Statistical Methods for Recovering GWAS Data

Genome-wide association studies (GWAS) are used to investigate associations between genetic variants and health and disease. GWAS often use a “chip” to genotype single nucleotide polymorphisms (SNPs) spanning the genome and test for association between genotype and phenotype at each SNP. The topic of this dissertation is methods for recovering some of the markers that are typically discarded or not analyzed in a GWAS. During data cleaning, prior to the statistical analysis, many genetic markers are discarded because they fail to meet standard quality control criteria. In addition, some analysis results are filtered out because they are considered unreliable. However, there are some discarded data that could be recovered and used in the analysis. For instance, markers that fail to meet a cutoff p-value for the Hardy-Weinberg equilibrium (HWE) test are typically discarded, as are markers with minor allele frequency below some arbitrary cutoff. In addition, markers on the X-chromosome are often not analyzed because sex chromosome analyses are not as straightforward as autosomal analyses, and the statistical methods for testing association on X-chromosome markers are not well established or well tested. In order to make use of more information from any given GWAS, the standard quality control criteria could be modified and more flexible and data specific analysis methods could be developed. This should have the potential to increase power. Genetic variation and environmental/behavioral factors interact to cause almost all human diseases with great public health significance. Refinements in data analysis will help improve the process of identifying the genetic factors and their interactions. These are important in order to provide better prevention and treatment for human diseases so to maintain public health. The methods in this dissertation are proposed and investigated to help with providing a better public health

Inter-Observer Variation in the Pathologic Identification of Minimal Extrathyroidal Extension in Papillary Thyroid Carcinoma

Background: Extrathyroidal extension (ETE) is a significant prognostic factor in papillary thyroid carcinoma (PTC). Minimal extrathyroidal extension (mETE) is characterized by involvement of the sternothyroid muscle or perithyroid soft tissue, and is generally identified by light microscope examination. Patients with mETE, identified pathologically, are automatically upstaged to pT3. However, the prognostic implications of mETE have been a source of controversy in the literature. Moreover, there is also controversy surrounding the identification of mETE on pathological specimens. The objective of this study was to determine the level of agreement among expert pathologists in the identification of mETE in PTC cases. Methods: Eleven expert pathologists from the United States, Italy, and Canada were asked to perform a review of 69 scanned slides of representative permanent sections of PTC specimens. Each slide was evaluated for the presence of mETE. The pathologists were also asked to list the criteria they use to identify mETE. Results: The overall strength of agreement for identifying mETE was slight (??=?0.14). Inter-pathologist agreement was best for perithyroidal skeletal muscle involvement (??=?0.46, moderate agreement) and worst for invasion around thick-walled vascular structures (??=?0.02, slight agreement). In addition, there was disagreement over the constellation of histologic features that are diagnostic for mETE, which affected overall agreement for diagnosing mETE. Conclusions: Overall agreement for the identification of mETE is poor. Disagreement is a result of both variation in individual pathologists' interpretations of specimens and disagreement on the histologic criteria for mETE. Thus, the utility of mETE in staging and treatment of PTC is brought into question. The lack of concordance may explain the apparent lack of agreement regarding the prognostic significance of this pathologic feature.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140271/1/thy.2015.0508.pd

Inter-Observer Variation in the Pathologic Identification of Extranodal Extension in Nodal Metastasis from Papillary Thyroid Carcinoma

Background: Extranodal extension (ENE) in lymph node metastases has been shown to worsen the prognosis of papillary thyroid cancer (PTC). Despite the clinical significance of ENE, there are no stringent criteria for its microscopic diagnosis, and its identification is subject to inter-observer variability. The objective of this study was to determine the level of agreement among expert pathologists in the identification of ENE in PTC cases. Methods: Eleven expert pathologists from the United States, Italy, and Canada were asked to review 61 scanned slides of representative permanent sections of PTC specimens from Mount Sinai Beth Israel Medical Center in New York. Each slide was evaluated for the presence of ENE. The pathologists were also asked to report the criteria they use to identify ENE. Results: The overall strength of agreement in identifying ENE was only fair (??=?0.35), and the proportion of observed agreement was 0.68. The proportions of observed agreement for the identification of perinodal structures (fat, nerve, skeletal, and thick-walled vessel involvement) ranged from 0.61 to 0.997. Conclusions: Overall agreement for the identification of ENE is poor. The lack of agreement results from both variation in pathologists' identification of features and disagreement on the histologic criteria for ENE. This lack of concordance may help explain some of the discordant information regarding prognosis in clinical studies when this feature is identified.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140272/1/thy.2015.0551.pd

An Early Biomarker Algorithm Predicts Lethal Graft-Vs-Host Disease and Survival after Allogeneic Hematopoietic Cell Transplantation

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Biomarkers Predict Graft-Vs-Host Disease Outcomes Better Than Clinical Response after One Week of Treatment

