Diet and lifestyle have changed dramatically in the last few decades, leading to an increase in prevalence of obesity, defined as a body mass index >30Kg/m2, dyslipidaemias (defined as abnormal lipid profiles) and type 2 diabetes (T2D). Together, these cardiometabolic traits and diseases, have contributed to the increased burden of cardiovascular disease, the leading cause of death in Western societies. Complex traits and diseases, such as cardiometabolic traits, arise as a result of the interaction between an individual's predisposing genetic makeup and a permissive environment. Since 2007, genome-wide association studies (GWAS) have been successfully applied to complex traits leading to the discovery of thousands of trait-associated variants. Nonetheless, much is still to be understood regarding the genetic architecture of these traits, as well as their underlying biology. This thesis aims to further explore the genetic architecture of cardiometabolic traits by using complementary approaches with greater genetic and phenotype resolution, ranging from studying clinically ascertained extreme phenotypes, deep molecular profiling, or sequence level data. In chapter 2, I investigated the genetic architecture of healthy human thinness (N=1,471) and contrasted it to that of severe early onset childhood obesity (N=1,456). I demonstrated that healthy human thinness, like severe obesity, is a heritable trait, with a polygenic component. I identified a novel BMI-associated locus at PKHD1, and found evidence of association at several loci that had only been discovered using large cohorts with >40,000 individuals demonstrating the power gains in studying clinical extreme phenotypes. In chapter 3, I coupled high-resolution nuclear magnetic resonance (NMR) measurements in healthy blood donors, with next-generation sequencing to establish the role of rare coding variation in circulating metabolic biomarker biology. In gene-based analysis, I identified ACSL1, MYCN, FBXO36 and B4GALNT3 as novel gene-trait associations (P < 2.5x10-6). I also found a novel link between loss-of-function mutations in the "regulation of the pyruvate dehydrogenase (PDH) complex" pathway and intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL) and circulating cholesterol measurements. In addition, I demonstrated that rare "protective" variation in lipoprotein metabolism genes was present in the lower tails of four measurements which are CVD risk factors in this healthy population, demonstrating a role for rare coding variation and the extremes of healthy phenotypes. In chapter 4, I performed a genome-wide association study of fructosamine, a measurement of total serum protein glycation which is useful to monitor rapid changes in glycaemic levels after treatment, as it reflects average glycaemia over 2-3 weeks. In contrast to HbA1c, which reflects average glucose concentration over the life-span of the erythrocyte (~3 months), fructosamine levels are not predicted to be influenced by factors affecting the erythrocyte. Surprisingly, I found that in this dataset fructosamine had low heritability (2% vs 20% for HbA1c), and was poorly correlated with HbA1c and other glycaemic traits. Despite this, I found two loci previously associated with glycaemic or albumin traits, G6PC2 and FCGRT respectively (P < 5x10-8), associated with fructosamine suggesting shared genetic influence. Altogether my results demonstrate the utility of higher resolution genotype and phenotype data in further elucidating the genetic architecture of a range of cardiometabolic traits, and the power advantages of study designs that focus on individuals at the extremes of phenotype distribution. As large cohorts and national biobanks with sequencing and deep multi-dimensional phenotyping become more prevalent, we will be moving closer to understanding the multiple aetiological mechanisms leading to CVD, and subsequently improve diagnosis and treatment of these conditions.

