Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality.

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 reuse 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-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 14 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 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 and marker quality and various batch effects. Upon completion of the genotyping and QC analyses for each site's primary study, eMERGE Coordinating Center merged the datasets from all five sites. This larger merged dataset reentered 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 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 datasets accessible through the National Center for Biotechnology Information in the database of Genotypes and Phenotypes. 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.

Evaluating the influence of quality control decisions and software algorithms on SNP calling for the affymetrix 6.0 SNP array platform.

OBJECTIVE: Our goal was to evaluate the influence of quality control (QC) decisions using two genotype calling algorithms, CRLMM and Birdseed, designed for the Affymetrix SNP Array 6.0. METHODS: Various QC options were tried using the two algorithms and comparisons were made on subject and call rate and on association results using two data sets. RESULTS: For Birdseed, we recommend using the contrast QC instead of QC call rate for sample QC. For CRLMM, we recommend using the signal-to-noise rate ≥4 for sample QC and a posterior probability of 90% for genotype accuracy. For both algorithms, we recommend calling the genotype separately for each plate, and dropping SNPs with a lower call rate (<95%) before evaluating samples with lower call rates. To investigate whether the genotype calls from the two algorithms impacted the genome-wide association results, we performed association analysis using data from the GENOA cohort; we observed that the number of significant SNPs were similar using either CRLMM or Birdseed. CONCLUSIONS: Using our suggested workflow both algorithms performed similarly; however, fewer samples were removed and CRLMM took half the time to run our 854 study samples (4.2 h) compared to Birdseed (8.4 h).

Leukocyte DNA methylation signature differentiates pancreatic cancer patients from healthy controls.

Pancreatic adenocarcinoma (PaC) is one of most difficult tumors to treat. Much of this is attributed to the late diagnosis. To identify biomarkers for early detection, we examined DNA methylation differences in leukocyte DNA between PaC cases and controls in a two-phase study. In phase I, we measured methylation levels at 1,505 CpG sites in treatment-naïve leukocyte DNA from 132 never-smoker PaC patients and 60 never-smoker healthy controls. We found significant differences in 110 CpG sites (false discovery rate <0.05). In phase II, we tested and validated 88 of 96 phase I selected CpG sites in 240 PaC cases and 240 matched controls (p≤0.05). Using penalized logistic regression, we built a prediction model consisting of five CpG sites (IL10_P348, LCN2_P86, ZAP70_P220, AIM2_P624, TAL1_P817) that discriminated PaC patients from controls (C-statistic = 0.85 in phase I; 0.76 in phase II). Interestingly, one CpG site (LCN2_P86) alone could discriminate resectable patients from controls (C-statistic= 0.78 in phase I; 0.74 in phase II). We also performed methylation quantitative trait loci (methQTL) analysis and identified three CpG sites (AGXT_P180_F, ALOX12_E85_R, JAK3_P1075_R) where the methylation levels were significantly associated with single nucleotide polymorphisms (SNPs) (false discovery rate <0.05). Our results demonstrate that epigenetic variation in easily obtainable leukocyte DNA, manifested by reproducible methylation differences, may be used to detect PaC patients. The methylation differences at certain CpG sites are partially attributable to genetic variation. This study strongly supports future epigenome-wide association study using leukocyte DNA for biomarker discovery in human diseases.

Homozygosity mapping and exome sequencing reveal GATAD1 mutation in autosomal recessive dilated cardiomyopathy.

