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The Global Globin Network (GGN) is a project-wide initiative of the Human Variome/Global Variome Project (HVP) focusing on haemoglobinopathies to build the capacity for genomic diagnosis, clinical services, and research in low- and middle-income countries. At present, there is no framework to evaluate the improvement of care, treatment, and prevention of thalassaemia and other haemoglobinopathies globally, despite thalassaemia being one of the most common monogenic diseases worldwide. Here, we propose a universally applicable system for evaluating and grouping countries based on qualitative indicators according to the quality of care, treatment, and prevention of haemoglobinopathies. We also apply this system to GGN countries as proof of principle. To this end, qualitative indicators were extracted from the IthaMaps database of the ITHANET portal, which allowed four groups of countries (A, B, C, and D) to be defined based on major qualitative indicators, supported by minor qualitative indicators for countries with limited resource settings and by the overall haemoglobinopathy carrier frequency for the target countries of immigration. The proposed rubrics and accumulative scores will help analyse the performance and improvement of care, treatment, and prevention of haemoglobinopathies in the GGN and beyond. Our proposed criteria complement future data collection from GGN countries to help monitor the quality of services for haemoglobinopathies, provide ongoing estimates for services and epidemiology in GGN countries, and note the contribution of the GGN to a local and global reduction of disease burden.

Abstract The CAPP2 trial investigated the long-term effects of aspirin and resistant starch on cancer incidence in patients with Lynch syndrome (LS). Participants with LS were randomized double-blind to 30 g resistant starch (RS) daily or placebo for up to 4 years. We present long-term cancer outcomes based on the planned 10-year follow-up from recruitment, supplemented by National Cancer Registry data to 20 years in England, Wales, and Finland. Overall, 463 participants received RS and 455 participants received placebo. After up to 20 years follow-up, there was no difference in colorectal cancer incidence (n = 52 diagnosed with colorectal cancer among those randomized to RS against n = 53 on placebo) but fewer participants had non-colorectal LS cancers in those randomized to RS (n = 27) compared with placebo (n = 48); intention-to-treat (ITT) analysis [HR, 0.54; 95% confidence interval (CI), 0.33–0.86; P = 0.010]. In ITT analysis, allowing for multiple primary cancer diagnoses among participants by calculating incidence rate ratios (IRR) confirmed the protective effect of RS against non–colorectal cancer LS cancers (IRR, 0.52; 95% CI, 0.32–0.84; P = 0.0075). These effects are particularly pronounced for cancers of the upper GI tract; 5 diagnoses in those on RS versus 21 diagnoses on placebo. The reduction in non–colorectal cancer LS cancers was detectable in the first 10 years and continued in the next decade. For colorectal cancer, ITT analysis showed no effect of RS on colorectal cancer risk (HR, 0.92; 95% CI, 0.62–1.34; P = 0.63). There was no interaction between aspirin and RS treatments. In conclusion, 30 g daily RS appears to have a substantial protective effect against non–colorectal cancer cancers for patients with LS. Prevention Relevance: Regular bowel screening and aspirin reduce colorectal cancer among patients with LS but extracolonic cancers are difficult to detect and manage. This study suggests that RS reduces morbidity associated with extracolonic cancers. See related Spotlight, p. 557

Background: The heritabilities of bipolar disorder (BD) and borderline personality disorder (BPD) are 80% and 65%, respectively, indicating substantial genetic contributions to both disorders. BD and BPD are often comorbid, and both disorders have a polygenic architecture. These variants are thought to subtly affect multiple pathways, associated with structural brain abnormalities commonly observed in patients with BD and BPD. Brain regions have been shown to be highly heritable and under distinct genetic influences. However, the overlap in genetic risk between BD and BPD and altered brain regions, respectively, has not yet been determined. Aims and Objectives: The aim of this project was to determine whether genetic risk for BD and BPD overlaps with genetic risk for altered brain regions. Methods: Genome-wide association study (GWAS) summary statistics for BD (Ncases=20,352; Ncontrols= 31,358), BPD (Ncase=998; Ncontrol=1,545), eight subcortical brain volumes (nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen and thalamus) and intracranial volume (ICV) (N=27,087), and cortical surface area and thickness (N=37,479) were obtained. Pleiotropy and concordance were assessed using SNP-Effect Concordance Analysis. Conditional false discovery rate (cFDR) was used to condition BD and BPD GWAS results on genetic variants that influence brain regions. Linkage Disequilibrium Score Regression was used to examine genome-wide correlations between BD, BPD and brain regions. Mendelian randomization was used to test for causal associations between BD, BPD and each brain region, respectively. Results: There was evidence of significant pleiotropy and positive concordance between BD and BPD (ppleiotropy=5x10-4; pconcordance=1x10-6, OR=1.29). Significant pleiotropy was observed between BD and the thickness of several cortical regions and two gyri, namely the lateral occipital (p=2.25x10-5), pars triangularis (p=1.1x10-4), rostral anterior cingulate regions (p=2.18x10-4) and post central (p=7.9x10-6) and supramarginal gyri (p=1.45x10-7). Significant positive concordance was noted between BPD and thickness of the lateral occipital region (p=3x10-4; OR=1.02). After conditioning BD onto BPD and each regional brain GWAS, 171 additional variants were significantly associated with BD (FDR