Glycosylation is among the most common post-translational protein modifications. Glycans are complex carbohydrates attached to the surface of many proteins, but are rarely extensively studied in a high-throughput manner. However, there is an increasing evidence of their involvement in various physiological processes and diseases. Glycosylation of Immunglobulin G was shown to be important in adaptive immunity, where it can act as a "safety switch" for different types of the immune response. Although the main enzymes of the glycosylation pathway are known, little is understood about how this template-independent process is regulated to result in a faithful synthesis of a specific glycoform. This question was previously addressed using genome-wide association studies (GWAS) and 9 loci were identified as being significantly associated with IgG N-glycosylation. Only 4 of these loci were the known glycosylation enzymes. An additional five loci were discovered by applying a newly developed multivariate GWAS method on the same dataset. Here, by performing a GWAS on 77 IgG N-glycan traits measured by ultra-performance liquid chromatography in more than 8000 samples from four European cohorts the number of genome-wide significant (p? ≤ 2.4 x 10−9) loci increased to 27, 15 of which are novel, with 6 additional loci being suggestively associated (p? ≤ 2.4 x 10−8). To assess which of the genes from the associated loci are more likely to be regulating IgG glycosylation, different gene prioritising strategies were employed. For 7 loci evidence of a non-synonymous amino acid change was found, two of which were predicted to be deleterious. Evidence of regulation through changes in gene expression levels in B-cells, the cell lineage responsible for production of IgG, was found for 4 genes, with an additional 11 genes exhibiting the same evidence with expression in peripheral blood or other immune cells. For the remaining loci the most likely candidate gene was proposed based on co-expression with genes from the enriched gene-sets or based on a physical proximity to the variant with the strongest association. To narrow down the most important loci for a functional follow-up, the omics nature of this data was used to compare glycome-wide SNP effects and suggest how newly discovered loci form a functional network that regulates the established members of the glycosylation pathway. The potential role of IgG glycosylation in various complex traits and diseases was explored by assessing the pleiotropy of the associated SNPs. The inflation of SNPs related to autoimmune, digestive and neurological diseases was observed in glycosylation SNPs. To assess whether IgG N-glycosylation is likely to share the same causal variant as the identified pleiotropic traits and diseases, regional association patterns were compared using summary data based Mendelian Randomisation analyses. This work demonstrates that an increased sample size empowered the identification of novel loci, enabling further insights into the molecular mechanisms underlying protein glycosylation and its relationship with complex human diseases. It also shows that such analyses of omic traits can assist in creating a functional network of the identified loci, prioritising the most important genes and allowing a more focused approach to future experimental functional follow-up.

Data from a Croatian isolate population are analysed in a genome-wide association study (GWAS) for a variety of disease-related quantitative traits. A novel genomewide approach to analysing pedigree-based association data called GRAMMAR is utilised. One of the significant findings, for uric acid, is followed up in greater detail, and is replicated in another isolate population, from Orkney. The associated SNPs are located in the SLC2A9 gene, coding for a known glucose transporter, which leads to identification of SLC2A9 as a urate transporter too (Vitart et al., 2008). These SNPs are later implicated in affecting gout, a disease known to be linked with high serum uric acid levels, in an independent study (Dehghan et al., 2008). Subsequently, investigation into different ways in which to use SNP data to identify quantitative trait loci (QTL) for genome-wide association (GWA) studies is performed. Several multi-marker approaches are compared to single SNP analysis using simulated phenotypes and real genotype data, and results show that for rare variants haplotype analysis is the most effective method of detection. Finally, the multi-marker methods are compared with single SNP analysis on the real uric acid data. Interpretation of real data results was complicated due to low sample size, since only founder and unrelated individuals may be used for population-based haplotype analysis, nonetheless, results of the prior analyses of simulated data indicate that multi-marker methods, in particular haplotypes, may greatly facilitate detection of QTL with low minor allele frequency in GWA studies.

