Explore the vast range of titles available.

Five genes have been identified that contribute to Mendelian forms of Parkinson disease (PD); however, mutations have been found in fewer than 5% of patients, suggesting that additional genes contribute to disease risk. Unlike previous studies that focused primarily on sporadic PD, we have performed the first genomewide association study (GWAS) in familial PD. Genotyping was performed with the Illumina HumanCNV370Duo array in 857 familial PD cases and 867 controls. A logistic model was employed to test for association under additive and recessive modes of inheritance after adjusting for gender and age. No result met genomewide significance based on a conservative Bonferroni correction. The strongest association result was with SNPs in the GAK/DGKQ region on chromosome 4 (additive model: p = 3.4 x 10⁻⁶; OR = 1.69). Consistent evidence of association was also observed to the chromosomal regions containing SNCA (additive model: p = 5.5 x 10⁻⁵; OR = 1.35) and MAPT (recessive model: p = 2.0 x 10⁻⁵; OR = 0.56). Both of these genes have been implicated previously in PD susceptibility; however, neither was identified in previous GWAS studies of PD. Meta-analysis was performed using data from a previous case-control GWAS, and yielded improved p values for several regions, including GAK/DGKQ (additive model: p = 2.5 x 10⁻⁷) and the MAPT region (recessive model: p = 9.8 x 10⁻⁶; additive model: p = 4.8 x 10⁻⁵). These data suggest the identification of new susceptibility alleles for PD in the GAK/DGKQ region, and also provide further support for the role of SNCA and MAPT in PD susceptibility.

Comprehensive Association Study of Type 2 Diabetes and Related Quantitative Traits With 222 Candidate Genes Kyle J. Gaulton 1 , Cristen J. Willer 2 , Yun Li 2 , Laura J. Scott 2 , Karen N. Conneely 2 , Anne U. Jackson 2 , William L. Duren 2 , Peter S. Chines 3 , Narisu Narisu 3 , Lori L. Bonnycastle 3 , Jingchun Luo 4 , Maurine Tong 3 , Andrew G. Sprau 3 , Elizabeth W. Pugh 5 , Kimberly F. Doheny 5 , Timo T. Valle 6 , Gonçalo R. Abecasis 2 , Jaakko Tuomilehto 6 7 8 , Richard N. Bergman 9 , Francis S. Collins 3 , Michael Boehnke 2 and Karen L. Mohlke 1 1 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 2 Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 3 Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 4 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 5 Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 6 Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, Helsinki, Finland 7 Department of Public Health, University of Helsinki, Helsinki, Finland 8 South Ostrobothnia Central Hospital, Seinäjoki, Finland 9 Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California Corresponding author: Karen Mohlke, mohlke{at}med.unc.edu Abstract OBJECTIVE— Type 2 diabetes is a common complex disorder with environmental and genetic components. We used a candidate gene–based approach to identify single nucleotide polymorphism (SNP) variants in 222 candidate genes that influence susceptibility to type 2 diabetes. RESEARCH DESIGN AND METHODS— In a case-control study of 1,161 type 2 diabetic subjects and 1,174 control Finns who are normal glucose tolerant, we genotyped 3,531 tagSNPs and annotation-based SNPs and imputed an additional 7,498 SNPs, providing 99.9% coverage of common HapMap variants in the 222 candidate genes. Selected SNPs were genotyped in an additional 1,211 type 2 diabetic case subjects and 1,259 control subjects who are normal glucose tolerant, also from Finland. RESULTS— Using SNP- and gene-based analysis methods, we replicated previously reported SNP-type 2 diabetes associations in PPARG , KCNJ11 , and SLC2A2 ; identified significant SNPs in genes with previously reported associations ( ENPP1 [rs2021966, P = 0.00026] and NRF1 [rs1882095, P = 0.00096]); and implicated novel genes, including RAPGEF1 (rs4740283, P = 0.00013) and TP53 (rs1042522, Arg72Pro, P = 0.00086), in type 2 diabetes susceptibility. CONCLUSIONS— Our study provides an effective gene-based approach to association study design and analysis. One or more of the newly implicated genes may contribute to type 2 diabetes pathogenesis. Analysis of additional samples will be necessary to determine their effect on susceptibility. Footnotes Published ahead of print at http://diabetes.diabetesjournals.org on 4 August 2008. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Accepted July 21, 2008. Received December 11, 2007. DIABETES

