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Context Individuals with cystic fibrosis (CF) develop a distinct form of diabetes characterized by β-cell dysfunction and islet amyloid accumulation similar to type 2 diabetes (T2D), but generally have normal insulin sensitivity. CF-related diabetes (CFRD) risk is determined by both CFTR, the gene responsible for CF, and other genetic variants. Objective To identify genetic modifiers of CFRD and determine the genetic overlap with other types of diabetes. Design and Patients A genome-wide association study was conducted for CFRD onset on 5740 individuals with CF. Weighted polygenic risk scores (PRSs) for type 1 diabetes (T1D), T2D, and diabetes endophenotypes were tested for association with CFRD. Results Genome-wide significance was obtained for variants at a novel locus (PTMA) and 2 known CFRD genetic modifiers (TCF7L2 and SLC26A9). PTMA and SLC26A9 variants were CF-specific; TCF7L2 variants also associated with T2D. CFRD was strongly associated with PRSs for T2D, insulin secretion, postchallenge glucose concentration, and fasting plasma glucose, and less strongly with T1D PRSs. CFRD was inconsistently associated with PRSs for insulin sensitivity and was not associated with a PRS for islet autoimmunity. A CFRD PRS comprising variants selected from these PRSs (with a false discovery rate < 0.1) and the genome-wide significant variants was associated with CFRD in a replication population. Conclusions CFRD and T2D have more etiologic and mechanistic overlap than previously known, aligning along pathways involving β-cell function rather than insulin sensitivity. Two CFRD risk loci are unrelated to T2D and may affect multiple aspects of CF. An 18-variant PRS stratifies risk of CFRD in an independent population.

Short telomere syndromes manifest as familial idiopathic pulmonary fibrosis; they are the most common premature aging disorders. We used genome-wide linkage to identify heterozygous loss of function of ZCCHC8, a zinc-knuckle containing protein, as a cause of autosomal dominant pulmonary fibrosis. ZCCHC8 associated with TR and was required for telomerase function. In ZCCHC8 knockout cells and in mutation carriers, genomically extended telomerase RNA (TR) accumulated at the expense of mature TR, consistent with a role for ZCCHC8 in mediating TR 3′ end targeting to the nuclear RNA exosome. We generated Zcchc8-null mice and found that heterozygotes, similar to human mutation carriers, had TR insufficiency but an otherwise preserved transcriptome. In contrast, Zcchc8−/− mice developed progressive and fatal neurodevelopmental pathology with features of a ciliopathy. The Zcchc8−/− brain transcriptome was highly dysregulated, showing accumulation and 3′ end misprocessing of other low-abundance RNAs, including those encoding cilia components as well as the intronless replication-dependent histones. Our data identify a novel cause of human short telomere syndromes-familial pulmonary fibrosis and uncover nuclear exosome targeting as an essential 3′ end maturation mechanism that vertebrate TR shares with replication-dependent histones.

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P, Reporting Summary. Further information on experimental design is available in the Life Sciences Reporting Summary linked to this article.

Purpose Cancer of the prostate (CaP) is the leading cancer among men in sub-Saharan Africa (SSA). A substantial proportion of these men with CaP are diagnosed at late (usually incurable) stages, yet little is known about the etiology of CaP in SSA. Methods We established the Men of African Descent and Carcinoma of the Prostate Network, which includes seven SSA centers partnering with five US centers to study the genetics and epidemiology of CaP in SSA. We developed common data elements and instruments, regulatory infrastructure, and biosample collection, processing, and shipping protocols. We tested this infrastructure by collecting epidemiologic, medical record, and genomic data from a total of 311 patients with CaP and 218 matched controls recruited at the seven SSA centers. We extracted genomic DNA from whole blood, buffy coat, or buccal swabs from 265 participants and shipped it to the Center for Inherited Disease Research (Baltimore, MD) and the Centre for Proteomics and Genomics Research (Cape Town, South Africa), where genotypes were generated using the UK Biobank Axiom Array. Results We used common instruments for data collection and entered data into the shared database. Double-entered data from pilot participants showed a 95% to 98% concordance rate, suggesting that data can be collected, entered, and stored with a high degree of accuracy. Genotypes were obtained from 95% of tested DNA samples (100% from blood-derived DNA samples) with high concordance across laboratories. Conclusion We provide approaches that can produce high-quality epidemiologic and genomic data in multicenter studies of cancer in SSA.

Megacystis-microcolon-intestinal hypoperistalsis syndrome (MMIHS) is a severe disease characterized by functional obstruction in the urinary and gastrointestinal tract. The molecular basis of this condition started to be defined recently, and the genes related to the syndrome (ACTG2-heterozygous variant in sporadic cases; and MYH11 (myosin heavy chain 11), LMOD1 (leiomodin 1) and MYLK (myosin light chain (MLC) kinase)-autosomal recessive inheritance), encode proteins involved in the smooth muscle contraction, supporting a myopathic basis for the disease. In the present article, we described a family with two affected siblings with MMIHS born to consanguineous parents and the molecular investigation performed to define the genetic etiology. Previous whole exome sequencing of the affected child and parents did not identify a candidate gene for the disease in this family, but now we present a reanalysis of the data that led to the identification of a homozygous deletion encompassing the last exon of MYL9 (myosin regulatory light chain 9) in the affected individual. MYL9 gene encodes a regulatory myosin MLC and the phosphorylation of this protein is a crucial step in the contraction process of smooth muscle cell. Despite the absence of human or animal phenotype related to MYL9, a cause-effect relationship between MYL9 and the MMIHS seems biologically plausible. The present study reveals a strong candidate gene for autosomal recessive forms of MMIHS, expanding the molecular basis of this disease and reinforces the myopathic basis of this condition.

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and population-level differences in prostate cancer risk were revealed using a novel genotyping array. Significance: This study presents an Africa-specific genotyping array, which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

Genotyping arrays have been widely adopted as an efficient means to interrogate variation across the human genome. Genetic variants may be observed either directly, via genotyping, or indirectly, through linkage disequilibrium with a genotyped variant. The total proportion of genomic variation captured by an array, either directly or indirectly, is referred to as “genomic coverage.” Here we use genotype imputation and Phase 3 of the 1000 Genomes Project to assess genomic coverage of several modern genotyping arrays. We find that in general, coverage increases with increasing array density. However, arrays designed to cover specific populations may yield better coverage in those populations compared to denser arrays not tailored to the given population. Ultimately, array choice involves trade-offs between cost, density, and coverage, and our work helps inform investigators weighing these choices and trade-offs.