Abstract Graft-versus-host disease (GVHD), the primary cause of non-relapse mortality (NRM) following allogeneic hematopoietic stem cell transplantation, does not always respond to treatment with high dose systemic corticosteroids. We have recently shown that a combination of three biomarkers (TNFR1, ST2, and REG3α) measured at onset of GVHD can predict day 28 response to treatment and 6-month NRM (Levine, Lancet Haem, 2015). Our goal in the current study was to determine if the same biomarker-based Ann Arbor GVHD algorithm can alsopredict treatment response andmortality whenapplied after one week of systemic corticosteroid treatment. The study population consisted of 378 patients (pts) with acute GVHD from 11 centers in the Mount Sinai Acute GVHD International Consortium. All pts were treated with systemic steroids and provided a plasma or serum sample obtained after one week of treatment (±3 days). The median starting dose of systemic steroids for Grade II-IV GVHD was 2.0 mg/kg/day and for Grade I was 1.0 mg/kg/day, after which treatment varied. Patients were divided into test (n=236) and validation (n=142) cohorts. We applied the Ann Arbor GVHD algorithm to concentrations of TNFR1, ST2, and REG3α measured after one week of treatment to generate a predicted probability of 6-month NRM, which we term the treatment score (TS). We employed unsupervised k-medoidclustering to partition TS values from the test cohort into two groups (high and low). This unbiased approach identified a high score group made up of 25% of pts (n=58) in the test cohort. We observed that the day 28 response rate (complete, CR + partial, PR) was significantly lower in pts with high scores compared to low scores in the test cohort (24% vs 65%, p<0.0001) (Fig 1A). Analysis of the validation cohort using the same TS definitions showed similar differences in response rates (22% vs 61%, p<0.0001) (Fig 1B). Further, nearly four times as many pts with high scores in both cohorts died within 6 months from non-relapse causes compared to pts with low scores (test: 57% vs 17%, p<0.0001; validation: 57% vs 14%, p<0.0001) (Fig 1C/D). As expected, the majority of non-relapse deaths in pts treated for GVHD were directly attributable to GVHD (test: 95%; validation: 89%). Relapse rates for high and low score pts were similar (data not shown), and thus pts with a high TS experienced significantly worse overall survival in both cohorts (test: 37% vs 72%, p<0.0001; validation: 38% vs 79%, p<0.0001) (Fig 1E/F). Approximately half of the pts in each cohort (test: 48%; validation: 44%) responded (CR+PR) to the first week of steroids and these ptshad significantly lower 6-month NRM than non-responders (NR) (test: 17% vs 36%, p=0.0002; validation: 13% vs 36%, p=0.0014). Yet the TS continued to stratify mortality risk independently of clinical response. In the test cohort, pts with a high score comprised 16% of all early responders and experienced more than twice the NRM of early responders with a low score (33% vs 13%, p=0.022) (Fig 2A). Conversely, test cohort pts who did not respond by day 7, but had a low score, fared much better than non-responders with a high score (NRM 21% vs 68%, p<0.0001) (Fig 2B). Two thirds of early non-responders comprised this more favorable group. These highly significant results reproduced in the independent validation cohort in similar proportions (CR+PR: 45% vs 6%, p=0.0003; NR: 61% vs 22%, p=0.0001) (Fig 2C/D). Finally, a subset analysis revealed that pts classified as NR after one week of steroids due to isolated, yet persistent, grade I skin GVHD (24/378, 6%) overwhelmingly had low treatment scores (22/24, 92%) and experienced rates of NRM (9%) comparable to responders with low scores, thus forming a distinct, albeit small, subset of pts with non-responsive GVHD that fares particularly well (Fig 3). In conclusion, a treatment score based on three GVHD biomarkers measured after one week of steroids stratifies pts into two groups with distinct risks for treatment failure and 6-month NRM. It is particularly noteworthy that the TS identifies two subsets of pts with steroid refractory (SR) GVHD who have highly different outcomes (Fig 2B/D). The much larger group, approximately two thirds of all SR pts, may not need the same degree of treatment escalation as is traditional for clinical non-response, and thus overtreatment might be avoided. Because the TSis measured at a common decision making time point, it may prove useful to guide risk-adapted therapy. Disclosures Mielke: Novartis: Consultancy; MSD: Consultancy, Other: Travel grants; Celgene: Other: Travel grants, Speakers Bureau; Gilead: Other: Travel grants; JAZZ Pharma: Speakers Bureau. Kroeger:Novartis: Honoraria, Research Funding. Chen:Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding. Jagasia:Therakos: Consultancy. Kitko:Therakos: Honoraria, Speakers Bureau. Ferrara:Viracor: Patents & Royalties: GVHD biomarker patent. Levine:Viracor: Patents & Royalties: GVHD biomarker patent

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

We propose a novel conditional graphical model - spaceMap - to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations (CNAs) on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network. spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast (n=77) and ovarian (n=174) cancer studies. Each cancer exhibits disruption of 'ion transmembrane transport' and 'regulation from RNA polymerase II promoter' by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers. The R package spaceMap - including vignettes and documentation - is hosted on https://topherconley.github.io/spacemap

Combined association of Presenilin-1 and Apolipoprotein E polymorphisms with maternal meiosis II error in Down syndrome births

Abstract Alzheimer's disease and Down syndrome often exhibit close association and predictively share common genetic risk-factors. Presenilin-1 (PSEN-1) and Apolipoprotein E (APOE) genes are associated with early and late onset of Alzheimer's disease, respectively. Presenilin −1 is involved in faithful chromosomal segregation. A higher frequency of the APOE ε4 allele has been reported among young mothers giving birth to Down syndrome children. In this study, 170 Down syndrome patients, grouped according to maternal meiotic stage of nondisjunction and maternal age at conception, and their parents were genotyped for PSEN-1 intron-8 and APOE polymorphisms. The control group consisted of 186 mothers of karyotypically normal children. The frequencies of the PSEN-1 T allele and TT genotype, in the presence of the APOE ε4 allele, were significantly higher among young mothers (< 35 years) with meiosis II nondisjunction than in young control mothers (96.43% vs. 65.91% P = 0.0002 and 92.86% vs. 45.45% P < 0.0001 respectively) but not among mothers with meiosis I nondisjunction. We infer that the co-occurrence of the PSEN-1 T allele and the APOE ε4 allele associatively increases the risk of meiotic segregation error II among young women