Background Over 90% of adults aged 20 years or older with permanent teeth have suffered from dental caries leading to pain, infection, or even tooth loss. Although caries prevalence has decreased over the past decade, there are still about 23% of dentate adults who have untreated carious lesions in the US. Dental caries is a complex disorder affected by both individual susceptibility and environmental factors. Approximately 35-55% of caries phenotypic variation in the permanent dentition is attributable to genes, though few specific caries genes have been identified. Therefore, we conducted the first genome-wide association study (GWAS) to identify genes affecting susceptibility to caries in adults. Methods Five independent cohorts were included in this study, totaling more than 7000 participants. For each participant, dental caries was assessed and genetic markers (single nucleotide polymorphisms, SNPs) were genotyped or imputed across the entire genome. Due to the heterogeneity among the five cohorts regarding age, genotyping platform, quality of dental caries assessment, and study design, we first conducted genome-wide association (GWA) analyses on each of the five independent cohorts separately. We then performed three meta-analyses to combine results for: (i) the comparatively younger, Appalachian cohorts (N = 1483) with well-assessed caries phenotype, (ii) the comparatively older, non-Appalachian cohorts (N = 5960) with inferior caries phenotypes, and (iii) all five cohorts (N = 7443). Top ranking genetic loci within and across meta-analyses were scrutinized for biologically plausible roles on caries. Results Different sets of genes were nominated across the three meta-analyses, especially between the younger and older age cohorts. In general, we identified several suggestive loci (P-value ≤ 10E-05) within or near genes with plausible biological roles for dental caries, including RPS6KA2 and PTK2B, involved in p38-depenedent MAPK signaling, and RHOU and FZD1, involved in the Wnt signaling cascade. Both of these pathways have been implicated in dental caries. ADMTS3 and ISL1 are involved in tooth development, and TLR2 is involved in immune response to oral pathogens. Conclusions As the first GWAS for dental caries in adults, this study nominated several novel caries genes for future study, which may lead to better understanding of cariogenesis, and ultimately, to improved disease predictions, prevention, and/or treatment.

Microarray single-nucleotide polymorphism genotyping, combined with imputation of untyped variants, has been widely adopted as an efficient means to interrogate variation across the human genome. “Genomic coverage” is the total proportion of genomic variation captured by an array, either by direct observation or through an indirect means such as linkage disequilibrium or imputation. We have performed imputation-based genomic coverage assessments of eight current genotyping arrays that assay from ~0.3 to ~5 million variants. Coverage was determined separately in each of the four continental ancestry groups in the 1000 Genomes Project phase 1 release. We used the subset of 1000 Genomes variants present on each array to impute the remaining variants and assessed coverage based on correlation between imputed and observed allelic dosages. More than 75% of common variants (minor allele frequency > 0.05) are covered by all arrays in all groups except for African ancestry, and up to ~90% in all ancestries for the highest density arrays. In contrast, less than 40% of less common variants (0.01 < minor allele frequency < 0.05) are covered by low density arrays in all ancestries and 50–80% in high density arrays, depending on ancestry. We also calculated genome-wide power to detect variant-trait association in a case-control design, across varying sample sizes, effect sizes, and minor allele frequency ranges, and compare these array-based power estimates with a hypothetical array that would type all variants in 1000 Genomes. These imputation-based genomic coverage and power analyses are intended as a practical guide to researchers planning genetic studies.

