Statistical modelling strategies in molecular epidemiology, with an application to attention-deficit hyperactivity disorder

Since 1990s, the technological developments in measuring molecular data have been instrumental in advancing molecular epidemiology. A consequential challenge is to integrate the evidence from multiple molecular datasets for a better understanding of the biological mechanisms underlying complex traits. In this thesis, I applied different statistical modelling approaches to investigate the biological background of attention-deficit/hyperactivity disorder (ADHD), a common neurodevelopmental disorder with an early onset, high persistence and a notable impact on the global burden of disease. I examined the putative impact of exposure to maternal smoking during pregnancy on the risk of ADHD and other adverse outcomes in the offspring related to epigenetic modifications and other molecular changes. The potential causality between ADHD and obesity was analysed using genetically informative methods. The link between systemic chronic inflammation, which can be triggered by smoking or obesity, and common psychiatric outcomes was also investigated. The main dataset used was the Northern Finland Birth Cohort 1986 (N = 6,728 for ADHD symptoms, N = 432 for phenotype and full omics data available), with complementary data from other European cohorts and publicly available summary statistics. I used integrative statistical approaches that leverage evidence from different omics datasets. Regression modelling and Mendelian Randomisation techniques were applied throughout, and a recently published network method based on sparse canonical correlation analysis was also used. The results showed evidence for a long-term impact of intrauterine smoke exposure on offspring DNA methylation, and some indication that DNA methylation mediates the effect of the smoke exposure on offspring later life health outcomes. There was also suggestive bidirectional causality between ADHD and obesity, and evidence for an inflammatory component in the aetiology of psychiatric outcomes. This thesis adds to the literature by a thorough investigation of different omics datasets and integrative statistical approaches applied to ADHD and other psychiatric outcomes.Open Acces

The link between Attention Deficit Hyperactivity Disorder (ADHD) symptoms and obesity-related traits: Genetic and prenatal explanations

Abstract Attention-deficit/hyperactivity disorder (ADHD) often co-occurs with obesity, however, the potential causality between the traits remains unclear. We examined both genetic and prenatal evidence for causality using Mendelian Randomisation (MR) and polygenic risk scores (PRS). We conducted bi-directional MR on ADHD liability and six obesity-related traits using summary statistics from the largest available meta-analyses of genome-wide association studies. We also examined the shared genetic aetiology between ADHD symptoms (inattention and hyperactivity) and body mass index (BMI) by PRS association analysis using longitudinal data from Northern Finland Birth Cohort 1986 (NFBC1986, n = 2984). Lastly, we examined the impact of the prenatal environment by association analysis of maternal pre-pregnancy BMI and offspring ADHD symptoms, adjusted for PRS of both traits, in NFBC1986 dataset. Through MR analyses, we found evidence for bidirectional causality between ADHD liability and obesity-related traits. PRS association analyses showed evidence for genetic overlap between ADHD symptoms and BMI. We found no evidence for a difference between inattention and hyperactivity symptoms, suggesting that neither symptom subtype is driving the association. We found evidence for association between maternal pre-pregnancy BMI and offspring ADHD symptoms after adjusting for both BMI and ADHD PRS (association p-value = 0.027 for inattention, p = 0.008 for hyperactivity). These results are consistent with the hypothesis that the co-occurrence between ADHD and obesity has both genetic and prenatal environmental origins

Multivariable G-E interplay in the prediction of educational achievement

Polygenic scores are increasingly powerful predictors of educational achievement. It is unclear, however, how sets of polygenic scores, which partly capture environmental effects, perform jointly with sets of environmental measures, which are themselves heritable, in prediction models of educational achievement. Here, for the first time, we systematically investigate gene-environment correlation (rGE) and interaction (GxE) in the joint analysis of multiple genome-wide polygenic scores (GPS) and multiple environmental measures as they predict tested educational achievement (EA). We predict EA in a representative sample of 7,026 16-year-olds, with 20 GPS for psychiatric, cognitive and anthropometric traits, and 13 environments (including life events, home environment, and SES) measured earlier in life. Environmental and GPS predictors were modelled, separately and jointly, in penalized regression models with out-of-sample comparisons of prediction accuracy, considering the implications that their interplay had on model performance. Jointly modelling multiple GPS and environmental factors significantly improved prediction of EA, with cognitive-related GPS adding unique independent information beyond SES, home environment and life events. We found evidence for rGE underlying variation in EA (rGE =.38; 95% CIs =.30,.45). We estimated that 40% (95% CIs = 31%, 50%) of the polygenic scores effects on EA were mediated by environmental effects, and in turn that 18% (95% CIs = 12%, 25%) of environmental effects were accounted for by the polygenic model, indicating genetic confounding. Lastly, we did not find evidence that GxE effects significantly contributed to multivariable prediction. Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence

Circulating inflammatory cytokines and risk of five cancers : a Mendelian randomization analysis

Background Epidemiological and experimental evidence has linked chronic inflammation to cancer aetiology. It is unclear whether associations for specific inflammatory biomarkers are causal or due to bias. In order to examine whether altered genetically predicted concentration of circulating cytokines are associated with cancer development, we performed a two-sample Mendelian randomisation (MR) analysis. Methods Up to 31,112 individuals of European descent were included in genome-wide association study (GWAS) meta-analyses of 47 circulating cytokines. Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene (cis), were used as instrumental variables. Inverse-variance weighted MR was used as the primary analysis, and the MR assumptions were evaluated in sensitivity and colocalization analyses and a false discovery rate (FDR) correction for multiple comparisons was applied. Corresponding germline GWAS summary data for five cancer outcomes (breast, endometrial, lung, ovarian, and prostate), and their subtypes were selected from the largest cancer-specific GWASs available (cases ranging from 12,906 for endometrial to 133,384 for breast cancer). Results There was evidence of inverse associations of macrophage migration inhibitory factor with breast cancer (OR per SD = 0.88, 95% CI 0.83 to 0.94), interleukin-1 receptor antagonist with endometrial cancer (0.86, 0.80 to 0.93), interleukin-18 with lung cancer (0.87, 0.81 to 0.93), and beta-chemokine-RANTES with ovarian cancer (0.70, 0.57 to 0.85) and positive associations of monokine induced by gamma interferon with endometrial cancer (3.73, 1.86 to 7.47) and cutaneous T-cell attracting chemokine with lung cancer (1.51, 1.22 to 1.87). These associations were similar in sensitivity analyses and supported in colocalization analyses. Conclusions Our study adds to current knowledge on the role of specific inflammatory biomarker pathways in cancer aetiology. Further validation is needed to assess the potential of these cytokines as pharmacological or lifestyle targets for cancer prevention.Peer reviewe

Genetic analysis of over half a million people characterises C-reactive protein loci

Chronic low-grade inflammation is linked to a multitude of chronic diseases. We report the largest genome-wide association study (GWAS) on C-reactive protein (CRP), a marker of systemic inflammation, in UK Biobank participants (N = 427,367, European descent) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (total N = 575,531 European descent). We identify 266 independent loci, of which 211 are not previously reported. Gene-set analysis highlighted 42 gene sets associated with CRP levels (p ≤ 3.2 ×10−6) and tissue expression analysis indicated a strong association of CRP related genes with liver and whole blood gene expression. Phenome-wide association study identified 27 clinical outcomes associated with genetically determined CRP and subsequent Mendelian randomisation analyses supported a causal association with schizophrenia, chronic airway obstruction and prostate cancer. Our findings identified genetic loci and functional properties of chronic low-grade inflammation and provided evidence for causal associations with a range of diseases

Arthroscopic partial meniscectomy versus placebo surgery for a degenerative meniscus tear : a 2-year follow-up of the randomised controlled trial

Objective To assess if arthroscopic partial meniscectomy (APM) is superior to placebo surgery in the treatment of patients with degenerative tear of the medial meniscus. Methods In this multicentre, randomised, participant-blinded and outcome assessor-blinded, placebo-surgery controlled trial, 146 adults, aged 35-65 years, with knee symptoms consistent with degenerative medial meniscus tear and no knee osteoarthritis were randomised to APM or placebo surgery. The primary outcome was the between-group difference in the change from baseline in the Western Ontario Meniscal Evaluation Tool (WOMET) and Lysholm knee scores and knee pain after exercise at 24 months after surgery. Secondary outcomes included the frequency of unblinding of the treatment-group allocation, participants' satisfaction, impression of change, return to normal activities, the incidence of serious adverse events and the presence of meniscal symptoms in clinical examination. Two subgroup analyses, assessing the outcome on those with mechanical symptoms and those with unstable meniscus tears, were also carried out. Results In the intention-to-treat analysis, there were no significant between-group differences in the mean changes from baseline to 24 months in WOMET score: 27.3 in the APM group as compared with 31.6 in the placebo-surgery group (between-group difference, -4.3; 95% CI, -11.3 to 2.6); Lysholm knee score: 23.1 and 26.3, respectively (-3.2; -8.9 to 2.4) or knee pain after exercise, 3.5 and 3.9, respectively (-0.4; -1.3 to 0.5). There were no statistically significant differences between the two groups in any of the secondary outcomes or within the analysed subgroups. Conclusions In this 2-year follow-up of patients without knee osteoarthritis but with symptoms of a degenerative medial meniscus tear, the outcomes after APM were no better than those after placebo surgery. No evidence could be found to support the prevailing ideas that patients with presence of mechanical symptoms or certain meniscus tear characteristics or those who have failed initial conservative treatment are more likely to benefit from APM.Peer reviewe

Metabolic Traits and Stroke Risk in Individuals of African Ancestry: Mendelian Randomization Analysis.