The OCAC OncoArray genotyping project: et al., To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC., The OCAC OncoArray genotyping project was funded through grants from the US National Institutes of Health (CA1X01HG007491-01 (C.I.A.), U19-CA148112 (T.A.S.), R01-CA149429 (C.M.P.) and R01-CA058598 (M.T.G.)); Canadian Institutes of Health Research (MOP-86727 (L.E.K.)); and the Ovarian Cancer Research Fund (A.B.). Funding for the CIMBA OncoArray genotyping was provided by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministère de l’Économie, de la Science et de l’Innovation du Québec through Génome Québec, the Quebec Breast Cancer Foundation for the PERSPECTIVE project, the US National Institutes of Health (CA1X01HG007491-01 (C.I.A.)), the Odense University Hospital Research Foundation (M.T.), the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (1420190 (S.K.P.)), the Italian Association for Cancer Research (IG16933 (L.O.)) and German Cancer Aid (110837 (R.K.S.).

Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P

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.

Background: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers and cancer related traits. Methods: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. Results: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. Conclusions: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental and lifestyle related exposures. Impact: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. TRICL (Transdisciplinary Research for Cancer of Lung) and International Lung Cancer Consortium (ILCCO): National Institute of Health U19 CA148127-01 (C.I. Amos, J. Byun, Y. Li, X. Xiao, J. L. Forman, A. Risch, H. Bickeböller, M. Waldenberger, I. Brüske, J. Manz, P. Brennan, R. Hung, H. Shen, Y. Shi, A. Kamal, C. I. Amos, J.K. Field), Canadian Cancer Society Research Institute (no. 020214, R. Hung). DRIVE (Discovery, Biology, and Risk of Inherited Variants in Breast Cancer): National Institute of Health U19 CA148065 (D.J. Hazelett, D. F. Easton, S. Lindström, P. Kraft, J. Dennis, A. Dunning, K. Michailidou, L. Fachal, S. Benlloch, J. Cunningham, K. Butterbach, K. Offit, R. Schmutzler, L. Ottini, D. Vincent). CORECT (ColoRectal Transdisciplinary Study): National Institute of Health U19 CA148107; R01 CA81488, P30 CA014089 (S. Gruber, U. Peters, G. Casey). ELLIPSE (ELLIPSE, Elucidating Loci in Prostate Cancer Susceptibility): This work was support by U19 CA148537(F. Schumacher, S.A. Gayther, S.L. Schmit, C.K. Edlund, D.J. Hazelett, G.A. Coetzee, C. Haiman, S. Demetriades, D. Van Den Berg). FOCI (Transdisciplinary Cancer Genetic Association and Interacting Studies): National Institutes of Health U19 CA148112- 01 (T.A. Sellers, C. Phelan), R01-CA122443(B. Hicks) ) P50-CA116201, CA192393 (F. Couch), P50-CA136393(J. Cunningham, F. Couch), OCAC - CA-149429 (C. Phelan). , P30-CA15083 (B. Hicks), Cancer Research UK (C490/A8339(A. Antoniou, P. Pharoah, A. Dunning), C490/A16561(P. Pharoah, A. Dunning), C490/A10119 (A. Dunning, P. Pharoah), C490/A10124 (A. Dunning, P. Pharoah)). ASTERISK: a Hospital Clinical Research Program (PHRC) and supported by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC) (F. Bacot). DACHS: German Research Council (Deutsche Downloaded from cebp.aacrjournals.org on October 31, 2016. © 2016 American Association for Cancer Research. Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on October 3, 2016; DOI: 10.1158/1055-9965.EPI-16-0106 Forschungsgemeinschaft, BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, and CH 117/1-1, C.K. Edlund), and the German Federal Ministry of Education and Research (01KH0404 and 01ER0814). Prostate Lung Colon Ovarian Screening trial: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837, coordinator: R. Schmutzler, Cologne). The PERSPECTIVE (J. Simard) project was supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (MOP-86727, L.E. Kelemen), the Ministère de l’Économie, Innovation et Exportation du Québec through Genome Québec, and thee Quebec Breast Cancer Foundation, including additional funding from the Canadian Breast Cancer Foundation and the National Cancer Institute USA through Breast Cancer Family Registry Cohort (#1UM1CA164920-01A1). European Union Framework Programme for Research and Innovation (MSCA-IF-2014-EF-656144)(L. Fachal). The Breast Cancer Research Foundation, P30 CA08748, Andrew Sabin Foundation (K. Offit). CIDR genotyping for the Oncoarray was conducted under contract 268201200008I to K.Doheny and through grant 1X01HG007491- 01 to C.I. Amos. This is the author accepted manuscript. The final version is available from American Association for Cancer Research via http://dx.doi.org/10.1158/1055-9965.EPI-16-0106

High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure. peerReviewed

Background Preterm delivery (PTD) is the leading cause of neonatal morbidity and mortality. Epidemiologic studies indicate recurrence of PTD is maternally inherited creating a strong possibility that mitochondrial variants contribute to its etiology. This study examines the association between mitochondrial genotypes with PTD and related outcomes. Methods This study combined, through meta-analysis, two case-control, genome-wide association studies (GWAS); one from the Danish National Birth Cohort (DNBC) Study and one from the Norwegian Mother and Child Cohort Study (MoBa) conducted by the Norwegian Institute of Public Health. The outcomes of PTD (≤36 weeks), very PTD (≤32 weeks) and preterm prelabor rupture of membranes (PPROM) were examined. 135 individual SNP associations were tested using the combined genome from mothers and neonates (case vs. control) in each population and then pooled via meta-analysis. Results After meta-analysis there were four SNPs for the outcome of PTD below p≤0.10, and two below p≤0.05. For the additional outcomes of very PTD and PPROM there were three and four SNPs respectively below p≤0.10. Conclusion Given the number of tests no single SNP reached study wide significance (p=0.0006). Our study does not support the hypothesis that mitochondrial genetics contributes to the maternal transmission of PTD and related outcomes.

