Social Meets Molecular: Combining Phylogenetic and Latent Class Analyses to Understand HIV-1 Transmission in Switzerland

Switzerland has a complex human immunodeficiency virus (HIV) epidemic involving several populations. We examined transmission of HIV type 1 (HIV-1) in a national cohort study. Latent class analysis was used to identify socioeconomic and behavioral groups among 6,027 patients enrolled in the Swiss HIV Cohort Study between 2000 and 2011. Phylogenetic analysis of sequence data, available for 4,013 patients, was used to identify transmission clusters. Concordance between sociobehavioral groups and transmission clusters was assessed in correlation and multiple correspondence analyses. A total of 2,696 patients were infected with subtype B, 203 with subtype C, 196 with subtype A, and 733 with recombinant subtypes (mainly CRF02_AG and CRF01_AE). Latent class analysis identified 8 patient groups. Most transmission clusters of subtype B were shared between groups of gay men (groups 1-3) or between the heterosexual groups "heterosexual people of lower socioeconomic position" (group 4) and "injection drug users" (group 8). Clusters linking homosexual and heterosexual groups were associated with "older heterosexual and gay people on welfare" (group 5). "Migrant women in heterosexual partnerships" (group 6) and "heterosexual migrants on welfare" (group 7) shared non-B clusters with groups 4 and 5. Combining approaches from social and molecular epidemiology can provide insights into HIV-1 transmission and inform the design of prevention strategies.

Street characteristics preferred for transportation walking among older adults: a choice-based conjoint analysis with manipulated photographs

BACKGROUND: Knowledge about the relationships between micro-scale environmental factors and older adults' walking for transport is limited and inconsistent. This is probably due to methodological limitations, such as absence of an accurate neighborhood definition, lack of environmental heterogeneity, environmental co-variation, and recall bias. Furthermore, most previous studies are observational in nature. We aimed to address these limitations by investigating the effects of manipulating photographs on micro-scale environmental factors on the appeal of a street for older adults' transportation walking. Secondly, we used latent class analysis to examine whether subgroups could be identified that have different environmental preferences for transportation walking. Thirdly, we investigated whether these subgroups differed in socio-demographic, functional and psychosocial characteristics, current level of walking and environmental perceptions of their own street.

METHODS: Data were collected among 1131 Flemish older adults through an online (n = 940) or an interview version of the questionnaire (n = 191). This questionnaire included a choice-based conjoint exercise with manipulated photographs of a street. These manipulated photographs originated from one panoramic photograph of an existing street that was manipulated on nine environmental attributes. Participants chose which of two presented streets they would prefer to walk for transport.

RESULTS: In the total sample, sidewalk evenness had by far the greatest appeal for transportation walking. The other environmental attributes were less important. Four subgroups that differed in their environmental preferences for transportation walking were identified. In the two largest subgroups (representing 86% of the sample) sidewalk evenness was the most important environmental attribute. In the two smaller subgroups (each comprising 7% of the sample), traffic volume and speed limit were the most important environmental attributes for one, and the presence of vegetation and a bench were the most important environmental attributes for the other. This latter subgroup included a higher percentage of service flat residents than the other subgroups.

CONCLUSIONS: Our results suggest that the provision of even sidewalks should be considered a priority when developing environmental interventions aiming to stimulate older adults' transportation walking. Natural experiments are needed to confirm whether our findings can be translated to real environments and actual transportation walking behavior.

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.

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).

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.

Identification of molecular genetic factors that influence migraine

Migraine is a common neurological disorder with a strong genetic basis. However, the complex nature of the disorder has meant that few genes or susceptibility loci have been identified and replicated consistently to confirm their involvement in migraine. Approaches to genetic studies of the disorder have included analysis of the rare migraine subtype, familial hemiplegic migraine with several causal genes identified for this severe subtype. However, the exact genetic contributors to the more common migraine subtypes are still to be deciphered. Genome-wide studies such as genome-wide association studies and linkage analysis as well as candidate genes studies have been employed to investigate genes involved in common migraine. Neurological, hormonal and vascular genes are all considered key factors in the pathophysiology of migraine and are a focus of many of these studies. It is clear that the influence of individual genes on the expression of this disorder will vary. Furthermore, the disorder may be dependent on gene–gene and gene–environment interactions that have not yet been considered. In addition, identifying susceptibility genes may require phenotyping methods outside of the International Classification of Headache Disorders II criteria, such as trait component analysis and latent class analysis to better define the ambit of migraine expression.

