Our objective was to better understand the genetic bases of dilated cardiomyopathy (DCM), a leading cause of systolic heart failure. We conducted the largest genome-wide association study performed so far in DCM, with 2719 cases and 4440 controls in the discovery population. We identified and replicated two new DCM-associated loci on chromosome 3p25.1 [lead single-nucleotide polymorphism (SNP) rs62232870, P = 8.7 × 10⁻¹¹ and 7.7 × 10⁻⁴ in the discovery and replication steps, respectively] and chromosome 22q11.23 (lead SNP rs7284877, P = 3.3 × 10⁻⁸ and 1.4 × 10⁻³ in the discovery and replication steps, respectively), while confirming two previously identified DCM loci on chromosomes 10 and 1, BAG3 and HSPB7. A genetic risk score constructed from the number of risk alleles at these four DCM loci revealed a 3-fold increased risk of DCM for individuals with 8 risk alleles compared to individuals with 5 risk alleles (median of the referral population). In silico annotation and functional 4C-sequencing analyses on iPSC-derived cardiomyocytes identify SLC6A6 as the most likely DCM gene at the 3p25.1 locus. This gene encodes a taurine transporter whose involvement in myocardial dysfunction and DCM is supported by numerous observations in humans and animals. At the 22q11.23 locus, in silico and data mining annotations, and to a lesser extent functional analysis, strongly suggest SMARCB1 as the candidate culprit gene. This study provides a better understanding of the genetic architecture of DCM and sheds light on novel biological pathways underlying heart failure. Step 1: Through the largest genome-wide association study performed so far in dilated cardiomyopathy, we identified and replicated two new loci on chromosome 3p25.1 and 22q11.23. Step 2: Combined in silico and functional analyses at the associated loci revealed the best culprit gene at each locus: SLC6A6 (chromosome 3) and SMARCB1 (chromosome 22). The discovery of these two new players shed light on novel biological pathways and putative new therapeutic targets.

Age-related macular degeneration (AMD) is a common condition of vision loss with disease development strongly influenced by environmental and genetic factors. Recently, 34 loci were associated with AMD at genome-wide significance. So far, little is known about a genetic overlap between AMD and other complex diseases or disease-relevant traits. For each of 60 complex diseases/traits with publicly available genome-wide significant association data, the lead genetic variant per independent locus was extracted and a genetic score was calculated for each disease/trait as the weighted sum of risk alleles. The association with AMD was estimated based on 16,144 AMD cases and 17,832 controls using logistic regression. Of the respective disease/trait variance, the 60 genetic scores explained on average 4.8% (0.27–20.69%) and 16 of them were found to be significantly associated with AMD (Q-values < 0.01, p values from < 1.0 × 10–16 to 1.9 × 10–3). Notably, an increased risk for AMD was associated with reduced risk for cardiovascular diseases, increased risk for autoimmune diseases, higher HDL and lower LDL levels in serum, lower bone-mineral density as well as an increased risk for skin cancer. By restricting the analysis to 1824 variants initially used to compute the 60 genetic scores, we identified 28 novel AMD risk variants (Q-values < 0.01, p values from 1.1 × 10–7 to 3.0 × 10–4), known to be involved in cardiovascular disorders, lipid metabolism, autoimmune diseases, anthropomorphic traits, ocular disorders, and neurological diseases. The latter variants represent 20 novel AMD-associated, pleiotropic loci. Genes in the novel loci reinforce previous findings strongly implicating the complement system in AMD pathogenesis. We demonstrate a substantial overlap of the genetics of several complex diseases/traits with AMD and provide statistically significant evidence for an additional 20 loci associated with AMD. This highlights the possibility that so far unrelated pathologies may have disease pathways in common.

Background To estimate prevalence and incidence of diseases through self-reports in observational studies, it is important to understand the accuracy of participant reports. We aimed to quantify the agreement of self-reported and general practitioner-reported diseases in an old-aged population and to identify socio-demographic determinants of agreement. Methods This analysis was conducted as part of the AugUR study (n=2449), a prospective population-based cohort study in individuals aged 70–95 years, including 2321 participants with consent to contact physicians. Self-reported chronic diseases of participants were compared with medical data provided by their respective general practitioners (n=589, response rate=25.4%). We derived overall agreement, over-reporting/under-reporting, and Cohen’s kappa and used logistic regression to evaluate the dependency of agreement on participants’ sociodemographic characteristics. Results Among the 589 participants (53.1% women), 96.9% reported at least one of the evaluated chronic diseases. Overall agreement was >80% for hypertension, diabetes, myocardial infarction, stroke, cancer, asthma, bronchitis/chronic obstructive pulmonary disease and rheumatoid arthritis, but lower for heart failure, kidney disease and arthrosis. Cohen’s kappa was highest for diabetes and cancer and lowest for heart failure, musculoskeletal, kidney and lung diseases. Sex was the primary determinant of agreement on stroke, kidney disease, cancer and rheumatoid arthritis. Agreement for myocardial infarction and stroke was most compromised by older age and for cancer by lower educational level. Conclusion Self-reports may be an effective tool to assess diabetes and cancer in observational studies in the old and very old aged. In contrast, self-reports on heart failure, musculoskeletal, kidney or lung diseases may be substantially imprecise.

The majority of patients suffering from chronic health disabilities is beyond 70 years of age. Typical late-onset chronic diseases include those affecting the heart, the kidney, cancer, and conditions of the eye such as age-related macular degeneration. These diseases disable patients for many years and largely compromise autonomy in daily life. Due to challenges in recruiting the elderly, the collection of population-based epidemiological data as a prerequisite to understand associated risk factors and mechanisms is commonly done in the general population within an age-range of 20 to 70 years. We establish the German AugUR study (Age-related diseases: understanding genetic and non-genetic influences - a study at the University of Regensburg), a prospective study in the mobile elderly general population in and around Regensburg in eastern Bavaria. In the long term, we aim to recruit 3,000 persons of Caucasian ethnicity with at least 70 years of age via residents’ registration offices and conduct 3-year follow-ups. The study protocol includes a standardized interview regarding social and life-style factors, medication history, quality-of-life, and existing diagnoses of common diseases. The participants undergo medical examinations for ophthalmological, cardiovascular or diabetes-related conditions, and general measurements of body shape and fitness. The program is particularly tailored for the elderly. Biobanking of whole blood, serum, plasma, and urine is conducted and standard laboratory measurements are performed in fresh samples. AugUR is specifically designed as a research platform to host studies of late onset diseases. Consequently, this platform will help (1) to unravel the genetic and non-genetic etiology of disease development and progression, (2) to serve as control group of elderly individuals for comparisons with various patient groups, (3) to derive prevalence and incidence data on chronic diseases, and (4) to provide clinical reference parameters for the elderly mobile general population. This data will foster our understanding of disease mechanisms, which may ultimately help to improve prevention, diagnosis, and therapy for frequent chronic diseases. Here we present the baseline study protocol of AugUR.

While age-related macular degeneration (AMD) poses an important personal and public health burden, comparing epidemiological studies on AMD is hampered by differing approaches to classify AMD. In our AugUR study survey, recruiting residents from in/around Regensburg, Germany, aged 70+, we analyzed the AMD status derived from color fundus images applying two different classification systems. Based on 1,040 participants with gradable fundus images for at least one eye, we show that including individuals with only one gradable eye (n = 155) underestimates AMD prevalence and we provide a correction procedure. Bias-corrected and standardized to the Bavarian population, late AMD prevalence is 7.3% (95% confidence interval = [5.4; 9.4]). We find substantially different prevalence estimates for “early/intermediate AMD” depending on the classification system: 45.3% (95%-CI = [41.8; 48.7]) applying the Clinical Classification (early/intermediate AMD) or 17.1% (95%-CI = [14.6; 19.7]) applying the Three Continent AMD Consortium Severity Scale (mild/moderate/severe early AMD). We thus provide a first effort to grade AMD in a complete study with different classification systems, a first approach for bias-correction from individuals with only one gradable eye, and the first AMD prevalence estimates from a German elderly population. Our results underscore substantial differences for early/intermediate AMD prevalence estimates between classification systems and an urgent need for harmonization.

Introduction: Both genetic and environmental factors contribute to rheumatoid arthritis (RA), a common and complex autoimmune disease. As well as the major susceptibility gene HLA-DRB1, recent genome-wide and candidate-gene studies reported additional evidence for association of single nucleotide polymorphism (SNP) markers in the PTPN22, STAT4, OLIG3/TNFAIP3 and TRAF1/C5 loci with RA. This study was initiated to investigate the association between defined genetic markers and RA in a Slovak population. In contrast to recent studies, we included intensively-characterized osteoarthritis (OA) patients as controls. Methods: We used material of 520 RA and 303 OA samples in a case-control setting. Six SNPs were genotyped using TaqMan assays. HLA-DRB1 alleles were determined by employing site-specific polymerase chain reaction (PCR) amplification. Results: No statistically significant association of TRAF1/C5 SNPs rs3761847 and rs10818488 with RA was detected. However, we were able to replicate the association signals between RA and HLA-DRB1 alleles, STAT4 (rs7574865), PTPN22 (rs2476601) and OLIG3/TNFAIP3 (rs10499194 and rs6920220). The strongest signal was detected for HLA-DRB1*04 with an allelic P = 1.2*10^-13 (OR = 2.92, 95% confidence interval (CI) = 2.18 – 3.91). Additionally, SNPs rs7574865 _STAT4 (P = 9.2*10^-6; OR = 1.71, 95% CI = 1.35 – 2.18) and rs2476601 _PTPN22 (P = 9.5*10^-4; OR = 1.67, 95% CI = 1.23 – 2.26) were associated with susceptibility to RA, whereas after permutation testing OLIG3/TNFAIP3 SNPs rs10499194 and rs6920220 missed our criteria for significance (P_corr = 0.114 and P_corr = 0.180, respectively). Conclusions: In our Slovak population, HLA-DRB1 alleles as well as SNPs in STAT4 and PTPN22 genes showed a strong association with RA.