<p></p><p>Sorghum (<i>Sorghum bicolor) </i>is a well-known agronomic crop of global importance. The demand for sorghum as a food crop makes it the fifth most important cereal in the world. The grain of sorghum is utilized for food and feed, whereas the sorghum biomass may have many other uses such as for fodder, bioenergy or even for construction. Globally, sorghum is consumed as a food crop and used for home construction primarily in the developing world. The grain and biomass yield of sorghum is drastically reduced by the parasitic plant <i>Striga hermonthica </i>which is endemic to Sub-Saharan Africa. To date, only one sorghum gene, <i>LGS1</i>, has been characterized as a genetic mechanism that reduces <i>S. hermonthica</i> parasitism by altering the strigolactone composition of the host root exudates which results in a reduction of the parasites ability to germinate. To establish more durable resistance additional genetic variation needs to be identified that reduces the <i>S. hermonthica </i>parasitism in sorghum, but also reduces the parasitic weed seed bank by promoting suicidal germination. To that end, the PP37 multi-parent advanced generation inter-cross (MAGIC) population was developed, originally as a recurrent selection population that was developed to recombine sorghum accessions with different putative resistance mechanisms to <i>S. hermonthica. </i>Whole genome sequences were developed for approximately 1,006 individuals of the PP37 MAGIC population. The population was phenotyped for <i>S. hermonthica </i>resistance during the 2016 and 2017 growing season in Northwestern Ethiopia. There was significant spatial variation in the <i>S. hermonthica </i>natural infestations that were partially attenuated for with artificial inoculation. The data was used to conduct a genome-wide association study that detected several subthreshold peaks, including the previously mapped <i>LGS1. </i>The highly quantitative nature of <i>S. hermonthica </i>resistance confounded with the complex spatial variation in the parasite infestations across a given location make it difficult to detect highly heritable variation across years and environments. </p> <p> In addition to <i>S. hermonthica </i>resistance, the plant architecture of the PP37 MAGIC was also assessed at a location in Northwestern Ethiopia that is free of the parasite, as it significantly reduces plant height. To asses plant architecture the total plant height, the height of the panicle base, flag leaf height, and pre-flag leaf height were collected using a relatively high-throughput barcoded measurement system. Sorghum head exertion and panicle length were derived from this data. The actual measures of plant architecture and the derived traits were used to conduct a genome-wide association study. The high heritability of this trait demonstrated the statistical power of the PP37 mapping population. Highly significant peaks were detected that resolved the <i>dwarf3</i> locus and an uncharacterized qHT7.1 that had only been previously resolved using a recombinant inbred line population. Furthermore, a novel significant locus was associated with exertion on chromosome 1. The random mating that was utilized to develop the PP37 MAGIC has broken the population structure that when present can hinder our ability associate regions of the genome to a given phenotype. As a result, novel candidate gene lists have been developed as an outcome of this research that refined the potential genes that need to be explored to validate qHT7.1 and the novel association on chromosome 1. </p> <p>This research demonstrated the power of MAGIC populations in determining the genomic regions that influence complex phenotypes, that facilitates future work in sorghum genetic improvement through plant breeding. This research however also demonstrates a large international research effort. The nuisances and lessons learned while conducting this international research project are also discussed to help facilitate and guide similar research projects in the future. The broader impacts of this research on the society at large are also discussed, to highlight the unique potential broader impacts of international research in the plant sciences. The broader impacts of this research include germplasm development and extensive human capacity building in plant breeding genetics for developing country students and aspiring scientists. Overall this research attempts to serve as a model for highlighting the interdisciplinary nature and complexity of conducting international plant science research, while also making significant strides in improving our understanding the genetic architecture of quantitative traits of agronomic importance in sorghum.</p><br><p></p>

No description available.

Plasma lipid, lipoprotein and apolipoprotein levels are considered as important and well-established intermediate quantitative phenotypes of Cardiovascular Disease (CVD) risk. Both the mean values and the phenotypic variance vary over the human lifespan. However, it is not known whether there is a genetic basis for this age variability. For example, might different genes act, or different gene interactions occur, as a person ages? If so, how might this be influenced by both environment and phenotype? An understanding of traits at different ages will not only provide insight into the genetic components involved in CHD development, but may also identify additional genetic factors that predispose an individual or population to premature (and later-onset) CHD. By identifying genetic factors that