Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

An Excess of Risk-Increasing Low-Frequency Variants Can Be a Signal of Polygenic Inheritance in Complex Diseases

In most complex diseases, much of the heritability remains unaccounted for by common variants. It has been postulated that lower-frequency variants contribute to the remaining heritability. Here, we describe a method to test for polygenic inheritance from lower-frequency variants by using GWAS summary association statistics. We explored scenarios with many causal low-frequency variants and showed that there is more power to detect risk variants than to detect protective variants, resulting in an increase in the ratio of detected risk to protective variants (R/P ratio). Such an excess can also occur if risk variants are present and kept at lower frequencies because of negative selection. The R/P ratio can be falsely elevated because of reasons unrelated to polygenic inheritance, such as uneven sample sizes or asymmetric population stratification, so precautions to correct for these confounders are essential. We tested our method on published GWAS results and observed a strong signal in some diseases (schizophrenia and type 2 diabetes) but not others. We also explored the shared genetic component in overlapping phenotypes related to inflammatory bowel disease (Crohn disease [CD] and ulcerative colitis [UC]) and diabetic nephropathy (macroalbuminuria and end-stage renal disease [ESRD]). Although the signal was still present when both CD and UC were jointly analyzed, the signal was lost when macroalbuminuria and ESRD were jointly analyzed, suggesting that these phenotypes should best be studied separately. Thus, our method may also help guide the design of future genetic studies of various traits and diseases.

Identification of novel therapeutics for complex diseases from genome-wide association data

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

New generation genomic platforms in investigation of complex diseases and BEN.

(Full text is available at http://www.manu.edu.mk/prilozi). New generation genomic platforms enable us to decipher the complex genetic basis of complex diseases and Balkan Endemic Nephropathy (BEN) at a high-throughput basis. They give valuable information about predisposing Single Nucleotide Polymorphisms (SNPs), Copy Number Variations (CNVs) or Loss of Heterozygosity (LOH) (using SNP-array) and about disease-causing mutations along the whole sequence of candidate-genes (using Next Generation Sequencing). This information could be used for screening of individuals in risk families and moving the main medicine stream to the prevention. They also might have an impact on more effective treatment. Here we discuss these genomic platforms and report some applications of SNP-array technology in a case with familial nephrotic syndrome. Key words: complex diseases, genome wide association studies, SNP, genomic arrays, next generation sequ-encing.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Novel therapeutics for complex diseases from genome-wide association data

The development of novel therapies is essential to lower the burden of complex diseases. The purpose of this study is to identify novel therapeutics for complex diseases using bioinformatic methods. Bioinformatic tools such as candidate gene prediction tools allow identification of disease genes by identifying the potential candidate genes linked to genetic markers of the disease. Candidate gene prediction tools can only identify candidates for further research, and do not identify disease genes directly. Integration of drug-target datasets with candidate gene data-sets can identify novel potential therapeutics suitable for repositioning in clinical trials. Drug repositioning can save valuable time and money spent in therapeutic development of complex diseases.

Epigenetics and migraine; complex mitochondrial interactions contributing to disease susceptibility

Migraine is a common neurological disorder classified by the World Health Organisation (WHO) as one of the top twenty most debilitating diseases in the developed world. Current therapies are only effective for a proportion of sufferers and new therapeutic targets are desperately needed to alleviate this burden. Recently the role of epigenetics in the development of many complex diseases including migraine has become an emerging topic. By understanding the importance of acetylation, methylation and other epigenetic modifications, it then follows that this modification process is a potential target to manipulate epigenetic status with the goal of treating disease. Bisulphite sequencing and methylated DNA immunoprecipitation have been used to demonstrate the presence of methylated cytosines in the human D-loop of mitochondrial DNA (mtDNA), proving that the mitochondrial genome is methylated. For the first time, it has been shown that there is a difference in mtDNA epigenetic status between healthy controls and those with disease, especially for neurodegenerative and age related conditions. Given co-morbidities with migraine and the suggestive link between mitochondrial dysfunction and the lowered threshold for triggering a migraine attack, mitochondrial methylation may be a new avenue to pursue. Creative thinking and new approaches are needed to solve complex problems and a systems biology approach, where multiple layers of information are integrated is becoming more important in complex disease modelling.

