination of pediatric BMI 
Obesity (Silver Spring, Md.)  2009;17(12):2254-2257.
The prevalence of obesity in children and adults in the United States has increased dramatically over the past decade. Besides environmental factors, genetic factors are known to play an important role in the pathogenesis of obesity. A number of genetic determinants of adult BMI have already been established through genome wide association studies. In this study, we examined 25 single nucleotide polymorphisms (SNPs) corresponding to thirteen previously reported genomic loci in 6,078 children with measures of BMI. Fifteen of these SNPs yielded at least nominally significant association to BMI, representing nine different loci including INSIG2, FTO, MC4R, TMEM18, GNPDA2, NEGR1, BDNF, KCTD15 and 1q25. Other loci revealed no evidence for association, namely at MTCH2, SH2B1, 12q13 and 3q27. For the 15 associated variants, the genotype score explained 1.12% of the total variation for BMI z-score. We conclude that among thirteen loci that have been reported to associate with adult BMI, at least nine also contribute to the determination of BMI in childhood as demonstrated by their associations in our pediatric cohort.
doi:10.1038/oby.2009.159
PMCID: PMC2860782  PMID: 19478790
5.  Investigation of the locus near MC4R with childhood obesity in Americans of European and African ancestry 
Obesity (Silver Spring, Md.)  2009;17(7):1461-1465.
Recently a modest, but consistently, replicated association was demonstrated between obesity and the single nucleotide polymorphism (SNP), rs17782313, 3’ of the MC4R locus as a consequence of a meta-analysis of genome wide association (GWA) studies of the disease in Caucasian populations. We investigated the association in the context of the childhood form of the disease utilizing data from our ongoing GWA study in a cohort of 728 European American (EA) obese children (BMI ≥ 95th percentile) and 3,960 EA controls (BMI < 95th percentile), as well as 1,008 African American (AA) obese children and 2,715 AA controls. rs571312, rs10871777 and rs476828 (perfect surrogates for rs17782313) yielded odds ratios in the EA cohort of 1.142 (P = 0.045), 1.137 (P = 0.054) and 1.145 (P = 0.042); however, there was no significant association with these SNPs in the AA cohort. When investigating all thirty SNPs present on the Illumina BeadChip at this locus, again there was no evidence for association in AA cases when correcting for the number of tests employed. As such, variants 3’ to the MC4R locus present on the genotyping platform utilized confer a similar magnitude of risk of obesity in Caucasian children as to their adult Caucasian counterparts but this observation did not extend to African Americans.
doi:10.1038/oby.2009.53
PMCID: PMC2860794  PMID: 19265794
6.  SNP array mapping of 20p deletions: Genotypes, Phenotypes and Copy Number Variation 
Human mutation  2009;30(3):371-378.
The use of array technology to define chromosome deletions and duplications is bringing us closer to establishing a genotype/phenotype map of genomic copy number alterations. We studied 21 patients and 5 relatives with deletions of the short arm of chromosome 20 using the Illumina HumanHap550 SNP array to 1) more accurately determine the deletion sizes, 2) identify and compare breakpoints, 3) establish genotype/phenotype correlations and 4) investigate the use of the HumanHap550 platform for analysis of chromosome deletions. Deletions ranged from 95kb to 14.62Mb, and all of the breakpoints were unique. Eleven patients had deletions between 95kb and 4Mb and these individuals had normal development, with no anomalies outside of those associated with Alagille syndrome. The proximal and distal boundaries of these eleven deletions constitute a 5.4MB region, and we propose that haploinsufficiency for only 1 of the 12 genes in this region causes phenotypic abnormalities. This defines the JAG1 associated critical region, in which deletions do not confer findings other than those associated with Alagille syndrome. The other 10 patients had deletions between 3.28Mb and 14.62Mb, which extended outside the critical region, and notably, all of these patients, had developmental delay. This group had other findings such as autism, scoliosis and bifid uvula. We identified 47 additional polymorphic genome-wide copy number variants (>20 SNPs), with 0–5 variants called per patient. Deletions of the short arm of chromosome 20 are associated with relatively mild and limited clinical anomalies. The use of SNP arrays provides accurate high-resolution definition of genomic abnormalities.
doi:10.1002/humu.20863
PMCID: PMC2650004  PMID: 19058200
SNP array analysis; 20p deletion; copy number variants; Alagille syndrome; haploinsufficiency; JAG1
7.  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
8.  Common variations in BARD1 influence susceptibility to high-risk neuroblastoma 
Nature genetics  2009;41(6):718-723.
We conducted a SNP-based genome-wide association study (GWAS) focused on the high-risk subset of neuroblastoma1. As our previous unbiased GWAS showed strong association of common 6p22 SNP alleles with aggressive neuroblastoma2, we now restricted our analysis to 397 high-risk cases compared to 2,043 controls. We detected new significant association of six SNPs at 2q35 within the BARD1 gene locus (Pallelic = 2.35×10−9 − 2.25×10−8). Each SNP association was confirmed in a second series of 189 high-risk cases and 1,178 controls (Pallelic = 7.90×10−7 − 2.77×10−4). The two most significant SNPs (rs6435862, rs3768716) were also tested in two additional independent high-risk neuroblastoma case series, yielding combined allelic odds-ratios of 1.68 each (P = 8.65×10−18 and 2.74×10−16, respectively). Significant association was also found with known BARD1 nsSNPs. These data show that common variation in BARD1 contributes to the etiology of the aggressive and most clinically relevant subset of human neuroblastoma.
doi:10.1038/ng.374
PMCID: PMC2753610  PMID: 19412175
9.  From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes 
PLoS Genetics  2009;5(10):e1000678.
Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.
Author Summary
An often touted utility of genome-wide association studies (GWAS) is that the resulting discoveries can facilitate implementation of personalized medicine, in which preventive and therapeutic interventions for complex diseases can be tailored to individual genetic profiles. However, recent studies using whole-genome SNP genotype data for disease risk assessment have generally failed to achieve satisfactory results, leading to a pessimistic view of the utility of genotype data for such purposes. Here we propose that sophisticated machine-learning approaches on a large ensemble of markers, which contain both confirmed and as yet unconfirmed disease susceptibility variants, may improve the performance of disease risk assessment. We tested an algorithm called Support Vector Machine (SVM) on three large-scale datasets for type 1 diabetes and demonstrated that risk assessment can be highly accurate for the disease. Our results suggest that individualized disease risk assessment using whole-genome data may be more successful for some diseases (such as T1D) than other diseases. However, the predictive accuracy will be dependent on the heritability of the disease under study, the proportion of the genetic risk that is known, and that the right set of markers and right algorithms are being used.
doi:10.1371/journal.pgen.1000678
PMCID: PMC2748686  PMID: 19816555
10.  Copy number variation at 1q21.1 associated with neuroblastoma 
Nature  2009;459(7249):987-991.
Common copy number variations (CNVs) represent a significant source of genetic diversity, yet their influence on phenotypic variability, including disease susceptibility, remains poorly understood. To address this problem in cancer, we performed a genome-wide association study (GWAS) of CNVs in the childhood cancer neuroblastoma, a disease where SNP variations are known to influence susceptibility1,2. We first genotyped 846 Caucasian neuroblastoma patients and 803 healthy Caucasian controls at 550,000 single nucleotide polymorphisms, and performed a CNV-based test for association. We then replicated significant observations in two independent sample sets comprised of a total of 595 cases and 3,357 controls. We identified a common CNV at 1q21.1 associated with neuroblastoma in the discovery set, which was confirmed in both replication sets (Pcombined = 2.97 × 10−17; OR = 2.49, 95% CI: 2.02 to 3.05). This CNV was validated by quantitative PCR, fluorescent in situ hybridization, and analysis of matched tumor specimens, and was shown to be heritable in an independent set of 713 cancer-free trios. We identified a novel transcript within the CNV which showed high sequence similarity to several “Neuroblastoma breakpoint family” (NBPF) genes3,4 and represents a new member of this gene family (NBPFX). This transcript was preferentially expressed in fetal brain and fetal sympathetic nervous tissues, and expression level was strictly correlated with CNV state in neuroblastoma cells. These data demonstrate that inherited copy number variation at 1q21.1 is associated with neuroblastoma and implicate a novel NBPF gene in early tumorigenesis of this childhood cancer.
doi:10.1038/nature08035
PMCID: PMC2755253  PMID: 19536264
11.  Genome-Wide Analyses of Exonic Copy Number Variants in a Family-Based Study Point to Novel Autism Susceptibility Genes 
PLoS Genetics  2009;5(6):e1000536.
The genetics underlying the autism spectrum disorders (ASDs) is complex and remains poorly understood. Previous work has demonstrated an important role for structural variation in a subset of cases, but has lacked the resolution necessary to move beyond detection of large regions of potential interest to identification of individual genes. To pinpoint genes likely to contribute to ASD etiology, we performed high density genotyping in 912 multiplex families from the Autism Genetics Resource Exchange (AGRE) collection and contrasted results to those obtained for 1,488 healthy controls. Through prioritization of exonic deletions (eDels), exonic duplications (eDups), and whole gene duplication events (gDups), we identified more than 150 loci harboring rare variants in multiple unrelated probands, but no controls. Importantly, 27 of these were confirmed on examination of an independent replication cohort comprised of 859 cases and an additional 1,051 controls. Rare variants at known loci, including exonic deletions at NRXN1 and whole gene duplications encompassing UBE3A and several other genes in the 15q11–q13 region, were observed in the course of these analyses. Strong support was likewise observed for previously unreported genes such as BZRAP1, an adaptor molecule known to regulate synaptic transmission, with eDels or eDups observed in twelve unrelated cases but no controls (p = 2.3×10−5). Less is known about MDGA2, likewise observed to be case-specific (p = 1.3×10−4). But, it is notable that the encoded protein shows an unexpectedly high similarity to Contactin 4 (BLAST E-value = 3×10−39), which has also been linked to disease. That hundreds of distinct rare variants were each seen only once further highlights complexity in the ASDs and points to the continued need for larger cohorts.
Author Summary
Autism spectrum disorders (ASDs) are common neurodevelopmental syndromes with a strong genetic component. ASDs are characterized by disturbances in social behavior, impaired verbal and nonverbal communication, as well as repetitive behaviors and/or a restricted range of interests. To identify genes likely to contribute to ASD etiology, we performed high density genotyping in 912 multiplex families from the Autism Genetics Resource Exchange (AGRE) collection and contrasted results to those obtained for 1,488 healthy controls. To enrich for variants most likely to interfere with gene function, we restricted our analyses to deletions and gains encompassing exons. Of the many genomic regions highlighted, 27 were seen to harbor rare variants in cases and not controls, both in the first phase of our analysis, and also in an independent replication cohort comprised of 859 cases and 1,051 controls. More work in a larger number of individuals will be required to determine which of the rare alleles highlighted here are indeed related to the ASDs and how they act to shape risk.
doi:10.1371/journal.pgen.1000536
PMCID: PMC2695001  PMID: 19557195

Results 1-11 (11)