account for variation in important intermediate traits (i.e. lipid levels), we hope to gain a better understanding of disease mechanisms and thus a better chance of developing clinical strategies for preventing or possibly treating abnormal lipid levels and, by association, CHD. The aim of this thesis was to better identify and explain the genetic basis of CHD by focusing on the use of lipid traits as intermediate quantitative phenotypes of CHD. First, phenotypic analyses using structural equation modeling were performed to estimate the relative importance of genetic and environmental factors, and also to investigate whether these traits are influenced by the same gene(s) across time or whether they are age-specific genetic effects. Then, genome-wide linkage analysis was performed to localize cardiovascular susceptibility loci. Finally, a small genome-wide association scan (GWAS) was performed on a subset of the data to identify the relevant variants, in particular those showing associations across time. Phenotypes and marker data were collected in two Australian samples: an adolescent and adult twin pair samples. The adult sample consisted of 1453 twin pairs (968 monozygotic and 485 dizygotic), measured for lipid traits. 415 adult twins provided blood on two to five occasions. The adolescent dataset consisted of 965 twin families (397 monozygotic and 568 dizygotic) measured longitudinally at ages twelve, fourteen and sixteen, and their siblings tested once for the same lipid variables. Results from both the adult and adolescent cohorts indicated that there is more than one genetic factor influencing total cholesterol, HDL, LDL and triglycerides over time (i.e. from different measurement occasions). Common environmental factors did not contribute to variances (except for HDL in adolescents). There were no sex differences in the heritabilities of these intermediate phenotypes. Non-shared environmental factors did not have significant long-term effects. Overall, these two cohorts confirmed that genetic variation contributes substantially to variation in these traits, and suggested there are changes in the genes affecting plasma lipid concentration at different periods of life. Thus, there are age-dependent gene effects influencing HDL, LDL, total cholesterol, or triglycerides at different ages. In the adult genome data, there were 485 adult dizygotic twin pairs typed on average 595 markers, at an average inter-marker distance of 5.0 cM. The genome-wide linkage analysis revealed evidence for linkage in the 7p13 region for triglycerides. Possible candidate genes included NPC1L1 and GSBS. Other regions of “suggestive” linkage identified were chromosome 4p13 (at 62 cM) and Xq26.2-28 (81 cM). Adolescent twins and their siblings from 760 families were typed for linkage using 16,781 markers spaced across the genome at an average distance of 6.25 cM. The adolescent data revealed evidence for linkage to region 6p24.3 for triglycerides (–log10p = 6.81; equivalent LOD = 6.13; p = 0.00000016) and to region 2q31.1 for HDL (–log10p = 3.22, equivalent LOD = 2.27; p = 0.00061). No obvious candidate gene is known in this 6p region. Possible candidate genes in the 2q region include LRP2 and ABCB11. A significant region of linkage was also found on 2q35 for LDL (–log10p = 5.59; equivalent LOD score = 4.53). Other interesting regions of linkage included chromosomes 1q32.1, 4p15.1, 5q13.2, 11p14.3 and 18q11.2. Thus, regions were identified by linkage analyses that are likely to harbour genetic risk factors for cardiovascular disease in the analysed Australian population: chromosomes 7p13 (in adults), 6p24 (adolescents), 2q31.1 and 2q35 (in adolescents). Other regions included 1q32.1, 4p15.1, 5q13.2, 11p14.3 and 18q11.2 in adolescents and chromosome 4p13 and Xq26.2–28 in adults. Genome-wide association results for adolescents showed significant evidence of association between total cholesterol at age 14 (p = 8.24x10-7) and rs10503840 on 8p21.1. Such association has not previously been reported. Evidence of differential association across time was also found between HDL and variant rs10492859, located in the intron of the CDH13 gene, consistent with earlier studies on larger datasets. Significant association (p = 2.25x10-6) was also found between rs10507266 on 12q24.21 in an intron of THRAP2, a gene involved in early development of heart and brain, with triglycerides at age 12. Evidence of association was also found between HDL across time and variant rs10492859 on 16q23. Several other “suggestive” potential loci associated with lipid traits at one time point as well as across time were also found. In conclusion, the work described in this thesis establishes the importance of age-specific genetic effects on plasma lipids and lipoproteins, and identifies several regions of highly significant genetic linkage with these phenotypes in either adolescence or adulthood. It is clear that, as well as cross-sectional studies to identify genes affecting CVD risk factors, longitudinal genetic linkage and association studies are needed to assess relative contributions to risk across the lifespan.

Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 5857. Adviser: Momiao Xiong. Includes bibliographical references (leaves xx-xx).