Polysubstance use in cannabis users referred for treatment: drug use profiles, psychiatric comorbidity and cannabis-related beliefs

Background: Population-based surveys demonstrate cannabis users are more likely to use both illicit and licit substances, compared with non-cannabis users. Few studies have examined the substance use profiles of cannabis users referred for treatment. Co-existing mental health symptoms and underlying cannabis-related beliefs associated with these profiles remains unexplored. Methods: Comprehensive drug use and dependence severity (Severity of Dependence Scale-Cannabis) data were collected on a sample of 826 cannabis users referred for treatment. Patients completed the General Health Questionnaire, Cannabis Expectancy Questionnaire, Cannabis Refusal Self-Efficacy Questionnaire, and Positive Symptoms and Manic-Excitement subscales of the Brief Psychiatric Rating Scale. Latent class analysis was performed on last month use of drugs to identify patterns of multiple drug use. Mental health comorbidity and cannabis beliefs were examined by identified drug use pattern. Results: A three-class solution provided the best fit to the data: (1) cannabis and tobacco users (n = 176), (2) cannabis, tobacco, and alcohol users (n = 498), and (3) wide-ranging sub- stance users (n = 132). Wide-ranging substance users (3) reported higher levels of cannabis dependence severity, negative cannabis expectancies, lower opportunistic, and emotional relief self-efficacy, higher levels of depression and anxiety and higher manic-excitement and positive psychotic symptoms. Conclusion: In a sample of cannabis users referred for treatment, wide-ranging substance use was associated with elevated risk on measures of cannabis dependence, co-morbid psychopathology, and dysfunctional cannabis cognitions. These findings have implications for cognitive-behavioral assessment and treatment.

The topography of multiple drug use among adolescent Australians : findings from the National Drug Strategy Household Survey

Introduction and aims: Despite evidence that many Australian adolescents have considerable experience with various drug types, little is known about the extent to which adolescents use multiple substances. The aim of this study was to examine the degree of clustering of drug types within individuals, and the extent to which demographic and psychosocial predictors are related to cluster membership. Design and method: A sample of 1402 adolescents aged 12-17. years were extracted from the Australian 2007 National Drug Strategy Household Survey. Extracted data included lifetime use of 10 substances, gender, psychological distress, physical health, perceived peer substance use, socioeconomic disadvantage, and regionality. Latent class analysis was used to determine clusters, and multinomial logistic regression employed to examine predictors of cluster membership. Result: There were 3 latent classes. The great majority (79.6%) of adolescents used alcohol only, 18.3% were limited range multidrug users (encompassing alcohol, tobacco, and marijuana), and 2% were extended range multidrug users. Perceived peer drug use and psychological distress predicted limited and extended multiple drug use. Psychological distress was a more significant predictor of extended multidrug use compared to limited multidrug use. Discussion and conclusion: In the Australian school-based prevention setting, a very strong focus on alcohol use and the linkages between alcohol, tobacco and marijuana are warranted. Psychological distress may be an important target for screening and early intervention for adolescents who use multiple drugs.