Cholesterol Disturbances in Inherited Neurodegenerative Diseases : Studies on Npc1, Ppt1 and Ctsd deficient mice

The central nervous system (CNS) is the most cholesterol-rich organ in the body. Cholesterol is essential to CNS functions such as synaptogenesis and formation of myelin. Significant differences exist in cholesterol metabolism between the CNS and the peripheral organs. However, the regulation of cholesterol metabolism in the CNS is poorly understood compared to our knowledge of the regulation of cholesterol homeostasis in organs reached by cholesterol-carrying lipoprotein particles in the circulation. Defects in CNS cholesterol homeostasis have been linked to a variety of neurodegenerative diseases, including common diseases with complex pathogenetic mechanisms such as Alzheimer s disease. In spite of intense effort, the mechanisms which link disturbed cholesterol homeostasis to these diseases remain elusive. We used three inherited recessive neurodegenerative disorders as models in the studies included in this thesis: Niemann-Pick type C (NPC), infantile neuronal ceroid lipofuscinosis and cathepsin D deficiency. Of these three, NPC has previously been linked to disturbed intracellular cholesterol metabolism. Elucidating the mechanisms with which disturbances of cholesterol homeostasis link to neurodegeneration in recessive inherited disorders with known genetic lesions should shed light on how cholesterol is handled in the healthy CNS and help to understand how these and more complex diseases develop. In the first study we analyzed the synthesis of sterols and the assembly and secretion of lipoprotein particles in Npc1 deficient primary astrocytes. We found that both wild type and Npc1 deficient astrocytes retain significant amounts of desmosterol and other cholesterol precursor sterols as membrane constituents. No difference was observed in the synthesis of sterols and the secretion of newly synthesized sterols between Npc1 wild type, heterozygote or knockout astrocytes. We found that the incorporation of newly synthesized sterols into secreted lipoprotein particles was not inhibited by Npc1 mutation, and the lipoprotein particles were similar to those excreted by wild type astrocytes in shape and size. The bulk of cholesterol was found to be secreted independently of secreted NPC2. These observations demonstrate the ability of Npc1 deficient astrocytes to handle de novo sterols, and highlight the unique sterol composition in the developing brain. Infantile neuronal ceroid lipofuscinosis is caused by the deficiency of a functional Ppt1 enzyme in the cells. In the second study, global gene expression studies of approximately 14000 mouse genes showed significant changes in the expression of 135 genes in Ppt1 deficient neurons compared to wild type. Several genes encoding for enzymes of the mevalonate pathway of cholesterol biosynthesis showed increased expression. As predicted by the expression data, sterol biosynthesis was found to be upregulated in the knockout neurons. These data link Ppt1 deficiency to disturbed cholesterol metabolism in CNS neurons. In the third study we investigated the effect of cathepsin D deficiency on the structure of myelin and lipid homeostasis in the brain. Our proteomics data, immunohistochemistry and western blotting data showed altered levels of the myelin protein components myelin basic protein, proteolipid protein and 2 , 3 -cyclic nucleotide 3 phosphodiesterase in the brains of cathepsin D deficient mice. Electron microscopy revealed altered myelin structure in cathepsin D deficient brains. Additionally, plasmalogen-derived alkenyl chains and 20- and 24-carbon saturated and monounsaturated fatty acids typical for glycosphingolipids were found to be significantly reduced, but polyunsaturated species were significantly increased in the knockout brains, pointing to a decrease in white matter. The levels of ApoE and ABCA1 proteins linked to cholesterol efflux in the CNS were found to be altered in the brains of cathepsin D deficient mice, along with an accumulation of cholesteryl esters and a decrease in triglycerols. Together these data demonstrate altered myelin architecture in cathepsin D deficient mice and link cathepsin D deficiency to aberrant cholesterol metabolism and trafficking. Basic research into rare monogenic diseases sheds light on the underlying biological processes which are perturbed in these conditions and contributes to our understanding of the physiological function of healthy cells. Eventually, understanding gained from the study of disease models may contribute towards establishing treatment for these disorders and further our understanding of the pathogenesis of other, more complex and common diseases.