Karen Mohlke, Markku Laakso, Michael Boehnke and colleagues report the first application of the Illumina HumanExome Beadchip array, examining association with insulin and glycemic traits in 8,229 nondiabetic Finnish males from the population-based Metabolic Syndrome in Men (METSIM) study. They identify low-frequency coding variants at both known and newly associated loci with insulin processing and secretion. Insulin secretion has a crucial role in glucose homeostasis, and failure to secrete sufficient insulin is a hallmark of type 2 diabetes. Genome-wide association studies (GWAS) have identified loci contributing to insulin processing and secretion 1 , 2 ; however, a substantial fraction of the genetic contribution remains undefined. To examine low-frequency (minor allele frequency (MAF) 0.5–5%) and rare (MAF < 0.5%) nonsynonymous variants, we analyzed exome array data in 8,229 nondiabetic Finnish males using the Illumina HumanExome Beadchip. We identified low-frequency coding variants associated with fasting proinsulin concentrations at the SGSM2 and MADD GWAS loci and three new genes with low-frequency variants associated with fasting proinsulin or insulinogenic index: TBC1D30 , KANK1 and PAM . We also show that the interpretation of single-variant and gene-based tests needs to consider the effects of noncoding SNPs both nearby and megabases away. This study demonstrates that exome array genotyping is a valuable approach to identify low-frequency variants that contribute to complex traits.

Identifying genetic variants that influence human height will advance our understanding of skeletal growth and development. Several rare genetic variants have been convincingly and reproducibly associated with height in mendelian syndromes, and common variants in the transcription factor gene HMGA2 are associated with variation in height in the general population 1 . Here we report genome-wide association analyses, using genotyped and imputed markers, of 6,669 individuals from Finland and Sardinia, and follow-up analyses in an additional 28,801 individuals. We show that common variants in the osteoarthritis-associated locus 2 GDF5-UQCC contribute to variation in height with an estimated additive effect of 0.44 cm (overall P < 10 −15 ). Our results indicate that there may be a link between the genetic basis of height and osteoarthritis, potentially mediated through alterations in bone growth and development.

Screening of 134 Single Nucleotide Polymorphisms (SNPs) Previously Associated With Type 2 Diabetes Replicates Association With 12 SNPs in Nine Genes Cristen J. Willer 1 , Lori L. Bonnycastle 2 , Karen N. Conneely 1 , William L. Duren 1 , Anne U. Jackson 1 , Laura J. Scott 1 , Narisu Narisu 2 , Peter S. Chines 2 , Andrew Skol 1 , Heather M. Stringham 1 , John Petrie 2 , Michael R. Erdos 2 , Amy J. Swift 2 , Sareena T. Enloe 2 , Andrew G. Sprau 2 , Eboni Smith 2 , Maurine Tong 2 , Kimberly F. Doheny 3 , Elizabeth W. Pugh 3 , Richard M. Watanabe 4 , Thomas A. Buchanan 5 , Timo T. Valle 6 , Richard N. Bergman 7 , Jaakko Tuomilehto 6 8 , Karen L. Mohlke 9 , Francis S. Collins 2 and Michael Boehnke 1 1 Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 2 Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 3 Center for Inherited Disease Research, Johns Hopkins University School of Medicine, Baltimore, Maryland 4 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 5 Department of Medicine, Division of Endocrinology, Keck School of Medicine, University of Southern California, Los Angeles, California 6 Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, Helsinki, Finland 7 Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 8 Department of Public Health, University of Helsinki, Helsinki, Finland, and the South Ostrobothnia Central Hospital, Seinäjoki, Finland 9 Department of Genetics, University of North Carolina, Chapel Hill, North Carolina Address correspondence and reprint requests to Michael Boehnke, PhD, Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029. E-mail: boehnke{at}umich.edu Abstract More than 120 published reports have described associations between single nucleotide polymorphisms (SNPs) and type 2 diabetes. However, multiple studies of the same variant have often been discordant. From a literature search, we identified previously reported type 2 diabetes–associated SNPs. We initially genotyped 134 SNPs on 786 index case subjects from type 2 diabetes families and 617 control subjects with normal glucose tolerance from Finland and excluded from analysis 20 SNPs in strong linkage disequilibrium ( r 2 > 0.8) with another typed SNP. Of the 114 SNPs examined, we followed up the 20 most significant SNPs ( P < 0.10) on an additional 384 case subjects and 366 control subjects from a population-based study in Finland. In the combined data, we replicated association ( P < 0.05) for 12 SNPs: PPARG Pro12Ala and His447, KCNJ11 Glu23Lys and rs5210, TNF −857, SLC2A2 Ile110Thr, HNF1A/TCF1 rs2701175 and GE117881_360, PCK1 −232, NEUROD1 Thr45Ala, IL6 −598, and ENPP1 Lys121Gln. The replication of 12 SNPs of 114 tested was significantly greater than expected by chance under the null hypothesis of no association ( P = 0.012). We observed that SNPs from genes that had three or more previous reports of association were significantly more likely to be replicated in our sample ( P = 0.03), although we also replicated 4 of 58 SNPs from genes that had only one previous report of association. FUSION, Finland-United States Investigation of NIDDM Genetics LD, linkage disequilibrium MAF, minor allele frequency MODY, maturity-onset diabetes of the young NGT, normal glucose tolerance SNP, single nucleotide polymorphism WHR, waist-to-hip ratio Footnotes Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org . The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Accepted September 18, 2006. Received April 7, 2006. DIABETES

