Development of bioinformatic tools and application of novel statistical methods in genome-wide analysis

Een genoom-brede associatie studie is een methode die genen betrokken bij complexe fenotypes identificeert door het hele genoom te scannen. Deze studies leggen over het algemeen de nadruk op kwantiteit in plaats van kwaliteit: eenvoudige statistische methodes worden toegepast op genetische data van een grote groep mensen. In dit proefschrift onderzochten wij methodes om de kwaliteit van deze methoden te verbeteren. Ten eerste ontwikkelden we software voor het uitvoeren van een automatische en volledige kwaliteitscontrole over de resultaten van zulke analyses. Ten tweede pasten we survival-analyse toe op zowel time-to-event fenotypes (in dit geval: de leeftijd waarop iemand cannabis gaat gebruiken) en biomarkers met een detectielimiet. Ten derde gebruikten we een nieuwe, volledigere referentie van het genoom waarmee we 10 nieuwe genen voor nierziekte hebben gevonden. Het tweede deel van dit proefschrift richtte zich op het ontleden van de erfelijkheid. Hiertoe onderzochten wij voor 32 complexe fenotypes (waaronder lengte, bloeddruk en cholesterol niveau) hoeveel erfelijkheid verklaard wordt door reeds bekende genetische varianten, en hoeveel er maximaal verklaard kan worden door alle veelvoorkomende genetische varianten. Dit onthulde, onder andere, dat zelfs up-to-date genetische voorspellingen van complexe fenotypes nog niet accuraat genoeg zijn voor gepersonaliseerde medische voorspellingen. Ook probeerden wij de erfelijkheid van neuroticisme te verhelderen door het op verschillende nieuwe manieren te analyseren. Concluderend: de in dit proefschrift beschreven en toegepaste methodes staan toe om kwalitatief betere genoom-brede analyses en erfelijkheidsanalyses uit te voeren

The Interaction of Genetic Predisposition and Socioeconomic Position With Type 2 Diabetes Mellitus:Cross-Sectional and Longitudinal Analyses From the Lifelines Cohort and Biobank Study

OBJECTIVE: A strong genetic predisposition for type 2 diabetes mellitus (T2DM) may aggravate the negative effects of low socioeconomic position (SEP) in the etiology of the disorder. This study aimed to examine cross-sectional and longitudinal associations and interactions of a genetic risk score (GRS) and SEP with T2DM, and to investigate whether clinical and behavioral risk factors can explain these associations and interactions. METHODS: We used data from 13,027 genotyped participants from the Lifelines study. The GRS was based on single-nucleotide polymorphisms (SNPs) genome-wide associated with T2DM and was categorized into tertiles. SEP was measured as educational level. T2DM was based on biological markers, recorded medication use, and self-reports. Cross-sectional and longitudinal associations, and interactions, between the GRS and SEP on T2DM were examined. RESULTS: The combination of a high GRS and low SEP had the strongest association with T2DM in cross-sectional (OR: 3.84; 95% CI: 2.28, 6.46) and longitudinal analyses (HR: 2.71; 1.39, 5.27), compared to a low GRS and high SEP. Interaction between a high GRS and a low SEP was observed in cross-sectional (relative excess risk due to interaction: 1.85; 0.65, 3.05) but not in longitudinal analyses. Clinical and behavioral risk factors mostly explained the observed associations and interactions. CONCLUSIONS: A high GRS combined with a low SEP provides the highest risk for T2DM. These factors also exacerbated each other's impact cross-sectionally but not longitudinally. Preventive measures should target individual and contextual factors of this high-risk group to reduce the risk of T2DM

Genome-Wide Association Scan of Serum Urea in European Populations Identifies Two Novel Loci

BACKGROUND: Serum urea level is a heritable trait, commonly used as a diagnostic marker for kidney function. Genome-wide association studies (GWAS) in East-Asian populations identified a number of genetic loci related to serum urea, however there is a paucity of data for European populations. METHODS: We performed a two-stage meta-analysis of GWASs on serum urea in 13,312 participants, with independent replication in 7,379 participants of European ancestry. RESULTS: We identified 6 genome-wide significant single nucleotide polymorphisms (SNPs) in or near 6 loci, of which 2 were novel (POU2AF1 and ADAMTS9-AS2). Replication of East-Asian and Scottish data provided evidence for an additional 8 loci. SNPs tag regions previously associated with anthropometric traits, serum magnesium, and urinary albumin-to-creatinine ratio, as well as expression quantitative trait loci for genes preferentially expressed in kidney and gastro-intestinal tissues. CONCLUSIONS: Our findings provide insights into the genetic underpinnings of urea metabolism, with potential relevance to kidney function

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets

Loci influencing blood pressure identified using a cardiovascular gene-centric array

Blood pressure (BP) is a heritable determinant of risk for cardiovascular disease (CVD). To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) and pulse pressure (PP), we genotyped 50 000 single-nucleotide polymorphisms (SNPs) that capture variation in 2100 candidate genes for cardiovascular phenotypes in 61 619 individuals of European ancestry from cohort studies in the USA and Europe. We identified novel associations between rs347591 and SBP (chromosome 3p25.3, in an intron of HRH1) and between rs2169137 and DBP (chromosome1q32.1 in an intron of MDM4) and between rs2014408 and SBP (chromosome 11p15 in an intron of SOX6), previously reported to be associated with MAP. We also confirmed 10 previously known loci associated with SBP, DBP, MAP or PP (ADRB1, ATP2B1, SH2B3/ATXN2, CSK, CYP17A1, FURIN, HFE, LSP1, MTHFR, SOX6) at array-wide significance (P 2.4 10(6)). We then replicated these associations in an independent set of 65 886 individuals of European ancestry. The findings from expression QTL (eQTL) analysis showed associations of SNPs in the MDM4 region with MDM4 expression. We did not find any evidence of association of the two novel SNPs in MDM4 and HRH1 with sequelae of high BP including coronary artery disease (CAD), left ventricular hypertrophy (LVH) or stroke. In summary, we identified two novel loci associated with BP and confirmed multiple previously reported associations. Our findings extend our understanding of genes involved in BP regulation, some of which may eventually provide new targets for therapeutic intervention.</p

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe

The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia : design, results and future prospects

The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.Peer reviewe