Leisure activities and obesity in adolescence : a follow-up study among twins

The aims of this dissertation were 1) to investigate associations of weight status of adolescents with leisure activities, and computer and cell phone use, and 2) to investigate environmental and genetic influences on body mass index (BMI) during adolescence. Finnish twins born in 1983–1987 responded to postal questionnaires at the ages of 11-12 (5184 participants), 14 (4643 participants), and 17 years (4168 participants). Information was obtained on weight and height, leisure activities including television viewing, video viewing, computer games, listening to music, board games, musical instrument playing, reading, arts, crafts, socializing, clubs, sports, and outdoor activities, as well as computer and cell phone use. Activity patterns were studied using latent class analysis. The relationship between leisure activities and weight status was investigated using logistic and linear regression. Genetic and environmental effects on BMI were studied using twin modeling. Of individual leisure activities, sports were associated with decreased overweight risk among boys in both cross-sectional and longitudinal analyses, but among girls only cross-sectionally. Many sedentary leisure activities, such as video viewing (boys/girls), arts (boys), listening to music (boys), crafts (girls), and board games (girls), had positive associations with being overweight. Computer use was associated with a higher prevalence of overweight in cross-sectional analyses. However, musical instrument playing, commonly considered as a sedentary activity, was associated with a decreased overweight risk among boys. Four patterns of leisure activities were found: ‘Active and sociable’, ‘Active but less sociable’, ‘Passive but sociable’, and ‘Passive and solitary’. The prevalence of overweight was generally highest among the ‘Passive and solitary’ adolescents. Overall, leisure activity patterns did not predict overweight risk later in adolescence. An exception were 14-year-old ‘Passive and solitary’ girls who had the greatest risk of becoming overweight by 17 years of age. Heritability of BMI was high (0.58-0.83). Common environmental factors shared by family-members affected the BMI at 11-12 and 14 years but their effect had disappeared by 17 years of age. Additive genetic factors explained 90-96% of the BMI stability across adolescence. Genetic correlations across adolescence were high, which suggests similar genetic effects on BMI throughout adolescence, while unique environmental effects on BMI appeared to vary. These findings suggest that family-based interventions hold promise for obesity prevention into early and middle adolescence, but that later in adolescence obesity prevention should focus on individuals. A useful target could be adolescents' leisure time, and our findings highlight the importance of versatility in leisure activities.

Migraine symptomatology and major depressive disorder

INTRODUCTION AND OBJECTIVE Migraine and major depressive disorder (MDD) frequently co-occur, but it is unclear whether depression is associated with a specific subtype of migraine. The objective of this study was to investigate whether migraine is qualitatively different in MDD patients (N = 1816) and non-depressed controls (N = 3428). METHODS Migraine symptom data were analyzed using multi-group Latent Class Analysis, and a qualitative comparison was made between the symptom profiles of MDD patients and controls, while allowing for differences in migraine prevalence and severity between groups. RESULTS In both groups, three migrainous headache classes were identified, which differed primarily in terms of severity. Both mild and severe migrainous headaches were two to three times more prevalent in MDD patients. Migraine symptom profiles showed only minor qualitative differences in the MDD and non-MDD groups: in the severe migrainous headache class, significant differences were observed only in the prevalence of aggravation by physical activity (83% and 91% for the non-MDD and MDD groups, respectively) and aura (42% vs. 53%, respectively). CONCLUSION The similar overall symptom profiles observed in the MDD and non-MDD subjects suggest that a similar disease process may underlie migraine in both groups.

A genome-wide linkage scan provides evidence for both new and previously reported loci influencing common migraine

Latent class analysis was performed on migraine symptom data collected in a Dutch population sample (N = 12,210, 59% female) in order to obtain empirical groupings of individuals suffering from symptoms of migraine headache. Based on these heritable groupings (h(2) = 0.49, 95% CI: 0.41-0.57) individuals were classified as affected (migrainous headache) or unaffected. Genome-wide linkage analysis was performed using genotype data from 105 families with at least 2 affected siblings. In addition to this primary phenotype, linkage analyses were performed for the individual migraine symptoms. Significance levels, corrected for the analysis of multiple traits, were determined empirically via a novel simulation approach. Suggestive linkage for migrainous headache was found on chromosomes 1 (LOD = 1.63; pointwise P = 0.0031), 13 (LOD = 1.63; P = 0.0031), and 20 (LOD = 1.85; P = 0.0018). Interestingly, the chromosome 1 peak was located close to the ATP1A2 gene, associated with familial hemiplegic migraine type 2 (FHM2). Individual symptom analysis produced a LOD score of 1.97 (P = 0.0013) on chromosome 5 (photo/phonophobia), a LOD score of 2.13 (P = 0.0009) on chromosome 10 (moderate/severe pain intensity) and a near significant LOD score of 3.31 (P = 0.00005) on chromosome 13 (pulsating headache). These peaks were all located near regions previously reported in migraine linkage studies. Our results provide important replication and support for the presence of migraine susceptibility genes within these regions, and further support the utility of an LCA-based phenotyping approach and analysis of individual symptoms in migraine genetic research. Additionally, our novel "2-step" analysis and simulation approach provides a powerful means to investigate linkage to individual trait components.