Aims Nicotine dependence is a highly heritable disorder associated with severe medical morbidity and mortality. Recent meta-analyses have found novel genetic loci associated with cigarettes per day (CPD), a proxy for nicotine dependence. The aim of this paper is to evaluate the importance of phenotype definition (i.e. CPD versus Fagerstrom Test for Cigarette Dependence (FTCD) score as a measure of nicotine dependence) on genome-wide association studies of nicotine dependence. Design Genome-wide association study. Setting Community sample. Participants A total of 3365 subjects who had smoked at least one cigarette were selected from the Study of Addiction: Genetics and Environment (SAGE). Of the participants, 2267 were European Americans, 999 were African Americans. Measurements Nicotine dependence defined by FTCD score ≥4, CPD. Findings The genetic locus most strongly associated with nicotine dependence was rs1451240 on chromosome 8 in the region of CHRNB3 [odds ratio (OR) = 0.65, P = 2.4 × 10−8]. This association was further strengthened in a meta-analysis with a previously published data set (combined P = 6.7 × 10−16, total n = 4200). When CPD was used as an alternate phenotype, the association no longer reached genome-wide significance (β = −0.08, P = 0.0004). Conclusions Daily cigarette consumption and the Fagerstrom Test for Cigarette Dependence show different associations with polymorphisms in genetic loci.

Clonal mosaicism for large chromosomal anomalies (duplications, deletions and uniparental disomy) was detected using SNP microarray data from over 50,000 subjects recruited for genome-wide association studies. This detection method requires a relatively high frequency of cells (>5–10%) with the same abnormal karyotype (presumably of clonal origin) in the presence of normal cells. The frequency of detectable clonal mosaicism in peripheral blood is low (

Genome-wide association studies (GWAS) are a useful approach in the study of the genetic components of complex phenotypes. Aside from large cohorts, GWAS have generally been limited to the study of one or a few diseases or traits. The emergence of biobanks linked to electronic medical records (EMRs) allows the efficient re-use of genetic data to yield meaningful genotype-phenotype associations for multiple phenotypes or traits. Phase I of the electronic MEdical Records and GEnomics (eMERGE-I) Network is a National Human Genome Research Institute (NHGRI)-supported consortium composed of five sites to perform various genetic association studies using DNA repositories and EMR systems. Each eMERGE site has developed EMR-based algorithms to comprise a core set of fourteen phenotypes for extraction of study samples from each site’s DNA repository. Each eMERGE site selected samples for a specific phenotype, and these samples were genotyped at either the Broad Institute or at the Center for Inherited Disease Research (CIDR) using the Illumina Infinium BeadChip technology. In all, approximately 17,000 samples from across the five sites were genotyped. A unified quality control (QC) pipeline was developed by the eMERGE Genomics Working Group and used to ensure thorough cleaning of the data. This process includes examination of sample quality, marker quality, and various batch effects. Upon completion of the genotyping and QC analyses for each site’s primary study, the eMERGE Coordinating Center merged the datasets from all five sites. This larger merged dataset re-entered the established eMERGE QC pipeline. Based on lessons learned during the process, additional analyses and QC checkpoints were added to the pipeline to ensure proper merging. Here we explore the challenges associated with combining datasets from different genotyping centers and describe the expansion to the eMERGE QC pipeline for merged datasets. These additional steps will be useful as the eMERGE project expands to include additional sites in eMERGE-II and also serve as a starting point for investigators merging multiple genotype data sets accessible through the National Center for Biotechnology Information (NCBI) in the database of Genotypes and Phenotypes (dbGaP). Our experience demonstrates that merging multiple datasets after additional QC can be an efficient use of genotype data despite new challenges that appear in the process.

Genotyping experiments are widely used in clinical and basic research laboratories to identify associations between genetic variations and normal/abnormal phenotypes. Genotyping assay techniques vary from single genomic regions that are interrogated using PCR reactions to high throughput assays examining genome-wide sequence and structural variation. The resulting genotype data may include millions of markers of thousands of individuals, requiring various statistical, modeling or other data analysis methodologies to interpret the results. To date, there are no standards for reporting genotyping experiments. Here we present the Minimum Information about a Genotyping Experiment (MIGen) standard, defining the minimum information required for reporting genotyping experiments. MIGen standard covers experimental design, subject description, genotyping procedure, quality control and data analysis. MIGen is a registered project under MIBBI (Minimum Information for Biological and Biomedical Investigations) and is being developed by an interdisciplinary group of experts in basic biomedical science, clinical science, biostatistics and bioinformatics. To accommodate the wide variety of techniques and methodologies applied in current and future genotyping experiment, MIGen leverages foundational concepts from the Ontology for Biomedical Investigations (OBI) for the description of the various types of planned processes and implements a hierarchical document structure. The adoption of MIGen by the research community will facilitate consistent genotyping data interpretation and independent data validation. MIGen can also serve as a framework for the development of data models for capturing and storing genotyping results and experiment metadata in a structured way, to facilitate the exchange of metadata.

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.

White blood cell count (WBC) is unique among identified inflammatory predictors of chronic disease in that it is routinely measured in asymptomatic patients in the course of routine patient care. We led a genome-wide association analysis to identify variants associated with WBC levels in 13,923 subjects in the electronic Medical Records and Genomics (eMERGE) Network. We identified two regions of interest that were each unique to subjects of genetically determined ancestry to the African continent (AA) or to the European continent (EA). WBC varies among different ancestry groups. Despite being ancestry specific, these regions were identifiable in the combined analysis. In AA subjects, the region surrounding the Duffy antigen/chemokine receptor gene (DARC) on 1q21 exhibited significant association (p value = 6.71e–55). These results validate the previously reported association between WBC and of the regulatory variant rs2814778 in the promoter region, which causes the Duffy negative phenotype (Fy−/−). A second missense variant (rs12075) is responsible for the two principal antigens, Fya and Fyb of the Duffy blood group system. The two variants, consisting of four alleles, act in concert to produce five antigens and subsequent phenotypes. We were able to identify the marginal and novel interaction effects of these two variants on WBC. In the EA subjects, we identified significantly associated SNPs tagging three separate genes in the 17q21 region: (1) GSDMA, (2) MED24, and (3) PSMD3. Variants in this region have been reported to be associated with WBC, neutrophil count, and inflammatory diseases including asthma and Crohn’s disease.

With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane-Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data-adaptive methods.

