Additional file 9: of Genome-wide association study identifies a major gene for beech bark disease resistance in American beech (Fagus grandifolia Ehrh.)

RNA sequence reads from each cDNA library mapped to the full-length copy of the candidate gene transcript sequence from contig 03321, representing the expression of the candidate Mt gene after the challenge by the insect vector. (DOCX 161 kb

Additional file 1: Figure S1. of A loss of function variant in CASP7 protects against Alzheimer’s disease in homozygous APOE ε4 allele carriers

Relationship between the frequency of rs10553596 in CASP7 and APOE e4 allele frequency. (A) Variant frequencies across the 1000 genome phase 3 ethnic groups for rs10553596 and the APOE ε4 allele, The ethnic groups are color coded by continent/region. (B) The frequency of rs10553596 versus the APOE ε4 allele. Figure S2. Frequency bar chart showing the variant frequencies across all DIVAS disease and control cohorts for rs10553596. The y axis shows the allele frequencies. Blue bars represent healthy cohorts with different ethnicities. Red bars present diseased cohorts. FALS stands for familial Amyotrophic lateral sclerosis. (PDF 134 kb

PheWAS View Plot of Meta-analysis Results with p<0.01 Replicating for the Same ICD-9 Category, Meeting Autoimmune and Immune-Related Diagnosis Criteria.

<p>The left track specifies the phenotype and ICD-9 Category code with which the SNP was associated. The next track indicates–log₁₀(p-value) from the meta-analysis performed on all replicating SNPs with p<0.01. The last track indicates the SNP that had the most significant p-value, and the direction of effect of the association (+, positive; -, negative). The total number of associations between the SNPs and diagnoses was 409.</p

Summary of Data Sets Used for the Study.

<p>Summary of Data Sets Used for the Study.</p

Pleiotropy: SNPs Associated with more than One Phenotype and Replicating across more than One Study for the Same ICD-9 Category.

<p>This chromosomal ideogram has lines indicating the location of the SNP, with filled colored circles indicating different ICD-9 code diagnoses associated with that particular SNP. When there are multiple pairs of the same phenotypes in the same region, this indicates regions where several SNPs in close proximity were associated with the same pairs of phenotypes.</p

Immune-Related ICD-9 categories selected for further analysis.

<p>Immune-Related ICD-9 categories selected for further analysis.</p

Overview of PheWAS with Immune Variants.

<p>This flow chart provides an overview of the steps taken to perform PheWAS between immune variants and ICD-9 diagnosis codes. The final testing dataset (purple) was formed by selecting SNPs from our array data that also exist on Immunochip and/or are within immune-related genes (yellow) and removing samples with missing genotypic or phenotypic data (green). Comprehensive associations were calculated between all final dataset SNPs and ICD-9 code based case/control status using logistic regression, with all models adjusted for age, sex and first five principal components. Replication was sought following both an exact ICD-9 code and a category ICD-9 code approach following the specified criteria. Pooled analysis was performed for both approaches using METAL. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160573#pone.0160573.s001" target="_blank">S1 Fig</a> for the full workflow from imputation through quality control, association testing, and replication for this study.</p

Cytoscape Network Showing the Connections between Phenotypes, the Genes with SNPs, and Pathways.

<p>In this network, green squares represent phenotype; red triangles represent genes; and blue circles are KEGG pathways. The colored lines highlight the link between phenotype and pathway. For the gene <i>HLA-DRA</i> with SNPs associated with “<i>714</i>: <i>rheumatoid arthritis</i>” and “<i>250</i>: <i>type 1 diabetes</i>” is present in the KEGG pathway of “<i>rheumatoid arthritis</i>” (red line) and “<i>type 1 diabetes”</i> (green line) respectively. Also, the blue edge shows the connection between <i>“714</i>: <i>rheumatoid arthritis”</i>, <i>“716</i>: <i>other specified arthropathies”</i> and the KEGG “<i>JAK-STAT signaling pathway</i>” through two interleukin genes, <i>IL23R</i> and <i>IL6</i>.</p

Synthesis view plot showing PheWAS results replicating across MyCode^® and BioVU that have previously reported associations.

<p>The first track is the chromosomal location for each SNP. The next column lists the SNP identifier, the phenotype associated in our study, and the reported GWAS trait (p<10⁻⁵). Results representing exact matches with the NHGRI-EBI GWAS catalog and GRASP are annotated with a single asterisk and the closely related traits are represented with a double asterisk. Blue symbols represent results from MyCode^®, red symbols represent results from BioVU and green symbols are the pooled analysis results obtained using the program METAL.</p

Genome-wide study of resistant hypertension identified from electronic health records

<div><p>Resistant hypertension is defined as high blood pressure that remains above treatment goals in spite of the concurrent use of three antihypertensive agents from different classes. Despite the important health consequences of resistant hypertension, few studies of resistant hypertension have been conducted. To perform a genome-wide association study for resistant hypertension, we defined and identified cases of resistant hypertension and hypertensives with treated, controlled hypertension among >47,500 adults residing in the US linked to electronic health records (EHRs) and genotyped as part of the electronic MEdical Records & GEnomics (eMERGE) Network. Electronic selection logic using billing codes, laboratory values, text queries, and medication records was used to identify resistant hypertension cases and controls at each site, and a total of 3,006 cases of resistant hypertension and 876 controlled hypertensives were identified among eMERGE Phase I and II sites. After imputation and quality control, a total of 2,530,150 SNPs were tested for an association among 2,830 multi-ethnic cases of resistant hypertension and 876 controlled hypertensives. No test of association was genome-wide significant in the full dataset or in the dataset limited to European American cases (n = 1,719) and controls (n = 708). The most significant finding was <i>CLNK</i> rs13144136 at p = 1.00x10^-6 (odds ratio = 0.68; 95% CI = 0.58–0.80) in the full dataset with similar results in the European American only dataset. We also examined whether SNPs known to influence blood pressure or hypertension also influenced resistant hypertension. None was significant after correction for multiple testing. These data highlight both the difficulties and the potential utility of EHR-linked genomic data to study clinically-relevant traits such as resistant hypertension.</p></div