Consistently replicating locus linked to migraine on 10q22-q23

Here, we present the results of two genome-wide scans in two diverse populations in which a consistent use of recently introduced migraine-phenotyping methods detects and replicates a locus on 10q22-q23, with an additional independent replication. No genetic variants have been convincingly established in migraine, and although several loci have been reported, none of them has been consistently replicated. We employed the three known migraine-phenotyping methods (clinical end diagnosis, latent-class analysis, and trait-component analysis) with robust multiple testing correction in a large sample set of 1675 individuals from 210 migraine families from Finland and Australia. Genome-wide multipoint linkage analysis that used the Kong and Cox exponential model in Finns detected a locus on 10q22-q23 with highly significant evidence of linkage (LOD 7.68 at 103 cM in female-specific analysis). The Australian sample showed a LOD score of 3.50 at the same locus (100 cM), as did the independent Finnish replication study (LOD score 2.41, at 102 cM). In addition, four previously reported loci on 8q21, 14q21, 18q12, and Xp21 were also replicated. A shared-segment analysis of 10q22-q23 linked Finnish families identified a 1.6-9.5 cM segment, centered on 101 cM, which shows in-family homology in 95% of affected Finns. This region was further studied with 1323 SNPs. Although no significant association was observed, four regions warranting follow-up studies were identified. These results support the use of symptomology-based phenotyping in migraine and suggest that the 10q22-q23 locus probably contains one or more migraine susceptibility variants.

Migraine with aura and migraine without aura are not distinct entities: further evidence from a large Dutch population study

It is often debated whether migraine with aura (MA) and migraine without aura (MO) are etiologically distinct disorders. A previous study using latent class analysis (LCA) in Australian twins showed no evidence for separate subtypes of MO and MA. The aim of the present study was to replicate these results in a population of Dutch twins and their parents, siblings and partners (N = 10,144). Latent class analysis of International Headache Society (IHS)-based migraine symptoms resulted in the identification of 4 classes: a class of unaffected subjects (class 0), a mild form of nonmigrainous headache (class 1), a moderately severe type of migraine (class 2), typically without neurological symptoms or aura (8% reporting aura symptoms), and a severe type of migraine (class 3), typically with neurological symptoms, and aura symptoms in approximately half of the cases. Given the overlap of neurological symptoms and nonmutual exclusivity of aura symptoms, these results do not support the MO and MA subtypes as being etiologically distinct. The heritability in female twins of migraine based on LCA classification was estimated at .50 (95% confidence intervals [CI] .27 - .59), similar to IHS-based migraine diagnosis (h2 = .49, 95% CI .19-.57). However, using a dichotomous classification (affected-unaffected) decreased heritability for the IHS-based classification (h2 = .33, 95% CI .00-.60), but not the LCA-based classification (h2 = .51, 95% CI .23-.61). Importantly, use of the LCA-based classification increased the number of subjects classified as affected. The heritability of the screening question was similar to more detailed LCA and IHS classifications, suggesting that the screening procedure is an important determining factor in genetic studies of migraine.

Genomewide significant linkage to migrainous headache on chromosome 5q21

Familial typical migraine is a common, complex disorder that shows strong familial aggregation. Using latent-class analysis (LCA), we identified subgroups of people with migraine/severe headache in a community sample of 12,245 Australian twins (60% female), drawn from two cohorts of individuals aged 23-90 years who completed an interview based on International Headache Society criteria. We report results from genomewide linkage analyses involving 756 twin families containing a total of 790 independent sib pairs (130 affected concordant, 324 discordant, and 336 unaffected concordant for LCA-derived migraine). Quantitative-trait linkage analysis produced evidence of significant linkage on chromosome 5q21 and suggestive linkage on chromosomes 8, 10, and 13. In addition, we replicated previously reported typical-migraine susceptibility loci on chromosomes 6p12.2-p21.1 and 1q21-q23, the latter being within 3 cM of the rare autosomal dominant familial hemiplegic migraine gene (ATP1A2), a finding which potentially implicates ATP1A2 in familial typical migraine for the first time. Linkage analyses of individual migraine symptoms for our six most interesting chromosomes provide tantalizing hints of the phenotypic and genetic complexity of migraine. Specifically, the chromosome 1 locus is most associated with phonophobia; the chromosome 5 peak is predominantly associated with pulsating headache; the chromosome 6 locus is associated with activity-prohibiting headache and photophobia; the chromosome 8 locus is associated with nausea/vomiting and moderate/severe headache; the chromosome 10 peak is most associated with phonophobia and photophobia; and the chromosome 13 peak is completely due to association with photophobia. These results will prove to be invaluable in the design and analysis of future linkage and linkage disequilibrium studies of migraine.