Cannabis dependence is the third leading contributor to admissions to chemical dependency treatment settings (Treatment Episode Data Set, 2003) and rates of past year cannabis dependence in the U.S. population have increased by 18% since 1991–1992 (Compton et al., 2004). Several studies report strong heritable influences on cannabis dependence [h2 of 50–70%] (Agrawal and Lynskey, 2006). In an effort to identify contributors to this composite heritability, multiple linkage and association studies have been conducted. Several promising linkage regions (e.g. chromosome 3) exist. Candidate gene studies have largely focused on variants in the gene encoding the cannabinoid receptor CNR1 – results remain equivocal (Agrawal and Lynskey, 2009). To our knowledge, there are currently no published genomewide association studies (GWAS) of cannabis dependence. The goal of this investigation is to conduct association analysis between 948,142 single nucleotide polymorphisms (SNPs) and lifetime DSM-IV cannabis dependence using data on 708 cases with DSM-IV cannabis dependence and 2346 controls who report lifetime cannabis use but do not meet criteria for DSM-IV dependence. Data for this study come from the Study of Addiction: Genes and Environment (SAGE) (Bierut et al., 2010), which was one of the 8 Phase 1 studies of the Gene Environment Association (GENEVA) consortium (Cornelis et al., 2010). The study was designed primarily to study DSM-IV alcohol dependence as well as other correlated addiction phenotypes. All phenotypic data were collected using the Semi-Structured Assessment for the Genetics of Alcoholism Interview (SSAGA) (Bucholz et al., 1994b). Reliability of SSAGA diagnoses of substance use disorders is good (kappas of 0.7 and higher)(Bucholz et al., 1994a). Case status for the analyses reported here was defined as a lifetime history of DSM-IV cannabis dependence, modified to include cannabis withdrawal (i.e. 3 or more of 7 criteria clustering within a 12-month period). Controls had used cannabis at least once in their lifetime but did not meet criteria for DSM-IV dependence criteria (however, those meeting criteria for dependence on other psychoactive substances, including alcohol, were not excluded). Those who had never used cannabis even once in their lifetime (N=946) were omitted (however, re-doing analyses including them as unaffected showed similar results). DNA samples were genotyped on the Illumina Human 1M beadchip by the Center for Inherited Diseases Research (CIDR) at Johns Hopkins University. 948,658 SNPs passed data cleaning procedures – further within-sample filtering for autosomal and X-chromosome markers yielded 948,142 markers. HapMap genotyping controls, duplicates, related subjects, and outliers were removed from the sample set. Two thousand and nineteen subjects reported being European American and 1,035 reported being African American. Further details are available in a related publication (Bierut et al., 2010). Logistic regressions analyses were conducted in PLINK (Purcell et al., 2007), controlling for sex, age (defined, using quartiles, as 3 dummy measures representing 34 years or younger, 35–39 years, 40–44 years, with 45 years and older as the reference group), study source (see Bierut et al., 2010 for details) and two principal components (computed in EIGENSTRAT/EIGENSOFT, (Price et al., 2006)) indexing continuous variation in ethnicity. The overall genomic inflation factor was 1.014 after controlling for principal components representing ethnicity, suggesting minimal stratification effects. Genotypes (917,694 autosomal SNPs and 30,448 SNPs on the X-chromosome) were coded as 0, 1 or 2 copies of the reference allele so that risk associated with genotype was modified with each additional copy of the reference allele. DSM-IV cannabis dependent cases were significantly more likely to be males (68% vs. 43%) and were less likely to report completion of high school (18% vs 10% who did not complete). Consistent with ascertainment, cases were significantly more likely than controls to meet criteria for alcohol (94% vs 46%), nicotine (78% vs 46%) and cocaine (73% vs 21%) dependence, greater use of other illicit drugs (88% vs 47%) as well as report a younger age at initiation of cannabis use (14.5 vs 17.4 years) and more recent (44% vs 19% using in the past year) cannabis involvement. Thus, cases show marked phenotypic similarity to the highly heritable TypeII/B cluster of cannabis dependent subjects (Ehlers et al., 2009). The lowest p-values were obtained for polymorphisms on chromosome 17 (rs1019238 and rs1431318, p-values < E−7; rs8065311, p-value < E−6; rs9894332 and rs10521290, p-values < E−5). The top SNP lies in the ankyrin-repeat and fibronectin type III domain containing 1 (ANKFN1) gene, which was previously identified in a genomic study of general vulnerability to substance use disorders (Johnson et al., 2009). The genomic control p-value for rs1019238 was 7.3 × 10−7 suggesting adequate control for ethnic variation. The remaining SNPs (except rs10521290) are non-genic but are in strong LD (r2 of 0.75–0.88) with SNPs in ANKFN1. Fibronectin repeats are important constituents of a variety of proteins (Bloom and Calabro, 2009) and ankyrin repeats mediate protein interactions (Li et al., 2006). While there are links between ANKFN1 and PTEN (phosphatase and tensin homolog) in tumorigenesis (Syed et al., 2010), the specific role that ANKFN1 might play in the etiology of cannabis dependence is unknown. Table 1 presents odds-ratios for the full sample, as well as results stratified by ethnicity, for SNPs that yielded the most promising results. A number of genes on chromosome 12 (including the p- and q-arm) were associated, including carbohydrate (chondroitin 4) sulfotransferase 11 (CHST11). None of the SNPs that emerged as our top findings have known functional significance. Association results across the full autosomal genome and the X chromosome are shown in Figure 1. Figure 1 Manhattan plot showing genomewide association results for autosomal SNPs and SNPs on the X chromosome for DSM-IV cannabis dependence in SAGE. Table 1 Association results of top 30 SNPs from a genomewide association study of 708 DSM-IV cannabis dependence cases and 2346 controls from SAGE. (All allele frequencies and odds-ratios reflect analysis of increasing risk associated with the reference allele). ... While our primary analyses accounted for population stratification, we also show, in Table 1, findings within each ethnic group. A number of the SNPs showed ethnic variations in allele frequency. In a majority of instances, odds-ratios were comparable across the European- and African-American samples. However, 5 SNPs were found to have exceedingly low MAFs in the European-American sample suggesting that their overall significance in the full sample may be attributable to their effects in the smaller African-American sample. A limitation of this study is that SAGE was ascertained for alcohol dependence. This led to a high level of comorbidity in the cannabis dependent cases and exposed controls. However, the use of controls with other forms of substance dependence, but not cannabis dependence, protected against signals that may have been less specific. In addition, a series of analyses was conducted that supported the specificity of these findings. First, SNPs in ANKFN1 were not significant when case status was alcohol (also nicotine and cocaine) dependence without comorbid cannabis dependence. Second, we created a polydrug dependence diagnosis, and results were not significant when cannabis dependent individuals were excluded from those with other polydrug dependence. While a sample ascertained for cannabis dependence would be ideal, there are challenges associated with the recruitment of such a sample (Agrawal and Lynskey, 2009), and to our knowledge, no such samples currently exist. Further, while cannabis dependence is highly heritable, none of our association signals reach genomewide significance, nor do the composite of the top 30 signals explain a considerable proportion of this heritable variance in cannabis dependence. The Benjamini-Hochberg False Discovery Rate (Benjamini and Hochberg, 1995) for the top signal was .325. Power computations reveal that at MAFs ranging from 15–40%, association signals with odds-ratios exceeding 1.45, such as our top SNP, would be detected. Thus, both replication and meta-analysis are required to confirm or refute these findings. Characterizing the genetic underpinnings of cannabis dependence remains elusive – to this end, results from GWAS might provide clues into alternative biological pathways that shape the etiology of cannabis dependence.

Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies. This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium (HWE) test p-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis (PCA) to SNP selection. The methods are illustrated with examples from the ‘Gene Environment Association Studies’ (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of genome-wide association studies.

Parkinson's disease is a common disorder that leads to motor and cognitive disability. We performed a genome-wide association study of 2,000 individuals with Parkinson's disease (cases) and 1,986 unaffected controls from the NeuroGenetics Research Consortium (NGRC)(1-5). We confirmed associations with SNCA(2,6-8) and MAPT(3,7-9), replicated an association with GAK9 (using data from the NGRC and a previous study(9), P = 3.2 x 10(-9)) and detected a new association with the HLA region (using data from the NGRC only, P = 2.9 x 10(-8)), which replicated in two datasets (meta-analysis P = 1.9 x 10(-10)). The HLA association was uniform across all genetic and environmental risk strata and was strong in sporadic (P = 5.5 x 10(-10)) and late-onset (P = 2.4 x 10(-8)) disease. The association peak we found was at rs3129882, a noncoding variant in HLA-DRA. Two studies have previously suggested that rs3129882 influences expression of HLA-DR and HLA-DQ(10,11). The brains of individuals with Parkinson's disease show upregulation of DR antigens and the presence of DR-positive reactive microglia(12), and nonsteroidal anti-inflammatory drugs reduce Parkinson's disease risk(4,13). The genetic association with HLA supports the involvement of the immune system in Parkinson's disease and offers new targets for drug development.

Context: Several genome-wide association studies (GWAS) have been performed to identify genes contributing to bone mineral density (BMD), typically in samples of elderly women and men. Objective: The objective of the study was to identify genes contributing to BMD in premenopausal women. Design: GWAS using the Illumina 610Quad array in premenopausal European-American (EA) women and replication of the top 50 single-nucleotide polymorphisms (SNPs) for two BMD measures in African-American (AA) women. Subjects: Subjects included 1524 premenopausal EA women aged 20–45 yr from 762 sibships and 669 AA premenopausal women aged 20–44 yr from 383 sibships. Interventions: There were no interventions. Main Outcome Measures: BMD was measured at the lumbar spine and femoral neck by dual-energy x-ray absorptiometry. Age- and weight-adjusted BMD values were tested for association with each SNP, with P values determined by permutation. Results: SNPs in CATSPERB on chromosome 14 provided evidence of association with femoral neck BMD (rs1298989, P = 2.7 × 10−5; rs1285635, P = 3.0 × 10−5) in the EA women, and some supporting evidence was also observed with these SNPs in the AA women (rs1285635, P = 0.003). Genes identified in other BMD GWAS studies, including IBSP and ADAMTS18, were also among the most significant findings in our GWAS. Conclusions: Evidence of association to several novel loci was detected in a GWAS of premenopausal EA women, and SNPs in one of these loci also provided supporting evidence in a sample of AA women.

Excessive alcohol consumption is one of the leading causes of preventable death in the United States. Approximately 14% of those who use alcohol meet criteria during their lifetime for alcohol dependence, which is characterized by tolerance, withdrawal, inability to stop drinking, and continued drinking despite serious psychological or physiological problems. We explored genetic influences on alcohol dependence among 1,897 European-American and African-American subjects with alcohol dependence compared with 1,932 unrelated, alcohol-exposed, nondependent controls. Constitutional DNA of each subject was genotyped using the Illumina 1M beadchip. Fifteen SNPs yielded P < 10 −5 , but in two independent replication series, no SNP passed a replication threshold of P < 0.05. Candidate gene GABRA2 , which encodes the GABA receptor α2 subunit, was evaluated independently. Five SNPs at GABRA2 yielded nominal (uncorrected) P < 0.05, with odds ratios between 1.11 and 1.16. Further dissection of the alcoholism phenotype, to disentangle the influence of comorbid substance-use disorders, will be a next step in identifying genetic variants associated with alcohol dependence.

Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene-trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N > 80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.

Three genetic loci for lung cancer risk have been identified by genome-wide association studies (GWAS), but inherited susceptibility to specific histologic types of lung cancer is not well established. We conducted a GWAS of lung cancer and its major histologic types, genotyping 515,922 single-nucleotide polymorphisms (SNPs) in 5739 lung cancer cases and 5848 controls from one population-based case-control study and three cohort studies. Results were combined with summary data from ten additional studies, for a total of 13,300 cases and 19,666 controls of European descent. Four studies also provided histology data for replication, resulting in 3333 adenocarcinomas (AD), 2589 squamous cell carcinomas (SQ), and 1418 small cell carcinomas (SC). In analyses by histology, rs2736100 (TERT), on chromosome 5p15.33, was associated with risk of adenocarcinoma (odds ratio [OR] = 1.23, 95% confidence interval [CI] = 1.13–1.33, p = 3.02 × 10−7), but not with other histologic types (OR = 1.01, p = 0.84 and OR = 1.00, p = 0.93 for SQ and SC, respectively). This finding was confirmed in each replication study and overall meta-analysis (OR = 1.24, 95% CI = 1.17–1.31, p = 3.74 × 10−14 for AD; OR = 0.99, p = 0.69 and OR = 0.97, p = 0.48 for SQ and SC, respectively). Other previously reported association signals on 15q25 and 6p21 were also refined, but no additional loci reached genome-wide significance. In conclusion, a lung cancer GWAS identified a distinct hereditary contribution to adenocarcinoma.