Thesis (MSc)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Described as one of the costliest and most debilitating disorders, schizophrenia has proven to be among the greatest challenges for medical researchers. The disorder poses difficulties on all levels: from genotype to phenotype. Even though it is known that there is a substantial genetic contribution to schizophrenia susceptibility (~80%), it is unknown whether this is due to common variants, rare variants, epigenetic factors, polymorphisms in regulatory regions of the genome or a combination of all these factors. Over the past few decades, many approaches have been employed to elucidate the genetic architecture of schizophrenia, with the latest and most promising being genome wide association studies (GWAS). However, nearly a decade after the first GWAS, the limitations are increasingly being recognised and new avenues need to be explored. Studies have recently started to focus on the analysis of non-coding regions of the genome since these regions harbour the majority of variants identified in GWAS thus far. This study aimed to use recently developed programs that utilize data from large scale studies such as previous GWAS, the Encyclopaedia of DNA Elements (ENCODE), 1000 Genomes, HapMap and Functional Annotation of the Mammalian Genome (FANTOM) to establish a simple, yet effective bioinformatics pipeline for the identification and assessment of variants in regulatory regions. Using the established workflow, 149 single nucleotide polymorphisms (SNPs) in regulatory regions were implicated in schizophrenia susceptibility, with the most significant SNP being rs200981. Pathway and network analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and GeneMANIA respectively indicated that the most frequently affected genes were involved in immune responses or neurodevelopmental processes, which support previous findings. Yet, novel findings of this study implicated processes crucial for DNA packaging (from DNA level to chromatin level). The second part of the study used restriction fragment length polymorphism analysis of polymerase chain reaction-amplified fragments (PCR-RFLP) to genotype ten of the most significant SNPs (identified by bioinformatic analyses in the first part of the study) in a South African Xhosa cohort of 100 cases and 100 controls, while bi-directional Sanger sequencing was used to confirm the presence of these SNPs. Statistical analyses revealed two haplotypes of regulatory variants, rs200483-rs200485-rs2517611 (p = 0.0385; OR = 1.71; 95% CI = 1.01-2.91) and rs200981-rs2517611-rs3129701 (p = 0.041; OR = 0.51; 95% CI = 0.27-0.98) associated with schizophrenia susceptibility. Bioinformatic analysis indicated that these haplotypes affect DNA packaging, which supported the findings of the first part of the study and could implicate epigenetic processes. The findings of this study support the importance of regulatory variants in schizophrenia susceptibility. This study also showed the importance of combining GWAS data with additional analyses in order to better understand complex diseases. It is hoped that these findings could fuel future research, specifically in genetically unique populations. / AFRIKAANSE OPSOMMING: Skisofrenie kan beskryf word as een van die duurste en mees ernstige siektes en bly steeds een van die grootste uitdagings vir mediese navorsers. Hierdie versteuring behels probleme op alle vlakke: van genotipe tot fenotipe. Alhoewel dit bekend is dat daar 'n aansienlike genetiese bydrae tot skisofrenie vatbaarheid is (~ 80%), is dit onbekend of dit is as gevolg van algemene variasies, skaars variasies, epigenetiese faktore, variasies in regulerende gebiede van die genoom of 'n kombinasie van al hierdie faktore. Oor die afgelope paar dekades is verskeie benaderings gebruik om die genetiese samestelling van skisofrenie te bestudeer, met die nuutste en mees belowende synde genoom-wye assosiasie studies (GWAS). Byna 'n dekade na die eerste GWAS, word die beperkinge egter toenemend erken en nuwe navorsingstrategieë moet gebruik word. Studies het onlangs begin om meer te fokus op die analise van nie-koderende areas van die genoom aangesien hierdie areas die meerderheid van die variasies behels wat tot dusver in GWAS geïdentifiseer is. Hierdie studie het gepoog om onlangs ontwikkelde programme, wat gebruik maak van die data van grootskaalse studies soos vorige GWAS, die “Encyclopaedia of DNA Elements” (ENCODE), “1000 Genomes”, “HapMap” en “Functional Annotation of the Mammalian Genome” (FANTOM), te implementeer om sodoende 'n eenvoudige, maar doeltreffende bioinformatika pyplyn vir die identifisering en evaluering van variante in regulerende gebiede, te vestig. Deur die gebruik van die gevestigde bioinformatika pyplyn, is 149 enkel nukleotied polimorfismes (SNPs) in regulerende gebiede in skisofrenie vatbaarheid betrek, met rs200981 wat die mees betekenisvol was. Pad- en netwerk-analise met die onderskeidelike hulp van die “Database for Annotation, Visualization and Integrated Discovery” (DAVID) en “GeneMANIA”, het aangedui dat die gene wat die meeste geaffekteer was, betrokke is by immuunreaksies en neuro-ontwikkeling. Hierdie bevindinge ondersteun vorige studies. Tog het nuwe bevindinge van hierdie studie prosesse geïmpliseer wat uiters noodsaaklik is vir DNS verpakking (van DNS- tot chromatien-vlak). Die tweede deel van die studie het restriksie fragment lengte polimorfisme analise van polimerase ketting reaksie geamplifiseerde fragmente (PKR-RFLP) gebruik om tien van die belangrikste SNPs (wat geïdentifiseer is deur bioinformatiese ontledings in die eerste deel van die studie) in `n Suid-Afrikaanse Xhosa studiegroep van 100 skisofrenie gevalle en 100 kontroles te genotipeer, terwyl tweerigting Sanger volgordebepaling gebruik is om die teenwoordigheid van hierdie SNPs te bevestig. Statistiese analise het aangedui dat twee / National Research Foundation (DAAD-NRF)