Current findings from genetic studies of complex human traits often do not explain a large proportion of the estimated variation of these traits due to genetic factors. This could be, in part, due to overly stringent significance thresholds in traditional statistical methods, such as linear and logistic regression. Machine learning methods, such as Random Forests (RF), are an alternative approach to identify potentially interesting variants. One major issue with these methods is that there is no clear way to distinguish between probable true hits and noise variables based on the importance metric calculated. To this end, we are developing a method called the Relative Recurrency Variable Importance Metric (r2VIM), a RF-based variable selection method. Here, we apply r2VIM to the unrelated Genetic Analysis Workshop 19 data with simulated systolic blood pressure as the phenotype. We compare the number of “true” functional variants identified by r2VIM with those identified by linear regression analyses that use a Bonferroni correction to calculate a significance threshold. Our results show that r2VIM performed comparably to linear regression. Our findings are proof-of-concept for r2VIM, as it identifies a similar number of functional and nonfunctional variants as a more commonly used technique when the optimal importance score threshold is used.

Ischemic stroke (IS) is among the leading causes of death in Western countries. There is a significant genetic component to IS susceptibility, especially among young adults. To date, research to identify genetic loci predisposing to stroke has met only with limited success. We performed a genome-wide association (GWA) analysis of early-onset IS to identify potential stroke susceptibility loci. The GWA analysis was conducted by genotyping 1 million SNPs in a biracial population of 889 IS cases and 927 controls, ages 15–49 years. Genotypes were imputed using the HapMap3 reference panel to provide 1.4 million SNPs for analysis. Logistic regression models adjusting for age, recruitment stages, and population structure were used to determine the association of IS with individual SNPs. Although no single SNP reached genome-wide significance (P < 5 × 10−8), we identified two SNPs in chromosome 2q23.3, rs2304556 (in FMNL2; P = 1.2 × 10−7) and rs1986743 (in ARL6IP6; P = 2.7 × 10−7), strongly associated with early-onset stroke. These data suggest that a novel locus on human chromosome 2q23.3 may be associated with IS susceptibility among young adults.

Terri Beaty and colleagues report a genome-wide association study of cleft lip with/without cleft palate. They identified variants near MAFB and ABCA4 associated with risk of this birth defect in case-parent trios of European and Asian ancestry. Case-parent trios were used in a genome-wide association study of cleft lip with and without cleft palate. SNPs near two genes not previously associated with cleft lip with and without cleft palate ( MAFB , most significant SNP rs13041247, with odds ratio (OR) per minor allele = 0.704, 95% CI 0.635–0.778, P = 1.44 × 10 −11 ; and ABCA4 , most significant SNP rs560426, with OR = 1.432, 95% CI 1.292–1.587, P = 5.01 × 10 −12 ) and two previously identified regions (at chromosome 8q24 and IRF6 ) attained genome-wide significance. Stratifying trios into European and Asian ancestry groups revealed differences in statistical significance, although estimated effect sizes remained similar. Replication studies from several populations showed confirming evidence, with families of European ancestry giving stronger evidence for markers in 8q24, whereas Asian families showed stronger evidence for association with MAFB and ABCA4 . Expression studies support a role for MAFB in palatal development.