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

GWASinspector:comprehensive quality control of genome-wide association study results

Quality control (QC) of genome wide association study (GWAS) result files has become increasingly difficult due to advances in genomic technology. The main challenges include continuous increases in the number of polymorphic genetic variants contained in recent GWASs and reference panels, the rising number of cohorts participating in a GWAS consortium, and inclusion of new variant types. Here, we present GWASinspector, a flexible R package for comprehensive QC of GWAS results. This package is compatible with recent imputation reference panels, handles insertion/deletion and multi-allelic variants, provides extensive QC reports and efficiently processes big data files. Reference panels covering three human genome builds (NCBI36, GRCh37 and GRCh38) are available. GWASinspector has a user friendly design and allows easy set-up of the QC pipeline through a configuration file. In addition to checking and reporting on individual files, it can be used in preparation of a meta-analysis by testing for systemic differences between studies and generating cleaned, harmonized GWAS files. Comparison with existing GWAS QC tools shows that the main advantages of GWASinspector are its ability to more effectively deal with insertion/deletion and multi-allelic variants and its relatively low memory use

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function

HapMap imputed genome-wide association studies (GWAS) have revealed > 50 loci at which common variants with minor allele frequency > 5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 x 10(-8) previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until wholegenome sequencing becomes feasible in large samples

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

Associations of Genetic Factors, Educational Attainment, and Their Interaction With Kidney Function Outcomes

Both genetic predisposition and low educational attainment (EA) are associated with higher risk of chronic kidney disease. We examined the interaction of EA and genetic risk in kidney function outcomes. We included 3,597 participants from the Prevention of Renal and Vascular End-Stage Disease Cohort Study, a longitudinal study in a community-based sample from Groningen, the Netherlands (median follow-up, 11 years; 1997-2012). Kidney function was approximated by obtaining estimated glomerular filtration rate (eGFR) from serum creatinine and cystatin C. Individual longitudinal linear eGFR trajectories were derived from linear mixed models. Genotype data on 63 single-nucleotide polymorphisms, with known associations with eGFR, were used to calculate an allele-weighted genetic score (WGS). EA was categorized into high, medium, and low. In ordinary least squares analysis, higher WGS and lower EA showed additive effects on reduced baseline eGFR; the interaction term was nonsignificant. In analysis of eGFR decline, the significant interaction term suggested amplification of genetic risk by low EA. Adjustment for known renal risk factors did not affect our results. This study presents the first evidence of gene-environment interaction between EA and a WGS for eGFR decline and provides population-level insights into the mechanisms underlying socioeconomic disparities in chronic kidney disease

Search for a Functional Genetic Variant Mimicking the Effect of SGLT2 Inhibitor Treatment

SGLT2 inhibitors (SGLT2i) block renal glucose reabsorption. Due to the unexpected beneficial observations in type 2 diabetic patients potentially related to increased natriuresis, SGLT2i are also studied for heart failure treatment. This study aimed to identify genetic variants mimicking SGLT2i to further our understanding of the potential underlying biological mechanisms. Using the UK Biobank resource, we identified 264 SNPs located in the SLC5A2 gene or within 25kb of the 5′ and 3′ flanking regions, of which 91 had minor allele frequencies >1%. Twenty-seven SNPs were associated with glycated hemoglobin (HbA1c) after Bonferroni correction in participants without diabetes, while none of the SNPs were associated with sodium excretion. We investigated whether these variants had a directionally consistent effect on sodium excretion, HbA1c levels, and SLC5A2 expression. None of the variants met these criteria. Likewise, we identified no common missense variants, and although four SNPs could be defined as 5′ or 3′ prime untranslated region variants of which rs45612043 was predicted to be deleterious, these SNPs were not annotated to SLC5A2. In conclusion, no genetic variant was found mimicking SGLT2i based on their location near SLC5A2 and their association with sodium excretion or HbA1c and SLC5A2 expression or function

