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.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

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.

Using genome-Wwide complex trait analysis to quantify 'missing heritability' in Parkinson's disease

Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered. © Published by Oxford University Press 2012.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Dietetic practice by Australian and Canadian dietitians with people with Parkinson's disease

Although Parkinson’s disease (PD) is a complex disease for which appropriate nutrition management is important, limited evidence is currently available to support dietetic practice. Existing PD-specific guidelines do not span all phases of the Nutrition Care Process (NCP). This study aimed to document PD-specific nutrition management practice by Australian and Canadian dietitians. DAA members and PEN subscribers were invited to participate in an online survey (late 2011). Eighty-four dietitians responded (79.8% Australian). The majority (70.2%) worked in the clinical setting. Existing non-PD guidelines were used by 52.4% while 53.6% relied on self-initiated literature reviews. Weight loss/malnutrition, protein intake, dysphagia and constipation were common issues in all NCP phases. Respondents also requested more information/evidence for these topics. Malnutrition screening (82.1%) and assessment (85.7%) were routinely performed. One-third did not receive referrals for weight loss for overweight/obesity. Protein intake meeting gender/age recommendations (69.0%), and high energy/high protein diets to manage malnutrition (82.1%) were most commonly used. Constipation management was through high fibre diets (86.9%). Recommendations for spacing of meals and PD medications varied with 34.5% not making recommendations. Nutritional diagnosis (70.2%) and stage of disease (61.9%) guided monitoring frequency. Common outcome measures included appropriate weight change (97.6%) and regular bowel movements (88.1%). With limited PD-specific guidance, dietitians applied best available evidence for other groups with similar issues. Dietitians requested evidence-based guidelines specifically for the nutritional management of PD. Guideline development should focus on those areas reported as commonly encountered. This process can identify the gaps in evidence to guide future research.

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

Association between polymorphisms in the progesterone receptor gene and endometriosis

The progesterone receptor (PR) is a candidate gene for the development of endometriosis, a complex disease with strong hormonal features, common in women of reproductive age. We typed the 306 base pair Alu insertion (AluIns) polymorphism in intron G of PR in 101 individuals, estimated linkage disequilibrium (LD) between five single-nucleotide polymorphisms (SNPs) across the PR locus in 980 Australian triads (endometriosis case and two parents) and used transmission disequilibrium testing (TDT) for association with endometriosis. The five SNPs showed strong pairwise LD, and the AluIns was highly correlated with proximal SNPs rs1042839 (Δ2 = 0.877, D9 = 1.00, P < 0.0001) and rs500760 (Δ2 = 0.438, D9 = 0.942, P < 0.0001). TDT showed weak evidence of allelic association between endometriosis and rs500760 (P = 0.027) but not in the expected direction. We identified a common susceptibility haplotype GGGCA across the five SNPs (P = 0.0167) in the whole sample, but likelihood ratio testing of haplotype transmission and non-transmission of the AluIns and flanking SNPs showed no significant pattern. Further, analysis of our results pooled with those from two previous studies suggested that neither the T2 allele of the AluIns nor the T1/T2 genotype was associated with endometriosis.

Molecular genetics of migraine

Migraine is a common neurological disorder with a significantly heritable component. It is a complex disease and despite numerous molecular genetic studies, the exact pathogenesis causing the neurological disturbance remains poorly understood. Although several known molecular mechanisms have been associated with an increased risk for developing migraine, there remains significant scope for future studies. The majority of studies have investigated the most plausible candidate genes involved in common migraine pathogenesis utilising criteria that takes into account a combination of physiological functionality in conjunction with regions of genomic association. Thus, far genes involved in neurological, vascular or hormonal pathways have been identified and investigated on this basis. Genome-wide association studies (GWAS) studies have helped to identify novel regions that may be associated with migraine and have aided in providing the basis for further molecular investigations. However, further studies utilising sequencing technologies are required to characterise the genetic basis for migraine.

A possible role for mitochondrial dysfunction in migraine

Migraine is a common neurological disorder characterised by debilitating head pain and an assortment of additional symptoms which can include nausea, emesis, photophobia, phonophobia and occasionally visual sensory disturbances. Migraine is a complex disease caused by an interplay between predisposing genetic variants and environmental factors. It affects approximately 12 % of studied Caucasian populations with affected individuals being predominantly female. Genes involved in neurological, vascular or hormonal pathways have all been implicated in predisposition towards developing migraine. All of these are nuclear encoded genes, but given the role of mitochondria in a number of neurological disorders and in energy production it is possible that mitochondrial variants may play a role in the pathogenesis of this disease. Mitochondrial DNA has been a useful tool for studying population genetics and human genetic diseases due to the clear inheritance shown through successive generations. Given the clear gender bias found in migraine patients it may be important to investigate X-linked inheritance and mitochondrial-related variants in this disorder. This paper explores the possibility that mitochondrial DNA changes may play a role in migraine. Few variants in the mitochondrial genome have so far been investigated in migraine and new studies should be aimed towards investigating the role of mitochondrial DNA in this common disorder.