Desenvolvimento e validação de modelo preditivo e avaliação de testes de diagnóstico por classe latente para o parasitismo por L. chagasi em cães atendidos no Hospital Veterinário Universitário da UFPI, Teresina

A leishmaniose visceral americana (LVA) é uma doença em expansão no Brasil, para a qual se dispõem de poucas, e aparentemente ineficientes, estratégias de controle. Um dos grandes problemas para a contenção da leishmaniose visceral americana é a falta de um método acurado de identificação dos cães infectados, considerados os principais reservatórios da doença no meio urbano. Neste sentido, a caracterização de marcadores clínico-laboratoriais da infecção neste reservatório e a avaliação mais adequada do desempenho de testes para diagnóstico da infecção podem contribuir para aumentar a efetividade das estratégias de controle da LVA. Com isso, o presente estudo tem dois objetivos principais: (1) desenvolver e validar um modelo de predição para o parasitismo por Leishmania chagasi em cães, baseado em resultados de testes sorológicos e sinais clínicos e (2) avaliar a sensibilidade e especificidade de critérios clínicos, sorológicos e parasitológicos para detecção de infecção canina por L. chagasi mediante análise de classe latente. O primeiro objetivo foi desenvolvido a partir de estudo em que foram obtidos dados de exames clínico, sorológico e parasitológico de todos os cães, suspeitos ou não de LVA, atendidos no Hospital Veterinário Universitário da Universidade Federal do Piauí (HVU-UFPI), em Teresina, nos anos de 2003 e 2004, totalizando 1412 animais. Modelos de regressão logística foram construídos com os animais atendidos em 2003 com a finalidade de desenvolver um modelo preditivo para o parasitismo com base nos sinais clínicos e resultados de sorologia por Imunofluorescência Indireta (IFI). Este modelo foi validado nos cães atendidos no hospital em 2004. Para a avaliação da área abaixo da curva ROC (auROC), sensibilidade, especificidade, valores preditivos positivo (VPP), valores preditivos negativo (VPN) e acurácia global, foram criados três modelos: um somente baseado nas variáveis clínicas, outro considerando somente o resultado sorológico e um último considerando conjuntamente a clínica e a sorologia. Dentre os três, o último modelo apresentou o melhor desempenho (auROC=90,1%, sensibilidade=82,4%, especificidade=81,6%, VPP=73,4%, VPN=88,2% e acurácia global=81,9%). Conclui-se que o uso de modelos preditivos baseados em critérios clínicos e sorológicos para o diagnóstico da leishmaniose visceral canina pode ser de utilidade no processo de avaliação da infecção canina, promovendo maior agilidade na contenção destes animais com a finalidade de reduzir os níveis de transmissão. O segundo objetivo foi desenvolvido por meio de um estudo transversal com 715 cães de idade entre 1 mês e 13 anos, com raça variada avaliados por clínicos veterinários no HVU-UFPI, no período de janeiro a dezembro de 2003. As sensibilidades e especificidades de critérios clínicos, sorológicos e parasitológicos para detecção de infecção canina por Leishmania chagasi foram estimadas por meio de análise de classe latente, considerando quatro modelos de testes e diferentes pontos de corte. As melhores sensibilidades estimadas para os critérios clínico, sorológico e parasitológico foram de 60%, 95% e 66%, respectivamente. Já as melhores especificidades estimadas para os critérios clínico, sorológico e parasitológico foram de 77%, 90% e 100%, respectivamente. Conclui-se que o uso do exame parasitológico como padrão-ouro para validação de testes diagnósticos não é apropriado e que os indicadores de acurácia dos testes avaliados são insuficientes e não justificam que eles sejam usados isoladamente para diagnóstico da infecção com a finalidade de controle da doença.