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 (r2 > 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.

Genetic risk factors for intracranial aneurysm (IA) are not yet fully understood. Genomewide association studies have been successful at identifying common variants; however, the role of rare variation in IA susceptibility has not been fully explored. In this study, we report the use of whole exome sequencing (WES) in seven densely-affected families (45 individuals) recruited as part of the Familial Intracranial Aneurysm study. WES variants were prioritized by functional prediction, frequency, predicted pathogenicity, and segregation within families. Using these criteria, 68 variants in 68 genes were prioritized across the seven families. Of the genes that were expressed in IA tissue, one gene (TMEM132B) was differentially expressed in aneurysmal samples (n=44) as compared to control samples (n=16) (false discovery rate adjusted p-value=0.023). We demonstrate that sequencing of densely affected families permits exploration of the role of rare variants in a relatively common disease such as IA, although there are important study design considerations for applying sequencing to complex disorders. In this study, we explore methods of WES variant prioritization, including the incorporation of unaffected individuals, multipoint linkage analysis, biological pathway information, and transcriptome profiling. Further studies are needed to validate and characterize the set of variants and genes identified in this study.

Herpes zoster, commonly referred to as shingles, is caused by the varicella zoster virus (VZV). VZV initially manifests as chicken pox, most commonly in childhood, can remain asymptomatically latent in nerve tissues for many years and often re-emerges as shingles. Although reactivation may be related to immune suppression, aging and female sex, most inter-individual variability in re-emergence risk has not been explained to date. We performed a genome-wide association analyses in 22,981 participants (2280 shingles cases) from the electronic Medical Records and Genomics Network. Using Cox survival and logistic regression, we identified a genomic region in the combined and European ancestry groups that has an age of onset effect reaching genome-wide significance (P>1.0 × 10(-8)). This region tags the non-coding gene HCP5 (HLA Complex P5) in the major histocompatibility complex. This gene is an endogenous retrovirus and likely influences viral activity through regulatory functions. Variants in this genetic region are known to be associated with delay in development of AIDS in people infected by HIV. Our study provides further suggestion that this region may have a critical role in viral suppression and could potentially harbor a clinically actionable variant for the shingles vaccine.

The pattern and nature of linkage disequilibrium in the human genome is being studied and catalogued as part of the International HapMap Project [:2003 Nature 426:789-796]. A key goal of the HapMap Project is to enable identification of tag single nucleotide polymorphisms (SNPs) that capture a substantial portion of common human genetic variability while requiring only a small fraction of SNPs to be genotyped [International HapMap Consortium, 2005: Nature 437:1299-1320]. In the current study, we examined the effectiveness of using the CEU HapMap database to select tag SNPs for a Finnish sample. We selected SNPs in a 17.9-Mb region of chromosome 14 based on pairwise linkage disequilibrium (r(2)) estimates from the HapMap CEU sample, and genotyped 956 of these SNPs in 1,425 Finnish individuals. An excess of SNPs showed significantly different allele frequencies between the HapMap CEU and the Finnish samples, consistent with population-specific differences. However, we observed strong correlations between the two samples for estimates of allele frequencies, r(2) values, and haplotype frequencies. Our results demonstrate that the HapMap CEU samples provide an adequate basis for tag SNP selection in Finnish individuals, without the need to create a map specifically for the Finnish population, and suggest that the four-population HapMap data will provide useful information for tag SNP selection beyond the specific populations from which they were sampled.

The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. Approximately 51,000 DNA samples from distinct individuals have been genotyped using genome-wide SNP arrays across the nine sites of the network. The eMERGE Coordinating Center and the Genomics Workgroup developed a pipeline to impute and merge genomic data across the different SNP arrays to maximize sample size and power to detect associations with a variety of clinical endpoints. The 1000 Genomes cosmopolitan reference panel was used for imputation. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R (2) (estimated correlation between the imputed and true genotypes), and the relationship between allelic R (2) and minor allele frequency. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets. The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR.

Study Design. A genomic screen and statistical linkage analysis of 202 families with at least two individuals with idiopathic scoliosis was performed. Objectives. To identify candidate regions or the autosomal loci that may be involved in the expression of familial idiopathic scoliosis. Summary of Background Data. A large sample of families with individuals having idiopathic scoliosis (202 families; 1,198 individuals) was ascertained; diagnoses were based on physical examination and radiographic criteria. Methods. Model-independent linkage analysis of qualitative and quantitative traits (degree of lateral curvature) related to scoliosis was used to screen genotyping data from 391 markers in the 202 families. Subsets of families were determined before genotyping based on the most likely mode of inheritance for each family (autosomal dominant vs. X-linked dominant). Fine mapping results corroborated linkage in the primary candidate regions. Results. Candidate regions on chromosomes 6, 9, 16, and 17 were considered to have the strongest evidence for linkage across all subsets considered. Conclusion. Linkage analyses have identified several candidate regions, a significant step in defining the genetic etiology of this disorder.

Genetic Analysis Workshop 14 (GAW14) was held September 7–10, 2005, in Noordwijkerhout, The Netherlands. The overarching theme was the comparison of microsatellite and single-nucleotide polymorphism (SNP) markers for genome-wide scans, and the statistical methods that can best exploit the information provided in such scans for linkage and association studies. The 183 contributions submitted to GAW14 were organized into 18 presentation groups of 7–15 papers each. GAW14 participants had the choice of two data sets to analyze. COGA generously donated extensive family data on alcoholism, related phenotypes, pertinent covariates, and a set of previously analyzed genomescan microsatellite marker genotypes. The simulated data were designed to have similarities to the real data set. Extensive new SNP genotyping was performed on DNA provided by COGA for the previously genotyped families. Illumina, Affymetrix, and the Center for Inherited Disease Research performed this work and donated these data to GAW14 and to COGA. The group summary papers collected in this issue present the major findings from GAW14. Genet. Epidemiol. 29(Suppl. 1):S1–S6, 2005. Published 2005 Wiley-Liss, Inc.