OBJECTIVE: Circadian rhythm disruption is commonly observed in bipolar disorder (BD). Daylight is the most powerful signal to entrain the human circadian clock system. This exploratory study investigated if solar insolation at the onset location was associated with the polarity of the first episode of BD I. Solar insolation is the amount of electromagnetic energy from the Sun striking a surface area of the Earth.METHODS: Data from 7488 patients with BD I were collected at 75 sites in 42 countries. The first episode occurred at 591 onset locations in 67 countries at a wide range of latitudes in both hemispheres. Solar insolation values were obtained for every onset location, and the ratio of the minimum mean monthly insolation to the maximum mean monthly insolation was calculated. This ratio is largest near the equator (with little change in solar insolation over the year), and smallest near the poles (where winter insolation is very small compared to summer insolation). This ratio also applies to tropical locations which may have a cloudy wet and clear dry season, rather than winter and summer.RESULTS: The larger the change in solar insolation throughout the year (smaller the ratio between the minimum monthly and maximum monthly values), the greater the likelihood the first episode polarity was depression. Other associated variables were being female and increasing percentage of gross domestic product spent on country health expenditures. (All coefficients: P ≤ 0.001).CONCLUSION: Increased awareness and research into circadian dysfunction throughout the course of BD is warranted.

African populations are vastly underrepresented in genetic studies despite having the most genetic variation globally and facing wide-ranging environmental exposures. Most of these studies have been conducted in populations of European (EUR) ancestry using GWAS arrays that represent the genetic variation in these populations. Thus, the prediction accuracy of polygenic risk scores (PRS) derived from EUR ancestry populations is less accurate in populations of non-European ancestry, and least accurate in African (AFR) ancestry populations. The extent to which PRS prediction accuracy varies within AFR ancestry populations has not, however, been previously investigated. This study had two aims: the first was to investigate the contribution of common variants to the risk of schizophrenia in the South African Xhosa (SAX) population through genome-wide association study (GWAS) analysis, and to determine if PRS derived from EUR and East Asian (EAS) ancestry populations from the Psychiatric Genomics Consortium (PGC) Schizophrenia Working Group were generalizable to SAX. The second aim was to assess the generalizability of PRS for non-psychiatric phenotypes that were derived from EUR ancestry individuals from the UK Biobank (UKB, n = ~350,000) in the Uganda General Population Cohort (GPC, n = 4,778) and the South African Drakenstein Child Health Study (DHCS, n = 638). To address the first aim, a GWAS was conducted in 2,086 Xhosa individuals from South Africa with and without schizophrenia (ncases = 1,038; ncontrols = 1,048) using a custom-designed Affymetrix GWAS array designed to capture variation in the Xhosa population. The schizophrenia GWAS in SAX yielded one SNP (rs35172303 ; P = 4.74e-08, OR = 0.6004, 95%CI:[0.499,0.721]) in ZFP3 that met genome-wide significance. The association of variants in ZFP3 from the schizophrenia GWAS is consistent with those from an earlier exomesequence study in SAX undertaken by colleagues, but this gene has not previously been associated with schizophrenia in large-scale schizophrenia GWAS of predominantly EUR ancestry. After characterizing the genetic architecture of schizophrenia in SAX, it was found that the heritability was enriched across functional categories involved in the regulation of gene expression. Then, the accuracy of PRS derived from PGC Schizophrenia Working Group from both EUR and EAS ancestries in predicting schizophrenia in SAX was quantified. There was low PRS prediction accuracy using PGC-derived summary statistics in SAX (PGC-EUR: max R2 = 0.0057, P = 0.008; PGC-EAS: max R2 = 0.0059, P = 0.007). These findings are consistent with previous findings that showed that PRS predication accuracy is low when discovery and target cohorts come from different ancestral backgrounds. For the second aim, PRS prediction accuracy was quantified in simulations using data from the African Genome Variation project (AGVP) to represent continental AFR diversity. Samples were categorised by geographical region into West, East and South Africa cohorts. Each cohort was divided into a discovery and target datasets. The West and East African discovery data was used to predict the simulated phenotype in the three target cohorts. Using UKB EUR ancestry individuals, PRS prediction accuracy was assessed for 34 anthropometric and blood panel traits in the Uganda GPC, and then meta-analysed UKB with PAGE (Population Architecture using Genomics and Epidemiology, comprising about 50,000 Latino/Hispanic and African-American individuals) and BBJ (Biobank Japan, n = ~162,000) to assess how the inclusion of diverse sample impacts PRS prediction accuracy. Simulations were limited by sample size but showed that PRS prediction accuracy was highest when the discovery and target cohorts were matched by African region, and for phenotypes with the sparsest genetic architecture. Using empirical data from UKB and the Uganda GPC, a low prediction accuracy was observed across all 34 quantitative traits in GPC when using GWAS data from UKB. There was differential prediction accuracy across AFR ancestry groups within UKB, i.e. the prediction accuracy was highest for the Ethiopian and admixed populations, and lowest for southern African populations. When comparing PRS prediction accuracy of East African individuals from the UKB to that of individuals from GPC, the prediction accuracy was lowest in the Ugandan GPC population, indicating that the difference in environments between the two groups may be contributing to the difference in PRS accuracy. Moreover, the cross-ancestry meta-analyses showed that the inclusion of diverse samples in large scale studies improves PRS prediction accuracy, most especially for phenotypes with population-enriched variants. It was demonstrated for the first time in this thesis that EUR ancestry-derived PRS prediction accuracy varied within continental AFR ancestry groups, and tracks with population history and the evolution of humans. The higher prediction accuracy observed in Ethiopians can be explained by their genetic proximity to Europeans as a result of the back to Africa migration, whereas the southern African populations (including SAX) are more proximal to the ancestral populations that never left the continent. It is therefore imperative to not only include more African samples in future large-scale studies, but to have samples that adequately represent the genetic and environmental diversity on the African continent.