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The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass). Most gene-by-environment interaction methods rely on the availability of the interacting environment. Here, the authors propose a robust maximum likelihood method for estimating the overall statistical interaction between a genetic risk score for a continuous outcome and all environmental variables.

Background Genome-wide association studies (GWAS) have identified thousands of loci for disease-related human traits in cross-sectional data. However, the impact of age on genetic effects is underacknowledged. Also, identifying genetic effects on longitudinal trait change has been hampered by small sample sizes for longitudinal data. Such effects on deteriorating trait levels over time or disease progression can be clinically relevant. Results Under certain assumptions, we demonstrate analytically that genetic-by-age interaction observed in cross-sectional data can be indicative of genetic association on longitudinal trait change. We propose a 2-stage approach with genome-wide pre-screening for genetic-by-age interaction in cross-sectional data and testing identified variants for longitudinal change in independent longitudinal data. Within UK Biobank cross-sectional data, we analyze 8 complex traits (up to 370,000 individuals). We identify 44 genetic-by-age interactions (7 loci for obesity traits, 26 for pulse pressure, few to none for lipids). Our cross-trait view reveals trait-specificity regarding the proportion of loci with age-modulated effects, which is particularly high for pulse pressure. Testing the 44 variants in longitudinal data (up to 50,000 individuals), we observe significant effects on change for obesity traits (near APOE , TMEM18 , TFAP2B ) and pulse pressure (near FBN1 , IGFBP3 ; known for implication in arterial stiffness processes). Conclusions We provide analytical and empirical evidence that cross-sectional genetic-by-age interaction can help pinpoint longitudinal-change effects, when cross-sectional data surpasses longitudinal sample size. Our findings shed light on the distinction between traits that are impacted by age-dependent genetic effects and those that are not.

The problem of the genetics of related phenotypes is often addressed by analyzing adjusted-model traits, but such traits warrant cautious interpretation. Here, we adopt a joint view of adiposity traits in ~322,154 subjects (GIANT consortium). We classify 159 signals associated with body mass index (BMI), waist-to-hip ratio (WHR), or WHR adjusted for BMI (WHRadjBMI) at P  < 5 × 10 −8 , into four classes based on the direction of their effects on BMI and WHR. Our classes help differentiate adiposity genetics with respect to anthropometry, fat depots, and metabolic health. Class-specific Mendelian randomization reveals that variants associated with both WHR-decrease and BMI increase are linked to metabolically rather favorable adiposity through beneficial hip fat. Class-specific enrichment analyses implicate digestive systems as a pathway in adiposity genetics. Our results demonstrate that WHRadjBMI variants capture relevant effects of “unexpected fat distribution given the BMI” and that a joint view of the genetics underlying related phenotypes can inform on important biology. In GWAS, waist-to-hip ratio (WHR) is often adjusted for body mass index (BMI) to account for their correlation (WHRadjBMI). Here, Winkler et al. classify 159 genetic variants for BMI, WHR, or WHRadjBMI based on their effect directions for BMI and WHR to differentiate subtypes of adiposity genetics.

Loss of kidney function is a substantial personal and public health burden. Kidney function is typically assessed as estimated glomerular filtration rate (eGFR) based on serum creatinine. UK Biobank provides serum creatinine measurements from study center assessments (SC, n  = 425,147 baseline, n  = 15,314 with follow-up) and emerging electronic Medical Records (eMR, “GP-clinical”) present a promising resource to augment this data longitudinally. However, it is unclear whether eMR-based and SC-based creatinine values can be used jointly for research on eGFR decline. When comparing eMR-based with SC-based creatinine by calendar year ( n  = 70,231), we found a year-specific multiplicative bias for eMR-based creatinine that decreased over time (factor 0.84 for 2007, 0.97 for 2013). Deriving eGFR based on SC- and bias-corrected eMR-creatinine yielded 454,907 individuals with ≥ 1eGFR assessment (2,102,174 assessments). This included 206,063 individuals with ≥ 2 assessments over up to 60.2 years (median 6.00 assessments, median time = 8.7 years), where we also obtained eMR-based information on kidney disease or renal replacement therapy. We found an annual eGFR decline of 0.11 (95%-CI = 0.10–0.12) versus 1.04 mL/min/1.73m 2 /year (95%-CI = 1.03–1.05) without and with bias-correction, the latter being in line with literature. In summary, our bias-corrected eMR-based creatinine values enabled a 4-fold increased number of eGFR assessments in UK Biobank suitable for kidney function research.

Understanding the genetics of kidney function decline, or trait change in general, is hampered by scarce longitudinal data for GWAS (longGWAS) and uncertainty about how to analyze such data. We use longitudinal UK Biobank data for creatinine-based estimated glomerular filtration rate from 348,275 individuals to search for genetic variants associated with eGFR-decline. This search was performed both among 595 variants previously associated with eGFR in cross-sectional GWAS and genome-wide. We use seven statistical approaches to analyze the UK Biobank data and simulated data, finding that a linear mixed model is a powerful approach with unbiased effect estimates which is viable for longGWAS. The linear mixed model identifies 13 independent genetic variants associated with eGFR-decline, including 6 novel variants, and links them to age-dependent eGFR-genetics. We demonstrate that age-dependent and age-independent eGFR-genetics exhibit a differential pattern regarding clinical progression traits and kidney-specific gene expression regulation. Overall, our results provide insights into kidney aging and linear mixed model-based longGWAS generally. The authors use longitudinal data from the UK Biobank to search for genetic variants associated with kidney function decline. Using a linear mixed model, they identify 13 independent variants, incl. 6 novel, and link them to genetics of kidney aging.