Peri-urban visceral leishmaniasis (VL) caused by Leishmania chagasi is emerging in a new epidemiologic pattern in Brazilian cities. We studied peri-urban VL in endemic neighborhoods surrounding Natal, Brazil, identified through hospitalized individuals with VL. Clinical and environmental information obtained for 1106 members of 216 families living in endemic neighborhoods enabled us to identify 4 groups: VL: individuals with current or prior symptomatic visceral leishmaniasis (n = 135); DTH+: individuals with positive delayed-type hypersensitivity response with no history of VL (n = 390); Ab +: individuals with negative DTH response and seropositive (n = 21); DTH -: individuals with negative DTH and seronegative (n = 560). The mean +/-SD age of VL was 9.3+/-12.3 y. The gender distribution was nearly equal below age 5, but skewed toward males at higher ages. Acutely infected VL subjects had significantly lower hematocrits, neutrophils, and eosinophils than other categories. AB+ subjects also had lower eosinophil counts than others, a possible immune marker of early infection. VL was not associated with ownership of dogs or other animals, raising the question whether the reservoir differs in peri-urban settings. This new pattern of L. chagasi infection enables us to identify epidemiological and host factors underlying this emerging infectious disease.

The aim of the Finland-United States Investigation of NIDDM Genetics (FUSION) study is to identify genes that predispose to type 2 diabetes or are responsible for variability in diabetes-related traits via a positional cloning and positional candidate gene approach. In a previously published genome-wide scan of 478 Finnish affected sibling pair (ASP) families (FUSION 1), the strongest linkage results were on chromosomes 20 and 11. We now report a second genome-wide scan using an independent set of 242 Finnish ASP families (FUSION 2), a detailed analysis of the combined set of 737 FUSION 1 + 2 families (495 updated FUSION 1 families), and fine mapping of the regions of chromosomes 11 and 20. The strongest FUSION 2 linkage results were on chromosomes 6 (maximum logarithm of odds score [MLS] = 2.30 at 95 cM) and 14 (MLS = 1.80 at 57 cM). For the combined FUSION 1 + 2 families, three results were particularly notable: chromosome 11 (MLS = 2.98 at 82 cM), chromosome 14 (MLS = 2.74 at 58 cM), and chromosome 6 (MLS = 2.66 at 96 cM). We obtained smaller FUSION 1 + 2 MLSs on chromosomes X (MLS = 1.27 at 152 cM) and 20p (MLS = 1.21 at 20 cM). Among the 10 regions that showed nominally significant evidence for linkage in FUSION 1, four (on chromosomes 6, 11, 14, and X) also showed evidence for linkage in FUSION 2 and stronger evidence for linkage in the combined FUSION 1 + 2 sample.

Polymicrogyria is a cerebral cortical malformation that is grossly characterized by excessive cortical folding and microscopically characterized by abnormal cortical layering. Although polymicrogyria appears to have one or more genetic causes, no polymicrogyria loci have been identified. Here we describe the clinical and radiographic features of a new genetic form of polymicrogyria and localize the responsible gene. We studied two consanguineous Palestinian pedigrees with an autosomal recessive form of bilateral frontoparietal polymicrogyria (BFPP), using linkage analysis. Five affected children had moderate-to-severe mental retardation, developmental delay, and esotropia, and four of the five affected children developed seizures. Brain magnetic-resonance imaging revealed polymicrogyria that was most prominent in the frontal and parietal lobes but involved other cortical areas as well. A genomewide linkage screen revealed a single locus that was identical by descent in affected children in both families and showed a single disease-associated haplotype, suggesting a common founder mutation. The locus for BFPP maps to chromosome 16q12.2-21, with a minimal interval of 17 cM. For D16S514, the maximal pooled two-point LOD score was 3.98, and the maximal multipoint LOD score was 4.57. This study provides the first genetic evidence that BFPP is an autosomal recessive disorder and serves as a starting point for the identification of the responsible gene.

In March 2013 the American College of Medical Genetics and Genomics published a list of 56 genes with the recommendation that pathogenic and likely pathogenic variants detected incidentally by clinical sequencing be reported to patients. As an initial step in determining the practical consequences of this recommendation in the research setting, we searched for variants in these genes in 232 whole-exome sequences from the Baylor-Hopkins Center for Mendelian Genomics.We identified rare, nonsynonymous, and splicing single-nucleotide variants and insertions/deletions and assessed variant classification using the Human Gene Mutation, Emory, and ClinVar databases. We analyzed the burden of mutation in each of the 56 genes and determined which variants should be reported to patients.Our filtering resulted in 249 distinct variants, with a mean of 1.69 variants per individual. Half of these were novel missense mutations not classified by any of the three reference databases. Of 101 variants listed in the Human Gene Mutation Database, 48 were also in ClinVar and 3 were also in Emory; half of these shared variants were classified discordantly between databases. Some genes consistently had greater variation than others. In total, 0.86% of individuals had a reportable incidental variant.These observations demonstrate some current challenges of assessing phenotypic consequences of incidental variants for counseling patients.

Background: B vitamins play an important role in homocysteine metabolism, with vitamin deficiencies resulting in increased levels of homocysteine and increased risk for stroke. We performed a genome-wide association study (GWAS) in 2,100 stroke patients from the Vitamin Intervention for Stroke Prevention (VISP) trial, a clinical trial designed to determine whether the daily intake of high-dose folic acid, vitamins B6, and B12 reduce recurrent cerebral infarction. Methods: Extensive quality control (QC) measures resulted in a total of 737,081 SNPs for analysis. Genome-wide association analyses for baseline quantitative measures of folate, Vitamins B12, and B6 were completed using linear regression approaches, implemented in PLINK. Results: Six associations met or exceeded genome-wide significance (P ≤ 5 × 10−08). For baseline Vitamin B12, the strongest association was observed with a non-synonymous SNP (nsSNP) located in the CUBN gene (P = 1.76 × 10−13). Two additional CUBN intronic SNPs demonstrated strong associations with B12 (P = 2.92 × 10−10 and 4.11 × 10−10), while a second nsSNP, located in the TCN1 gene, also reached genome-wide significance (P = 5.14 × 10−11). For baseline measures of Vitamin B6, we identified genome-wide significant associations for SNPs at the ALPL locus (rs1697421; P = 7.06 × 10−10 and rs1780316; P = 2.25 × 10−08). In addition to the six genome-wide significant associations, nine SNPs (two for Vitamin B6, six for Vitamin B12, and one for folate measures) provided suggestive evidence for association (P ≤ 10−07). Conclusion: Our GWAS study has identified six genome-wide significant associations, nine suggestive associations, and successfully replicated 5 of 16 SNPs previously reported to be associated with measures of B vitamins. The six genome-wide significant associations are located in gene regions that have shown previous associations with measures of B vitamins; however, four of the nine suggestive associations represent novel finding and warrant further investigation in additional populations.

