The clinical utility of sequencing the entirety of CFTR.

BACKGROUND: Cystic fibrosis (CF) is caused by deleterious variants in each CFTR gene. We investigated the utility of whole-gene CFTR sequencing when fewer than two pathogenic or likely pathogenic (P/LP) variants were detected by conventional testing (sequencing of exons and flanking introns) of CFTR. METHODS: Individuals with features of CF and a CF-diagnostic sweat chloride concentration with zero or one P/LP variants identified by conventional testing enrolled in the CF Mutation Analysis Program (MAP) underwent whole-gene CFTR sequencing. Replication was performed on individuals enrolled in the CF Genome Project (CFGP), followed by phenotype review and interrogation of other genes. RESULTS: Whole-gene sequencing identified a second P/LP variant in 20/43 MAP enrollees (47 %) and 10/22 CFGP enrollees (45 %) who had one P/LP variant after conventional testing. No P/LP variants were detected when conventional testing was negative (MAP: n = 43; CFGP: n = 13). Genome-wide analysis was unable to find an alternative etiology in CFGP participants with fewer than two P/LP CFTR variants and CF could not be confirmed in 91 % following phenotype re-review. CONCLUSIONS: Whole-gene CFTR analysis is beneficial in individuals with one previously-identified P/LP variant and a CF-diagnostic sweat chloride. Negative conventional CFTR testing indicates that the phenotype should be re-evaluated.

Genetic Modifiers of Cystic Fibrosis Lung Disease Severity: Whole-Genome Analysis of 7,840 Patients.

Rationale: Lung disease is the major cause of morbidity and mortality in persons with cystic fibrosis (pwCF). Variability in CF lung disease has substantial non-CFTR (CF transmembrane conductance regulator) genetic influence. Identification of genetic modifiers has prognostic and therapeutic importance. Objectives: Identify genetic modifier loci and genes/pathways associated with pulmonary disease severity. Methods: Whole-genome sequencing data on 4,248 unique pwCF with pancreatic insufficiency and lung function measures were combined with imputed genotypes from an additional 3,592 patients with pancreatic insufficiency from the United States, Canada, and France. This report describes association of approximately 15.9 million SNPs using the quantitative Kulich normal residual mortality-adjusted (KNoRMA) lung disease phenotype in 7,840 pwCF using premodulator lung function data. Measurements and Main Results: Testing included common and rare SNPs, transcriptome-wide association, gene-level, and pathway analyses. Pathway analyses identified novel associations with genes that have key roles in organ development, and we hypothesize that these genes may relate to dysanapsis and/or variability in lung repair. Results confirmed and extended previous genome-wide association study findings. These whole-genome sequencing data provide finely mapped genetic information to support mechanistic studies. No novel primary associations with common single variants or rare variants were found. Multilocus effects at chr5p13 (SLC9A3/CEP72) and chr11p13 (EHF/APIP) were identified. Variant effect size estimates at associated loci were consistently ordered across the cohorts, indicating possible age or birth cohort effects. Conclusions: This premodulator genomic, transcriptomic, and pathway association study of 7,840 pwCF will facilitate mechanistic and postmodulator genetic studies and the development of novel therapeutics for CF lung disease.

Pleiotropic modifiers of age-related diabetes and neonatal intestinal obstruction in cystic fibrosis.

Individuals with cystic fibrosis (CF) develop complications of the gastrointestinal tract influenced by genetic variants outside of CFTR. Cystic fibrosis-related diabetes (CFRD) is a distinct form of diabetes with a variable age of onset that occurs frequently in individuals with CF, while meconium ileus (MI) is a severe neonatal intestinal obstruction affecting ∼20% of newborns with CF. CFRD and MI are slightly correlated traits with previous evidence of overlap in their genetic architectures. To better understand the genetic commonality between CFRD and MI, we used whole-genome-sequencing data from the CF Genome Project to perform genome-wide association. These analyses revealed variants at 11 loci (6 not previously identified) that associated with MI and at 12 loci (5 not previously identified) that associated with CFRD. Of these, variants at SLC26A9, CEBPB, and PRSS1 associated with both traits; variants at SLC26A9 and CEBPB increased risk for both traits, while variants at PRSS1, the higher-risk alleles for CFRD, conferred lower risk for MI. Furthermore, common and rare variants within the SLC26A9 locus associated with MI only or CFRD only. As expected, different loci modify risk of CFRD and MI; however, a subset exhibit pleiotropic effects indicating etiologic and mechanistic overlap between these two otherwise distinct complications of CF.

Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients.

Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped approximately 8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (approximately 30×) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among patients with CF. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the approximately 8,000 CF samples with GWAS array genotype using the Trans-Omics for Precision Medicine (TOPMed) freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for patients with CF, boosting genomic coverage from approximately 0.3-4.2 million genotyped markers to approximately 11-43 million well-imputed markers, and significantly improving polygenic risk score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of patients with CF. We demonstrate that despite having approximately 3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely owing to allele and haplotype differences between patients with CF and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.

Exome Array Analysis of 9721 Ischemic Stroke Cases from the SiGN Consortium.

Recent genome wide association studies have identified 89 common genetic variants robustly associated with ischemic stroke and primarily located in non-coding regions. To evaluate the contribution of coding variants, which are mostly rare, we performed an exome array analysis on 106,101 SNPs for 9721 ischemic stroke cases from the SiGN Consortium, and 12,345 subjects with no history of stroke from the Health Retirement Study and SiGN consortium. We identified 15 coding variants significantly associated with all ischemic stroke at array-wide threshold (i.e., p < 4.7 × 10-7), including two common SNPs in ABO that have previously been associated with stroke. Twelve of the remaining 13 variants were extremely rare in European Caucasians (MAF < 0.1%) and the associations were driven by African American samples. There was no evidence for replication of these associations in either TOPMed Stroke samples (n = 5613 cases) or UK Biobank (n = 5874 stroke cases), although power to replicate was very low given the low allele frequencies of the associated variants and a shortage of samples from diverse ancestries. Our study highlights the need for acquiring large, well-powered diverse cohorts to study rare variants, and the technical challenges using array-based genotyping technologies for rare variant genotyping.

A Custom Genotyping Array Reveals Population-Level Heterogeneity for the Genetic Risks of Prostate Cancer and Other Cancers in Africa.

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.

Genetic Modifiers of Cystic Fibrosis-Related Diabetes Have Extensive Overlap With Type 2 Diabetes and Related Traits.

CONTEXT: Individuals with cystic fibrosis (CF) develop a distinct form of diabetes characterized by ß-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 ß-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.

ZCCHC8, the nuclear exosome targeting component, is mutated in familial pulmonary fibrosis and is required for telomerase RNA maturation.

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.

Development, Evaluation, and Implementation of a Pan-African Cancer Research Network: Men of African Descent and Carcinoma of the Prostate.

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.

Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma.

Recent technological advancements have permitted high-throughput measurement of the human genome, epigenome, metabolome, transcriptome, and proteome at the population level. We hypothesized that subsets of genes identified from omic studies might have closely related biological functions and thus might interact directly at the network level. Therefore, we conducted an integrative analysis of multi-omic datasets of non-small cell lung cancer (NSCLC) to search for association patterns beyond the genome and transcriptome. A large, complex, and robust gene network containing well-known lung cancer-related genes, including EGFR and TERT, was identified from combined gene lists for lung adenocarcinoma. Members of the hypoxia-inducible factor (HIF) gene family were at the center of this network. Subsequent sequencing of network hub genes within a subset of samples from the Transdisciplinary Research in Cancer of the Lung-International Lung Cancer Consortium (TRICL-ILCCO) consortium revealed a SNP (rs12614710) in EPAS1 associated with NSCLC that reached genome-wide significance (OR = 1.50; 95% CI: 1.31-1.72; p = 7.75 × 10-9). Using imputed data, we found that this SNP remained significant in the entire TRICL-ILCCO consortium (p = .03). Additional functional studies are warranted to better understand interrelationships among genetic polymorphisms, DNA methylation status, and EPAS1 expression.

Homozygous deletion in MYL9 expands the molecular basis of megacystis-microcolon-intestinal hypoperistalsis syndrome.

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.

The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers.

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. Cancer Epidemiol Biomarkers Prev; 26(1); 126-35. ©2016 AACR.

Comparison of parametric and machine methods for variable selection in simulated Genetic Analysis Workshop 19 data.

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.

Filtering genetic variants and placing informative priors based on putative biological function.

