New methods for studying complex diseases via genetic association studies

Genome-wide association studies (GWAS) have delivered many novel insights about the etiology of many common heritable diseases. However, in most disorders studied by GWAS, the known single nucleotide polymorphisms (SNPs) associated with the disease do not account for a large portion of the genetic factors underlying the condition. This suggests that many of the undiscovered variants contributing to the risk of common diseases have weak effects or are relatively rare. This thesis introduces novel adaptations of techniques for improving detection power for both of these types of risk variants, and reports the results of analyses applying these methods to real datasets for common diseases. Chapter 2 describes a novel approach to improve the detection of weak-effect risk variants that is based on an adaptive sampling technique known as Distilled Sensing (DS). This procedure entails utilization of a portion of the total sample to exclude from consideration regions of the genome where there is no evidence of genetic association, and then testing for association with a greatly reduced number of variants in the remaining sample. Application of the method to simulated data sets and GWAS data from studies of age-related macular degeneration (AMD) demonstrated that, in many situations, DS can have superior power over traditional meta-analysis techniques to detect weak-effect loci. Chapter 3 describes an innovative pipeline to screen for rare variants in next generation sequencing (NGS) data. Since rare variants, by definition, are likely to be present in only a few individuals even in large samples, efficient methods to screen for rare causal variants are critical for advancing the utility of NGS technology. Application of our approach, which uses family-based data to identify candidate rare variants that could explain aggregation of disease in some pedigrees, resulted in the discovery of novel protein-coding variants linked to increased risk for Alzheimer's disease (AD) in African Americans. The techniques presented in this thesis address different aspects of the "missing heritability" problem and offer efficient approaches to discover novel risk variants, and thereby facilitate development of a more complete picture of genetic risk for common diseases

Omega-3 fatty acids and genome-wide interaction analyses reveal DPP10-pulmonary function association

Rationale: Omega-3 polyunsaturated fatty acids (n-3 PUFAs) have anti-inflammatory properties that could benefit adults with comprised pulmonary health. Objective: To investigate n-3 PUFA associations with spirometric measures of pulmonary function tests (PFTs) and determine underlying genetic susceptibility. Methods: Associations of n-3 PUFA biomarkers (a-linolenic acid, eicosapentaenoic acid, docosapentaenoic acid [DPA], and docosahexaenoic acid [DHA]) were evaluated with PFTs (FEV1, FVC, and FEV1/FVC) in meta-analyses across seven cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (N=16,134 of European or African ancestry). PFT-associated n-3 PUFAs were carried forward to genome-wide interaction analyses in the four largest cohorts (N=11,962) and replicated in one cohort (N=1,687). Cohort-specific results were combined using joint 2 degree-of-freedom (2df) meta-analyses of SNPassociations and their interactions with n-3PUFAs. Results: DPA and DHA were positively associated with FEV1 and FVC (P < 0.025), with evidence for effect modification by smoking and by sex. Genome-wide analyses identified a novel association of rs11693320-an intronic DPP10 SNP-with FVC when incorporating an interaction with DHA, and the finding was replicated (P-2df = 9.4 x 10(-9) across discovery and replication cohorts). The rs11693320-A allele (frequency, similar to 80%) was associated with lower FVC (P-SNP = 2.1 x 10(-9); beta(SNP) = 2161.0 ml), and the association was attenuated by higher DHA levels (P-SNPxDHA interaction = 2.1x10(-7); beta(SNPxDHA interaction) = 36.2 ml). Conclusions: We corroborated beneficial effects of n-3 PUFAs on pulmonary function. By modeling genome-wide n-3 PUFA interactions, we identified a novel DPP10 SNP association with FVC that was not detectable in much larger studies ignoring this interaction

A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants.