Idiopathic scoliosis is a highly prevalent disorder, familial in nature, with marked clinical variability. The purpose of this study was to characterize idiopathic scoliosis in a large series of families to be used for a genome-wide search. One hundred thirty-one multigenerational families (892 individuals) with at least two affected individuals were studied. Data obtained included curve pattern, treatment, and back pain. Maximum curvature as a continuous variable was evaluated using t tests for dichotomous characteristics and linear correlation for continuous variables. An analysis of familial loading was done. Four hundred forty-four individuals were classified as affected (82% female; 18% male). The right thoracic and left lumbar curves had the highest mean curvature (49 degrees). Mean curve size was greater in individuals with back pain. Back pain was most prevalent in the right thoracic and left lumbar curve pattern. The Pearson correlation coefficient between the number of affected family members and the maximum degree of curvature was 0.16, suggesting that the greater the lateral curvature, the higher the proportion of family members affected with scoliosis. The sample population is consistent with those of previous studies in relation to gender and curve size. Statistically, the familial nature of this disorder is supported.

Acrodermatitis enteropathica (AE) is a rare autosomal recessive pediatric disease characterized by dermatitis, diarrhea, alopecia, and growth failure. The disease results from insufficient uptake of zinc by the intestine and can be fatal unless the diet is supplemented with zinc. To map the gene responsible for AE, a genomewide screen was performed on 17 individuals, including 4 affected individuals, in a consanguineous Jordanian family. Three markers-D8S373, D10S212, and D6S1021-had a pattern consistent with tight linkage to a recessive disease: one allele in the affected sibs and multiple alleles in unaffected sibs and parents. Two-point parametric linkage analysis using FASTLINK identified one region, D8S373, with a maximum LOD score1.5 (1.94 at D8S373: recombination fraction.001). Twelve additional markers flanking D8S373 were used to genotype the extended family, to fine-map the AE gene. All five affected individuals-including one who was not genotyped in the genomewide screen-were found to be homozygous for a common haplotype, spanning approximately 3.5 cM, defined by markers D8S1713 and D8S2334 on chromosomal region 8q24.3. To support these mapping data, seven consanguineous Egyptian families with eight patients with AE were genotyped using these markers, and six patients from five families were found to be homozygous in this region. Multipoint analysis with all consanguineous families, by Mapmaker/Homoz, resulted in a maximum LOD score of 3.89 between D8S1713 and D8S373. Sliding three-point analysis resulted in a maximum LOD score of 5.16 between markers D8S1727 and D8S1744.

A novel method for joint detection of association caused by linkage disequilibrium (LD) and estimation of both recombination fraction and linkage disequilibrium parameters was compared to several existing implementations of the transmission/disequilibrium test (TDT) and modifications of the TDT in the simulated genetic isolate data from Genetic Analysis Workshop 12. The first completely genotyped trio of affected child and parents was selected from each family in each replicate so that the TDT tests are valid tests of linkage and association, rather than being only valid as tests for linkage. In general, power to detect LD using the genome-wide scan markers was inadequate in the individual replicate samples, but the power was better when analyzing several SNP markers in candidate gene 1.

The effect of inclusion of environmental risk factors on the power of sib-pair linkage methods was tested for a qualitative trait. It was found that inclusion of an environmental variable did not increase the power of the Haseman-Elston (H-E) sib-pair nonparametric linkage analysis test. However, a significant increase in power was observed for both the H-E and affected-sib-pair tests, even in small samples, when persons unexposed to the environmental risk factor were coded as unknown.

Multipoint linkage analysis was used to screen for evidence of linkage between alcoholism and five alcoholism-related quantitative traits. The results suggest that a susceptibility locus that influences monoamine oxidase activity and P300 amplitude at the Pz lead, and increases the risk of alcohol dependence may be linked to markers in the 12q24 region. Furthermore, the susceptibility for alcoholism may be associated with allele 3 (allele size 144) of D12S392.

Susceptibility genes on chromosome 18, and a parent-of-origin effect, have been suggested for bipolar disorder. We analyzed BP data sets provided by the National Institutes of Mental Health and Johns Hopkins University to the Genetic Analysis Workshop 10. Numerous statistical methods were applied to detect association, linkage, and parent-of-origin effects, under alternative diagnostic models. The methods were subsequently compared. Our results suggest that the evidence of linkage of BP to chromosome 18 markers tends to increase, often substantially, as the classification of "affected" individuals broadens. Additionally, a parent-of-origin effect was detected in some of the families.

The statistical properties of sib-pair and variance-components linkage methods were compared using the nuclear family data from Problem 2. Overall, the power to detect linkage was not high for either method. The variance-components method had better power for detection of linkage, particularly when covariates were included in the model. Type I error rates were similar to nominal error rates for both methods.

With white blood cell count emerging as an important risk factor for chronic inflammatory diseases, genetic associations of differential leukocyte types, specifically monocyte count, are providing novel candidate genes and pathways to further investigate. Circulating monocytes play a critical role in vascular diseases such as in the formation of atherosclerotic plaque. We performed a joint and ancestry-stratified genome-wide association analyses to identify variants specifically associated with monocyte count in 11 014 subjects in the electronic Medical Records and Genomics Network. In the joint and European ancestry samples, we identified novel associations in the chromosome 16 interferon regulatory factor 8 (IRF8) gene (P-value = 2.78×10(−16), β = −0.22). Other monocyte associations include novel missense variants in the chemokine-binding protein 2 (CCBP2) gene (P-value = 1.88×10(−7), β = 0.30) and a region of replication found in ribophorin I (RPN1) (P-value = 2.63×10(−16), β = −0.23) on chromosome 3. The CCBP2 and RPN1 region is located near GATA binding protein2 gene that has been previously shown to be associated with coronary heart disease. On chromosome 9, we found a novel association in the prostaglandin reductase 1 gene (P-value = 2.29×10(−7), β = 0.16), which is downstream from lysophosphatidic acid receptor 1. This region has previously been shown to be associated with monocyte count. We also replicated monocyte associations of genome-wide significance (P-value = 5.68×10(−17), β = −0.23) at the integrin, alpha 4 gene on chromosome 2. The novel IRF8 results and further replications provide supporting evidence of genetic regions associated with monocyte count.