Genome-wide association studies (GWAS) has played an import role in identifying genetic variants underlying human complex traits. However, its success is hindered by weak effect at causal variants and noise at non-causal variants. Penalized regression can be applied to handle GWAS problems. GWAS data has some specificities. Consecutive genetic markers are usually highly correlated due to linkage disequilibrium. This thesis introduces a moving-window penalized method for GWAS which smooths the effects of consecutive SNPs. Simulation studies indicate that this penalized moving window method provides improved true positive findings. The practical utility of the proposed method is demonstrated by applying it to Genetic Analysis Workshop 16 Rheumatoid Arthritis data. Next, the moving-window penalty is applied on generalized linear model. We call such an approach as smoothed lasso (SLasso). Coordinate descent computing algorithms are proposed in details, for both quadratic and logistic loss. Asymptotic properties are discussed. Then based on SLasso, we discuss a two-stage method called MW-Ridge. Simulation results show that while SLasso can provide more true positive findings than Lasso, it has a side-effect that it includes more unrelated random noises. MW-Ridge can eliminate such a side-effect and result in high true positive rates and low false detective rates. The applicability to real data is illustrated by using GAW 16 Rheumatoid Arthritis data. The SLasso and MW-Ridge approaches are then generalized to multivariate response data. The multivariate response data can be transformed into univariate response data. The causal variants are not required to be the same for different response variables. We found that no matter how the causal variants are matched, being fully matched or 60% matched, MW-Ridge can always over perform Lasso by detecting all true positives with lower false detective rates.

LED and HS have been funded by startup funds from Stanford and a pilot grant to LED from the Stanford Center for Clinical and Translation Research and Education (UL1 TR001085, PI Greenberg). LED has also been funded by Cohen Veterans Bioscience (CVB), and she is part of the CVB Working Group for PTSD Adaptive Platform Trial. BG has been funded by the NIH (R01-HD-059835, PI Williams) and CVB. HH has been funded by the NIH (NIH K01DK114379 and NIH R21AI139012), the Zhengxu and Ying He Foundation, and the Stanley Center for Psychiatric Research. MBR received funds from WPA Congress Mexico City 2018, Guayaquil CEPAM 2019, Asunción X CONGRESO LATINOAMERICANO DE LA FLAPB 2018, Guayaquil 2019 (Bago), and Lancet Psychiatry, London (commission on Violence against women) 2019. SS declares no potential conflict of interest. / Genome-wide approaches including polygenic risk scores (PRSs) are now widely used in medical research; however, few studies have been conducted in low- and middle-income countries (LMICs), especially in South America. This study was designed to test the transferability of psychiatric PRSs to individuals with different ancestral and cultural backgrounds and to provide genome-wide association study (GWAS) results for psychiatric outcomes in this sample. The PrOMIS cohort (N = 3308) was recruited from prenatal care clinics at the Instituto Nacional Materno Perinatal (INMP) in Lima, Peru. Three major psychiatric outcomes (depression, PTSD, and suicidal ideation and/or self-harm) were scored by interviewers using valid Spanish questionnaires. Illumina Multi-Ethnic Global chip was used for genotyping. Standard procedures for PRSs and GWAS were used along with extra steps to rule out confounding due to ancestry. Depression PRSs significantly predicted depression, PTSD, and suicidal ideation/self-harm and explained up to 0.6% of phenotypic variation (minimum p = 3.9 × 10−6). The associations were robust to sensitivity analyses using more homogeneous subgroups of participants and alternative choices of principal components. Successful polygenic prediction of three psychiatric phenotypes in this Peruvian cohort suggests that genetic influences on depression, PTSD, and suicidal ideation/self-harm are at least partially shared across global populations. These PRS and GWAS results from this large Peruvian cohort advance genetic research (and the potential for improved treatments) for diverse global populations. / National Institutes of Health / Revisión por pares