This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ng.3448Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. Here we report on a study of >12 million variants, including 163,714 directly genotyped, mostly rare, protein-altering variants. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5 × 10(-8)) distributed across 34 loci. Although wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first genetic association signal specific to wet AMD, near MMP9 (difference P value = 4.1 × 10(-10)). Very rare coding variants (frequency <0.1%) in CFH, CFI and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.We thank all participants of all the studies included for enabling this research by their participation in these studies. Computer resources for this project have been provided by the high-performance computing centers of the University of Michigan and the University of Regensburg. Group-specific acknowledgments can be found in the Supplementary Note. The Center for Inherited Diseases Research (CIDR) Program contract number is HHSN268201200008I. This and the main consortium work were predominantly funded by 1X01HG006934-01 to G.R.A. and R01 EY022310 to J.L.H

Predicting Irregularities in Population Cycles

Oscillating population data often exhibit cycle irregularities such as episodes of damped oscillation and abrupt changes of cycle phase. The prediction of such irregularities is of interest in applications ranging from food production to wildlife management. We use concepts from dynamical systems theory to present a model-based method for quantifying the risk of impending cycle irregularity

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

<div><p>Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.</p></div

A comprehensive genetic association study of Alzheimer disease in African Americans.

OBJECTIVES: To evaluate the association of genetic variation with late-onset Alzheimer disease (AD) in African Americans, including genes implicated in recent genome-wide association studies of whites. DESIGN: We analyzed a genome-wide set of 2.5 million imputed markers to evaluate the genetic basis of AD in an African American population. SUBJECTS: Five hundred thirteen well-characterized African American AD cases and 496 cognitively normal African American control subjects. SETTING: Data were collected from multiple sites as part of the Multi-Institutional Research on Alzheimer Genetic Epidemiology (MIRAGE) Study and the Henry Ford Health System as part of the Genetic and Environmental Risk Factors for Alzheimer Disease Among African Americans (GenerAAtions) Study. RESULTS: Several significant single-nucleotide polymorphisms (SNPs) were observed in the region of the apolipoprotein E gene (APOE). After adjusting for the confounding effects of APOE genotype, one of these SNPs, rs6859 in PVRL2, remained significantly associated with AD (P = .0087). Association was also observed with SNPs in CLU, PICALM, BIN1, EPHA1, MS4A, ABCA7, and CD33, although the effect direction for some SNPs and the most significant SNPs differed from findings in data sets consisting of whites. Finally, using the African American genome-wide association study data set as a discovery sample, we obtained suggestive evidence of association with SNPs for several novel candidate genes. CONCLUSIONS: Some genes contribute to AD pathogenesis in both white and African American cohorts, although it is unclear whether the causal variants are the same. A larger African American sample will be needed to confirm novel gene associations, which may be population specific

Survival analysis on biomarker identified treatment sensitive/resistant sub-groups.

<p>A. Using the Erlotinib model to stratify Erlotinib treated patients; B. Using Sorafenib model to stratify Sorafenib treated patients; C. Using Erlotinib model to stratify Sorafenib treated patients; and D. Using Sorafenib model to stratify Erlotinib treated patients.</p

PLSR modeling workflow applied on 183 cancer cell lines on OncoPanel.

<p>(A). Flow chart on the model building and testing steps. (B). A specially designed splitting strategy divides the training dataset into random training, random validation and balance validation subsets. (C). Representative example of random validation and balance validation. Red points were top performing models on 1000 random splits on this balanced split, based on both AUC and correlation measures. (D). AUC and correlation cutoff selection for the core PLSR model.</p

Causal network to depict functional relations between sensitivity-specific and resistance-specific signature genes.

<p>The network was reconstructed from canonical signaling pathways regulated by signature genes and a signature specific direct interaction network. Sensitivity-specific signature genes are highlighted with blue thermometers, resistance-specific genes with red thermometers.</p

Predicted percentage of Erlotinib and Sorafenib sensitive samples for some cancer indications from Gene Expression Omnibus datasets.

<p>The predictive models were derived from cell line Oncopanel expression data. Patient data normalization is described in the result section.</p><p>Predicted percentage of Erlotinib and Sorafenib sensitive samples for some cancer indications from Gene Expression Omnibus datasets.</p