BACKGROUND: Dilated cardiomyopathy (DCM) is a heritable, genetically heterogeneous disorder that typically exhibits autosomal dominant inheritance. Genomic strategies enable discovery of novel, unsuspected molecular underpinnings of familial DCM. We performed genome-wide mapping and exome sequencing in a unique family wherein DCM segregated as an autosomal recessive (AR) trait. METHODS AND RESULTS: Echocardiography in 17 adult descendants of first cousins revealed DCM in 2 female siblings and idiopathic left ventricular enlargement in their brother. Genotyping and linkage analysis mapped an AR DCM locus to chromosome arm 7q21, which was validated and refined by high-density homozygosity mapping. Exome sequencing of the affected sisters was then used as a complementary strategy for mutation discovery. An iterative bioinformatics process was used to filter >40,000 genetic variants, revealing a single shared homozygous missense mutation localized to the 7q21 critical region. The mutation, absent in HapMap, 1000 Genomes, and 474 ethnically matched controls, altered a conserved residue of GATAD1, encoding GATA zinc finger domain-containing protein 1. Thirteen relatives were heterozygous mutation carriers with no evidence of myocardial disease, even at advanced ages. Immunohistochemistry demonstrated nuclear localization of GATAD1 in left ventricular myocytes, yet subcellular expression and nuclear morphology were aberrant in the proband. CONCLUSIONS: Linkage analysis and exome sequencing were used as synergistic genomic strategies to identify GATAD1 as a gene for AR DCM. GATAD1 binds to a histone modification site that regulates gene expression. Consistent with murine DCM caused by genetic disruption of histone deacetylases, the data implicate an inherited basis for epigenetic dysregulation in human heart failure.

Obesity adversely affects survival in pancreatic cancer patients.

BACKGROUND: Higher body-mass index (BMI) has been implicated as a risk factor for developing pancreatic cancer, but its effect on survival has not been thoroughly investigated. The authors assessed the association of BMI with survival in a sample of pancreatic cancer patients and used epidemiologic and clinical information to understand the contribution of diabetes and hyperglycemia. METHODS: A survival analysis using Cox proportional hazards by usual adult BMI was performed on 1861 unselected patients with pancreatic adenocarcinoma; analyses were adjusted for covariates that included clinical stage, age, and sex. Secondary analyses incorporated self-reported diabetes and fasting blood glucose in the survival model. RESULTS: BMI as a continuous variable was inversely associated with survival from pancreatic adenocarcinoma (hazard ratio [HR], 1.019 for each increased unit of BMI [kg/m2], P<.001) after adjustment for age, stage, and sex. In analysis by National Institutes of Health BMI category, BMIs of 30 to 34.99 kg/m2 (HR, 1.14; 95% confidence interval [CI], 0.98-1.33), 35 to 39.99 kg/m2 (HR 1.32, 95% CI 1.08-1.62), and ≥40 (HR 1.60, 95% CI 1.26-2.04) were associated with decreased survival compared with normal BMI of 18.5 to 24.99 kg/m2 (overall trend test P<.001). Fasting blood glucose and diabetes did not affect the results. CONCLUSIONS: Higher BMI is associated with decreased survival in pancreatic cancer. Although the mechanism of this association remains undetermined, diabetes and hyperglycemia do not appear to account for the observed association.

Polymorphic variants in hereditary pancreatic cancer genes are not associated with pancreatic cancer risk.

BACKGROUND: Inherited risk of pancreatic cancer has been associated with mutations in several genes, including BRCA2, CDKN2A (p16), PRSS1, and PALB2. We hypothesized that common variants in these genes, single nucleotide polymorphisms (SNP), may also influence risk for pancreatic cancer development. METHODS: A clinic-based case-control study in non-Hispanic white persons compared 1,143 patients with pancreatic adenocarcinoma with 1,097 healthy controls. Twenty-eight genes directly and indirectly involved in the Fanconi/BRCA pathway (includes BRCA1, BRCA2, and PALB2) were identified and 248 tag SNPs were selected. In addition, 11 SNPs in CDKN2A, PRSS1, and PRSS2 were selected. Association studies were done at the gene level by principal components analysis, whereas recursive partitioning analysis was used to investigate pathway effects. At the individual SNP level, adjusted additive, dominant, and recessive models were investigated, and gene-environment interactions were also assessed. RESULTS: Gene level analyses showed no significant association of any genes with altered pancreatic cancer risk. Multiple single SNP analyses showed associations, which will require replication. Exploratory pathway analyses by recursive partitioning showed no association between SNPs and risk for pancreatic cancer. CONCLUSION: In a candidate gene and pathway SNP association study analysis, common variations in the Fanconi/BRCA pathway and other candidate familial pancreatic cancer genes are not associated with risk for pancreatic cancer.