BACKGROUND AND PURPOSE: Metabolic traits affect ischemic stroke (IS) risk, but the degree to which this varies across different ethnic ancestries is not known. Our aim was to apply Mendelian randomization to investigate the causal effects of type 2 diabetes (T2D) liability and lipid traits on IS risk in African ancestry individuals, and to compare them to estimates obtained in European ancestry individuals. METHODS: For African ancestry individuals, genetic proxies for T2D liability and circulating lipids were obtained from a meta-analysis of the African Partnership for Chronic Disease Research study, the UK Biobank, and the Million Veteran Program (total N=77 061). Genetic association estimates for IS risk were obtained from the Consortium of Minority Population Genome-Wide Association Studies of Stroke (3734 cases and 18 317 controls). For European ancestry individuals, genetic proxies for the same metabolic traits were obtained from Million Veteran Program (lipids N=297 626, T2D N=148 726 cases, and 965 732 controls), and genetic association estimates for IS risk were obtained from the MEGASTROKE study (34 217 cases and 406 111 controls). Random-effects inverse-variance weighted Mendelian randomization was used as the main method, complemented with sensitivity analyses more robust to pleiotropy. RESULTS: Higher genetically proxied T2D liability, LDL-C (low-density lipoprotein cholesterol), total cholesterol and lower genetically proxied HDL-C (high-density lipoprotein cholesterol) were associated with increased risk of IS in African ancestry individuals (odds ratio per doubling the odds of T2D liability [95% CI], 1.09 [1.07-1.11]; per standard-deviation increase in LDL-C, 1.12 [1.04-1.21]; total cholesterol: 1.23 [1.06-1.43]; HDL-C, 0.93 [0.89-0.99]). There was no evidence for differences in these estimates when performing analyses in European ancestry individuals. CONCLUSIONS: Our analyses support a causal effect of T2D liability and lipid traits on IS risk in African ancestry individuals, with Mendelian randomization estimates similar to those obtained in European ancestry individuals

Exploring the causal effect of maternal pregnancy adiposity on offspring adiposity: Mendelian randomisation using polygenic risk scores.

BACKGROUND: Greater maternal adiposity before or during pregnancy is associated with greater offspring adiposity throughout childhood, but the extent to which this is due to causal intrauterine or periconceptional mechanisms remains unclear. Here, we use Mendelian randomisation (MR) with polygenic risk scores (PRS) to investigate whether associations between maternal pre-/early pregnancy body mass index (BMI) and offspring adiposity from birth to adolescence are causal. METHODS: We undertook confounder adjusted multivariable (MV) regression and MR using mother-offspring pairs from two UK cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB). In ALSPAC and BiB, the outcomes were birthweight (BW; N = 9339) and BMI at age 1 and 4 years (N = 8659 to 7575). In ALSPAC only we investigated BMI at 10 and 15 years (N = 4476 to 4112) and dual-energy X-ray absorptiometry (DXA) determined fat mass index (FMI) from age 10-18 years (N = 2659 to 3855). We compared MR results from several PRS, calculated from maternal non-transmitted alleles at between 29 and 80,939 single nucleotide polymorphisms (SNPs). RESULTS: MV and MR consistently showed a positive association between maternal BMI and BW, supporting a moderate causal effect. For adiposity at most older ages, although MV estimates indicated a strong positive association, MR estimates did not support a causal effect. For the PRS with few SNPs, MR estimates were statistically consistent with the null, but had wide confidence intervals so were often also statistically consistent with the MV estimates. In contrast, the largest PRS yielded MR estimates with narrower confidence intervals, providing strong evidence that the true causal effect on adolescent adiposity is smaller than the MV estimates (Pdifference = 0.001 for 15-year BMI). This suggests that the MV estimates are affected by residual confounding, therefore do not provide an accurate indication of the causal effect size. CONCLUSIONS: Our results suggest that higher maternal pre-/early-pregnancy BMI is not a key driver of higher adiposity in the next generation. Thus, they support interventions that target the whole population for reducing overweight and obesity, rather than a specific focus on women of reproductive age