## 4 Discussion  
The CHRIS study constitutes a valuable resource to investigate the genomic, metabolomic, and environmental factors in human diseases. The low residential mobility across generations and low inbreeding, with a rather homogeneous lifestyle and environmental conditions, are elements that facilitate the identification of specific enriched genetic variants and altered metabolites in the CHRIS cohort [12]. On the other hand, it is important to consider that the study involves a small region; therefore, any possible findings cannot be  
Figure 4: Left: Volcano plot representing the results from the targeted metabolomics analysis. \(\text{coef}_{\text{AF}}\): log2 difference of metabolite concentrations between AF cases and controls. Significant metabolites are highlighted in red. Right: lysophosphatidylcholine a C20:3 concentration (adjusted for age, sex, BMI, and AF therapy) in controls (CTRL) and AF cases. Figure 5: Lysophosphatidylcholine a C20:3 concentration (adjusted for age, sex, BMI and AF therapy) in nine family members and other CHRIS participants. generalized and need confirmation in other populations. Moreover, the fact that the CHRIS study is not a cohort of patients, with only limited clinical data available and with only a subset of cases showing the phenotype of interest, could be a limiting factor for the type of study presented here. A general population is more suitable for the study of common variants associated with frequent phenotypes affecting many individuals. However, a general population such as that we have in the CHRIS study, consisting of families with several members and even generations residing in an area characterized by low regional migration, may help to also identify rare variants associated with a specific phenotype. The current work is an example of how AF can be investigated in a general population study context, considering the contribution of genomics and metabolomics. AF is the most common cardiac arrhythmia with a reported prevalence of approximately 3% in the global population [2], ranging from 0.56% previously reported in Japan [23] up to 6.1% and 5.5% in some studies from Spain [24] and the Netherlands [25], respectively. Other European studies described similar results [26; 27; 28]. The differences in reported prevalence may reflect distinct study settings, methods, and criteria used to identify AF cases and also differing characteristics of the study populations such as genetics, age, and ethnicity, as well as the presence of other heart diseases, risk, and environmental factors [29; 30]. Here, we report 110 (1.05%) AF cases identified among the study participants, using their answers from the questionnaire and the results from 10 s ECG recordings, which are likely to identify those already having a confirmed AF diagnosis or, in case of a lack of this information, only those with a permanent AF, identified using a single-occasion ECG. We may conjecture that our current result is liable to underestimate the real prevalence of AF in our study sample, failing to detect those subclinical, silent, and paroxysmal AF cases that would remain unrecognized using this approach. Additionally, we also have to consider the specific AF drug therapy, which itself is limited due to questions of compliance, as a further limiting factor for a more reliable selection of AF phenotype. In order to understand the genetic contribution to our AF cases, we performed a GWAS analysis and subsequently sequenced the identified four genome-wide significant variants. Given the low frequency of identified variants, Sanger sequencing validation was necessary since all variants were imputed. While the HRC panel used in imputation was expected to enable imputation of low-frequency and rare variants in European populations [31], such as the CHRIS cohort, extremely low minor allele frequency, as observed for the unconfirmed variants, is known to impede imputation accuracy [32].