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.

Whole-Exome Sequencing in Familial Parkinson Disease.

IMPORTANCE: Parkinson disease (PD) is a progressive neurodegenerative disease for which susceptibility is linked to genetic and environmental risk factors. OBJECTIVE: To identify genetic variants contributing to disease risk in familial PD. DESIGN, SETTING, AND PARTICIPANTS: A 2-stage study design that included a discovery cohort of families with PD and a replication cohort of familial probands was used. In the discovery cohort, rare exonic variants that segregated in multiple affected individuals in a family and were predicted to be conserved or damaging were retained. Genes with retained variants were prioritized if expressed in the brain and located within PD-relevant pathways. Genes in which prioritized variants were observed in at least 4 families were selected as candidate genes for replication in the replication cohort. The setting was among individuals with familial PD enrolled from academic movement disorder specialty clinics across the United States. All participants had a family history of PD. MAIN OUTCOMES AND MEASURES: Identification of genes containing rare, likely deleterious, genetic variants in individuals with familial PD using a 2-stage exome sequencing study design. RESULTS: The 93 individuals from 32 families in the discovery cohort (49.5% [46 of 93] female) had a mean (SD) age at onset of 61.8 (10.0) years. The 49 individuals with familial PD in the replication cohort (32.6% [16 of 49] female) had a mean (SD) age at onset of 50.1 (15.7) years. Discovery cohort recruitment dates were 1999 to 2009, and replication cohort recruitment dates were 2003 to 2014. Data analysis dates were 2011 to 2015. Three genes containing a total of 13 rare and potentially damaging variants were prioritized in the discovery cohort. Two of these genes (TNK2 and TNR) also had rare variants that were predicted to be damaging in the replication cohort. All 9 variants identified in the 2 replicated genes in 12 families across the discovery and replication cohorts were confirmed via Sanger sequencing. CONCLUSIONS AND RELEVANCE: TNK2 and TNR harbored rare, likely deleterious, variants in individuals having familial PD, with similar findings in an independent cohort. To our knowledge, these genes have not been previously associated with PD, although they have been linked to critical neuronal functions. Further studies are required to confirm a potential role for these genes in the pathogenesis of PD.

Preparation and use of varied natural tools for extractive foraging by bonobos (Pan Paniscus).

OBJECTIVES: The tool-assisted extractive foraging capabilities of captive (zoo) and semi-captive (sanctuary) bonobo (Pan paniscus) groups were compared to each other and to those known in wild chimpanzee (Pan troglodytes) cultures. MATERIALS AND METHODS: The bonobos were provided with natural raw materials and challenged with tasks not previously encountered, in experimental settings simulating natural contexts where resources requiring special retrieval efforts were hidden. They were shown that food was buried underground or inserted into long bone cavities, and left to tackle the tasks without further intervention. RESULTS: The bonobos used modified branches and unmodified antlers or stones to dig under rocks and in the ground or to break bones to retrieve the food. Antlers, short sticks, long sticks, and rocks were effectively used as mattocks, daggers, levers, and shovels, respectively. One bonobo successively struck a long bone with an angular hammer stone, completely bisecting it longitudinally. Another bonobo modified long branches into spears and used them as attack weapons and barriers. Bonobos in the sanctuary, unlike those in the zoo, used tool sets to perform sequential actions. DISCUSSION: The competent and diverse tool-assisted extractive foraging by the bonobos corroborates and complements the extensive information on similar tool use by chimpanzees, suggesting that such competence is a shared trait. Better performance by the sanctuary bonobos than the zoo group was probably due to differences in their cultural exposure and housing conditions. The bonobos' foraging techniques resembled some of those attributed to Oldowan hominins, implying that they can serve as referential models.

Lessons learned from whole exome sequencing in multiplex families affected by a complex genetic disorder, intracranial aneurysm.

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.

Assessment of incidental findings in 232 whole-exome sequences from the Baylor-Hopkins Center for Mendelian Genomics.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: These observations demonstrate some current challenges of assessing phenotypic consequences of incidental variants for counseling patients.

Genetic Associations with Plasma B12, B6, and Folate Levels in an Ischemic Stroke Population from the Vitamin Intervention for Stroke Prevention (VISP) Trial.

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.

Imputation and quality control steps for combining multiple genome-wide datasets.

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.