The importance of modelling heterogeneity in complex disease: application to NIMH Schizophrenia Genetics Initiative data

As for other complex diseases, linkage analyses of schizophrenia (SZ) have produced evidence for numerous chromosomal regions, with inconsistent results reported across studies. The presence of locus heterogeneity appears likely and may reduce the power of linkage analyses if homogeneity is assumed. In addition, when multiple heterogeneous datasets are pooled, inter-sample variation in the proportion of linked families (alpha) may diminish the power of the pooled sample to detect susceptibility loci, in spite of the larger sample size obtained. We compare the significance of linkage findings obtained using allele-sharing LOD scores (LOD(exp))-which assume homogeneity-and heterogeneity LOD scores (HLOD) in European American and African American NIMH SZ families. We also pool these two samples and evaluate the relative power of the LOD(exp) and two different heterogeneity statistics. One of these (HLOD-P) estimates the heterogeneity parameter alpha only in aggregate data, while the second (HLOD-S) determines alpha separately for each sample. In separate and combined data, we show consistently improved performance of HLOD scores over LOD(exp). Notably, genome-wide significant evidence for linkage is obtained at chromosome 10p in the European American sample using a recessive HLOD score. When the two samples are combined, linkage at the 10p locus also achieves genome-wide significance under HLOD-S, but not HLOD-P. Using HLOD-S, improved evidence for linkage was also obtained for a previously reported region on chromosome 15q. In linkage analyses of complex disease, power may be maximised by routinely modelling locus heterogeneity within individual datasets, even when multiple datasets are combined to form larger samples.

Epigenetics and autoimmune diseases

Epigenetics is defined as the study of all inheritable and potentially reversible changes in genome function that do not alter the nucleotide sequence within the DNA. Epigenetic mechanisms such as DNA methylation, histone modification, nucleosome positioning, and microRNAs (miRNAs) are essential to carry out key functions in the regulation of gene expression. Therefore, the epigenetic mechanisms are a window to understanding the possible mechanisms involved in the pathogenesis of complex diseases such as autoimmune diseases. It is noteworthy that autoimmune diseases do not have the same epidemiology, pathology, or symptoms but do have a common origin that can be explained by the sharing of immunogenetic mechanisms. Currently, epigenetic research is looking for disruption in one or more epigenetic mechanisms to provide new insights into autoimmune diseases. The identification of cell-specific targets of epigenetic deregulation will serve us as clinical markers for diagnosis, disease progression, and therapy approaches.

Organic solvents as risk factor for autoimmune diseases: a systematic review and meta-analysis

Background: Genetic and epigenetic factors interacting with the environment over time are the main causes of complex diseases such as autoimmune diseases (ADs). Among the environmental factors are organic solvents (OSs), which are chemical compounds used routinely in commercial industries. Since controversy exists over whether ADs are caused by OSs, a systematic review and meta-analysis were performed to assess the association between OSs and ADs. Methods and Findings: The systematic search was done in the PubMed, SCOPUS, SciELO and LILACS databases up to February 2012. Any type of study that used accepted classification criteria for ADs and had information about exposure to OSs was selected. Out of a total of 103 articles retrieved, 33 were finally included in the meta-analysis. The final odds ratios (ORs) and 95% confidence intervals (CIs) were obtained by the random effect model. A sensitivity analysis confirmed results were not sensitive to restrictions on the data included. Publication bias was trivial. Exposure to OSs was associated to systemic sclerosis, primary systemic vasculitis and multiple sclerosis individually and also to all the ADs evaluated and taken together as a single trait (OR: 1.54; 95% CI: 1.25-1.92; p-value, 0.001). Conclusion: Exposure to OSs is a risk factor for developing ADs. As a corollary, individuals with non-modifiable risk factors (i.e., familial autoimmunity or carrying genetic factors) should avoid any exposure to OSs in order to avoid increasing their risk of ADs.