Genetic Risk Scores for Complex Disease Traits in Youth

Background: For most disease-related traits the magnitude of the contribution of genetic factors in adolescents remains unclear. Methods: Twenty continuous traits related to anthropometry, cardiovascular and renal function, metabolism, and inflammation were selected from the ongoing prospective Tracking Adolescents' Individual Lives Survey cohort in the Netherlands with measurements of up to 5 waves from age 11 to 22 years (n=1354, 47.6% males) and all traits available at the third wave (mean age [SD]=16.22 [0.66]). For each trait, unweighted and weighted genetic risk scores (GRSs) were generated based on significantly associated single nucleotide polymorphisms identified from literature. The variance explained by the GRSs in adolescents were estimated by linear regression after adjustment for covariates. Results: Except for ALT (alanine transaminase), all GRSs were significantly associated with their traits. The trait variance explained by the GRSs was highest for lipoprotein[a] (39.59%) and varied between 0.09% (ALT) and 18.49% (LDL [low-density lipoprotein]) for the other traits. For most traits, the variances explained in adolescents were comparable with or slightly smaller than those in adults. Significant increases of trait levels (except ALT) and increased risks for overweight/obesity (odds ratio, 6.41 [95% CI, 2.95-15.56]) and hypertension (odds ratio, 2.86 [95% CI, 1.39-6.17]) were found in individuals in the top GRS decile compared with those at the bottom decile. Conclusions: Variances explained by adult-based GRSs for disease-related traits in adolescents, although still relatively modest, were comparable with or slightly smaller than in adults offering promise for improved risk prediction at early ages

Genetic Determinants of Serum Calcification Propensity and Cardiovascular Outcomes in the General Population

BACKGROUND: Serum calciprotein particle maturation time (T(50)), a measure of vascular calcification propensity, is associated with cardiovascular morbidity and mortality. We aimed to identify genetic loci associated with serum T(50) and study their association with cardiovascular disease and mortality. METHODS: We performed a genome-wide association study of serum T(50) in 2,739 individuals of European descent participating in the Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study, followed by a two-sample Mendelian randomization (MR) study to examine causal effects of T(50) on cardiovascular outcomes. Finally, we examined associations between T(50) loci and cardiovascular outcomes in 8,566 community-dwelling participants in the Rotterdam study. RESULTS: We identified three independent genome-wide significant single nucleotide polymorphism (SNPs) in the AHSG gene encoding fetuin-A: rs4917 (p = 1.72 × 10(−101)), rs2077119 (p = 3.34 × 10(−18)), and rs9870756 (p = 3.10 × 10(−8)), together explaining 18.3% of variation in serum T(50). MR did not demonstrate a causal effect of T(50) on cardiovascular outcomes in the general population. Patient-level analyses revealed that the minor allele of rs9870756, which explained 9.1% of variation in T(50), was associated with a primary composite endpoint of all-cause mortality or cardiovascular disease [odds ratio (95% CI) 1.14 (1.01–1.28)] and all-cause mortality alone [1.14 (1.00–1.31)]. The other variants were not associated with clinical outcomes. In patients with type 2 diabetes or chronic kidney disease, the association between rs9870756 and the primary composite endpoint was stronger [OR 1.40 (1.06–1.84), relative excess risk due to interaction 0.54 (0.01–1.08)]. CONCLUSIONS: We identified three SNPs in the AHSG gene that explained 18.3% of variability in serum T(50) levels. Only one SNP was associated with cardiovascular outcomes, particularly in individuals with type 2 diabetes or chronic kidney disease

Identification and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies

Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are currently under clinical development for treating anemia in chronic kidney disease (CKD), but it is important to monitor their cardiovascular safety. Genetic variants can be used as predictors to help inform the potential risk of adverse effects associated with drug treatments. We therefore aimed to use human genetics to help assess the risk of adverse cardiovascular events associated with therapeutically altered EPO levels to help inform clinical trials studying the safety of HIF-PHIs. By performing a genome-wide association meta-analysis of EPO (n = 6,127), we identified a cis-EPO variant (rs1617640) lying in the EPO promoter region. We validated this variant as most likely causal in controlling EPO levels by using genetic and functional approaches, including single-base gene editing. Using this variant as a partial predictor for therapeutic modulation of EPO and large genome-wide association data in Mendelian randomization tests, we found no evidence (at p < 0.05) that genetically predicted long-term rises in endogenous EPO, equivalent to a 2.2-unit increase, increased risk of coronary artery disease (CAD, OR [95% CI] = 1.01 [0.93, 1.07]), myocardial infarction (MI, OR [95% CI] = 0.99 [0.87, 1.15]), or stroke (OR [95% CI] = 0.97 [0.87, 1.07]). We could exclude increased odds of 1.15 for cardiovascular disease for a 2.2-unit EPO increase. A combination of genetic and functional studies provides a powerful approach to investigate the potential therapeutic profile of EPO-increasing therapies for treating anemia in CKD

Hidden heritability due to heterogeneity across seven populations

Meta-analyses of genome-wide association studies, which dominate genetic discovery, are based on data from diverse historical time periods and populations. Genetic scores derived from genome-wide association studies explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the ‘hidden heritability’ puzzle. Using seven sampling populations (n = 35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are substantially smaller across populations compared with within populations. We show that the hidden heritability varies substantially: from zero for height to 20% for body mass index, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results are more likely to reflect heterogeneity in phenotypic measurement or gene–environment interactions than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene–environment interaction may be a central challenge for genetic discovery