The role of the MTHFR gene in migraine

Migraine is a common neurological disorder and is characterized by debilitating head pain and an assortment of additional symptoms which can include nausea, emesis, photophobia, phonophobia, and occasionally, visual sensory disturbances. A number of genes have been implicated in the pathogenesis of this disease, including genes involved in regulating the vascular system. Of particular importance are the methylenetetrahydrofolate reductase (MTHFR) gene and the role it plays in migraine with aura. Migraine with aura has previously been shown to have a significant comorbidity with stroke, making the vascular class of genes a priority for migraine studies. In this report, we outline the importance of the MTHFR gene in migraine and also discuss the use of a genetic isolate to investigate MTHFR genetic variants. From this study, 3 MTHFR single nucleotide polymorphisms showing association with migraine in the Norfolk Island population have been identified, thus reinforcing the potential role of MTHFR in migraine susceptibility. Further studies will continue to build a gene profile of variants involved in the complex disease migraine and improve understanding of the underlying genetic causes of this disorder.

Linkage disequilibrium analysis in the genetically isolated Norfolk Island population

Norfolk Island is a human genetic isolate, possessing unique population characteristics that could be utilized for complex disease gene localization. Our intention was to evaluate the extent and strength of linkage disequilibrium (LD) in the Norfolk isolate by investigating markers within Xq13.3 and the NOS2A gene encoding the inducible nitric oxide synthase. A total of six microsatellite markers spanning approximately 11 Mb were assessed on chromosome Xq13.3 in a group of 56 men from Norfolk Island. Additionally, three single nucleotide polymorphisms (SNPs) localizing to the NOS2A gene were analyzed in a subset of the complex Norfolk pedigree. With the exception of two of the marker pairs, one of which is the most distantly spaced marker, all the Xq13.3 marker pairs were found to be in significant LD indicating that LD extends up to 9.5-11.5 Mb in the Norfolk Island population. Also, all SNPs studied showed significant LD in both Norfolk Islanders and Australian Caucasians, with two of the marker pairs in complete LD in the Norfolk population only. The Norfolk Island study population possesses a unique set of characteristics including founder effect, geographical isolation, exhaustive genealogical information and phenotypic data of use to cardiovascular disease risk traits. With LD extending up to 9.5-11 Mb, the Norfolk isolate should be a powerful resource for the localization of complex disease genes.

Serum immunoglobulin G directed against porcine trypsin in the serum of cystic fibrosis children receiving porcine pancreatic enzyme supplements

Cystic fibrosis (CF) patients require pancreatic enzyme replacement therapy to correct pancreatic insufficiency. These enzymes are derived from porcine pancreas and are known to be antigenic. To determine the possible clinical consequences, a specific ELISA was developed to detect IgG antibody directed against porcine trypsin (PTAb) in the sera of CF patients. The assay was used to evaluate the occurrence of PTAb in a cross sectional study of 103 CF patients in relation to the introduction of porcine enzyme therapy, clinical status and genotype. Antibodies against porcine trypsin were detected in the sera of 63% of patients unrelated to the age of commencement or the duration of enzyme therapy. No differences were observed in the clinical status of CF patients who had developed PTAb (n = 65) and those who had no detectable PTAb (n = 38) as determined from: the current prescribed dose of porcine pancreatic enzyme capsules; Z scores for height and weight; and respiratory function tests. It is suggested that the PTAb commonly found in the sera of CF patients are of doubtful clinical significance but the prospect of PTAb contributing to immune complex disease should be examined further.

Independent replication and meta-analysis for endometriosis risk loci

Endometriosis is a complex disease that affects 6-10% of women in their reproductive years and 20-50% of women with infertility. Genome-wide and candidate-gene association studies for endometriosis have identified 10 independent risk loci, and of these, nine (rs7521902, rs13394619, rs4141819, rs6542095, rs1519761, rs7739264, rs12700667, rs1537377, and rs10859871) are polymorphic in European populations. Here we investigate the replication of nine SNP loci in 998 laparoscopically and histologically confirmed endometriosis cases and 783 disease-free controls from Belgium. SNPs rs7521902, rs13394619, and rs6542095 show nominally significant (p <.05) associations with endometriosis, while the directions of effect for seven SNPs are consistent with the original reports. Association of rs6542095 at the IL1A locus with 'All' (p =.066) and 'Grade-B' (p =.01) endometriosis is noteworthy because this is the first successful replication in an independent population. Meta-analysis with the published results yields genome-wide significant evidence for rs7521902, rs13394619, rs6542095, rs12700667, rs7739264, and rs1537377. Notably, three coding variants in GREB1 (near rs13394619) and CDKN2B-AS1 (near rs1537377) also showed nominally significant associations with endometriosis. Overall, this study provides important replication in a uniquely characterized independent population, and indicates that the majority of the original genome-wide association findings are not due to chance alone. © The Author(s) 2015.