Background Micro- and macrovascular diseases are common in patients with type 2 diabetes mellitus (T2D) and may be partly caused by nocturnal hypoxemia. The study aimed to characterize the composition of nocturnal hypoxemic burden and to assess its association with micro- and macrovascular disease in patients with T2D. Methods This cross-sectional analysis includes overnight oximetry from 1247 patients with T2D enrolled in the DIACORE (DIAbetes COhoRtE) study. Night-time spent below a peripheral oxygen saturation of 90% (T90) as well as T90 associated with non-specific drifts in oxygen saturation (T90 non − specific ), T90 associated with acute oxygen desaturation (T90 desaturation ) and desaturation depths were assessed. Binary logistic regression analyses adjusted for known risk factors (age, sex, smoking status, waist-hip ratio, duration of T2D, HbA1c, pulse pressure, low-density lipoprotein, use of statins, and use of renin-angiotensin-aldosterone system inhibitors) were used to assess the associations of such parameters of hypoxemic burden with chronic kidney disease (CKD) as a manifestation of microvascular disease and a composite of cardiovascular diseases (CVD) reflecting macrovascular disease. Results Patients with long T90 were significantly more often affected by CKD and CVD than patients with a lower hypoxemic burden (CKD 38% vs. 28%, p  < 0.001; CVD 30% vs. 21%, p  < 0.001). Continuous T90 desaturation and desaturation depth were associated with CKD (adjusted OR 1.01 per unit, 95% CI [1.00; 1.01], p  = 0.008 and OR 1.30, 95% CI [1.06; 1.61], p  = 0.013, respectively) independently of other known risk factors for CKD. For CVD there was a thresholdeffect, and only severly and very severly increased T90 non−specific was associated with CVD ([Q3;Q4] versus [Q1;Q2], adjusted OR 1.51, 95% CI [1.12; 2.05], p  = 0.008) independently of other known risk factors for CVD. Conclusion While hypoxemic burden due to oxygen desaturations and the magnitude of desaturation depth were significantly associated with CKD, only severe hypoxemic burden due to non-specific drifts was associated with CVD. Specific types of hypoxemic burden may be related to micro- and macrovascular disease.

Background Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise. Main Here we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects (“SNP-to-gene” mapping), (ii) genes with kidney phenotypes in mice or human (“gene-to-phenotype”), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function (“GPS tab”) to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data ( https://kidneygps.ur.de/gps/ ). Conclusion With the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo research.

The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score (PRS) from these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish (AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study (GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Our discovery set recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary-statistics excluding our study and developed PRSs via clumping/thresholding or LDpred2. In our discovery set, 31/34 loci reported by IAMDGC were AMD-associated (P < 0.05). Of those, all effects were directionally consistent with IAMDGC and 11 loci had a P-value under Bonferroni-corrected threshold (0.05/34 = 0.0015). At a 5 × 10 −5 threshold, we discovered four suggestive associations in FAM189A1 , IGDCC4 , C7orf50 , and CNTNAP4 . Only the FAM189A1 variant was AMD-associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS + covariates had an AUC of 0.82 (95% CI 0.79–0.85) and performed better than covariates-only model (P = 5.1 × 10 −9 ). Therefore, previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.

Mitochondrial DNA copy number (mtDNA-CN) is a biomarker for mitochondrial dysfunction associated with several diseases. Previous genome-wide association studies (GWAS) have been performed to unravel underlying mechanisms of mtDNA-CN regulation. However, the identified gene regions explain only a small fraction of mtDNA-CN variability. Most of this data has been estimated from microarrays based on various pipelines. In the present study we aimed to (1) identify genetic loci for qPCR-measured mtDNA-CN from three studies (16,130 participants) using GWAS, (2) identify potential systematic differences between our qPCR derived mtDNA-CN measurements compared to the published microarray intensity-based estimates, and (3) disentangle the nuclear from mitochondrial regulation of the mtDNA-CN phenotype. We identified two genome-wide significant autosomal loci associated with qPCR-measured mtDNA-CN: at HBS1L (rs4895440, p = 3.39 × 10 –13 ) and GSDMA (rs56030650, p = 4.85 × 10 –08 ) genes. Moreover, 113/115 of the previously published SNPs identified by microarray-based analyses were significantly equivalent with our findings. In our study, the mitochondrial genome itself contributed only marginally to mtDNA-CN regulation as we only detected a single rare mitochondrial variant associated with mtDNA-CN. Furthermore, we incorporated mitochondrial haplogroups into our analyses to explore their potential impact on mtDNA-CN. However, our findings indicate that they do not exert any significant influence on our results.

Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (>175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms.