In this study, SNP-based heritability estimates of 16 phenotypic traits were estimated longitudinal data from a 10-year follow-up of the KARE cohort. The GCTA tool was used with a two-stage approach to determine the heritability estimate of phenotypic mean and longitudinal changes in each trait. Moreover, chromosomal heritability estimates were determined and GWAS analyses were performed using the same approach. Overall, heritability estimates within the population-based cohort including KARE are potentially lower than those of pedigree or twin studies for all 16 traits, regardless of whether the response is \(B_{0}\) that phenotypic mean of traits or \(B_{1}\) which stands for the changes by time of traits. For example, the heritability of height herein was estimated to be approximately 0.318 with \(B_{0}\) as the response, which is lower than the conventional heritability estimate of height of approximately 0.8 based on the assumption-free model (Visscher et al. 2006). In the case of TCHL and LDL, each heritability estimate was determined to be 0.265 and 0.22, respectively, which are also lower than the heritability estimates of 0.67 and 0.69 for TCHL and LDL, respectively, on familial and pedigree analysis (van Dongen et al. 2013). The underlying reason may be explained on the basis of the missing heritability, which describes the difference in values between heritability estimated via GWAS and via familial studies (Sandoval-Motta et al. 2017). However, systemic inflation of estimated heritability estimates of polygenic phenotypes in familial studies may be confounded owing to a shared environment or environment-dependent genetic effects (Robinson et al. 2017). Therefore, the population-based design similar to that of the present study potentially represents the average genetic effects regardless of various confounding environmental factors.

Based on the present \(B_{0}\) and \(B_{1}\) model, the heritabilities of \(B_{1}\) are markedly lower than those of \(B_{0}\), indicating that most of the genetic variance of traits are not temporally influenced. Here, \(B_{0}\) was not determined from the baseline measurements of traits but rather the average values of repeated measurements to yield a more robust and reasonable result. If baseline measurement and longitudinal changes \((B_{1} )\) calculated from those were considered responses during the estimation of heritability, the estimate may have been potentially inaccurate owing to the correlation between baseline and \(B_{1}\) values. Thus, by applying a regression model to estimate the average \(B_{0}\) and longitudinal changes \(B_{1}\), the effect of \(B_{0}\) on \(B_{1}\) in each subject could be removed.

On GWAS, the two-stage model elucidated significant variants associated with the traits and their changes in the longitudinal data. We confirmed several proven variants and identified some other significant unreported variants. In the case of the \(B_{0}\) model, rs4922117 (P = 2.13 \(\times\) 10−15) of log (HDL) and rs2335418 (P = 3.2 \(\times\) 10−9) of LDL were both unreported; however, their proximal genes LPL and HMGCR, respectively, were significantly associated with each trait (Hoffmann et al. 2018). Furthermore, unreported genes, such as rs180349, including non-coding SNPs with a significant P value for TG are proximal to BUD13, which is strongly associated with TG (Hoffmann et al. 2018). Variants including rs17482753 also had significant P values and was proximal to LPL, which is strongly associated with the HDL trait (Hoffmann et al. 2018). In the \(B_{1}\) model, rs2272402 (SLC6A1, P = 1.22 \(\times\) 10−8) was significant in both FEV1 and FVC lung function. The SLC6A1 enhancer is associated with pulmonary function. Therefore, the present results are concurrent with previous findings regarding genes associated with each phenotype.

Among the 16 phenotypic traits in this study, only FEV1 displayed longitudinally significant heritability herein (Fig. 4), thus reliably reflecting the physiological state of the lungs and airways and acting as a predictor of morbidity and mortality in the general population; FEV1 is also widely used to define chronic obstructive pulmonary disease (COPD) (Young et al. 2007). Lung function develops in early life, peaks at a specific time point in early adulthood, and subsequently declines with age. Therefore, the decline of lung function in middle-aged and older individuals is suggested to be heritable in the general population (Gottlieb et al. 2001). However, longitudinal studies on FEV1 and FEV1/FVC have suggested several significant genetic regions that markedly differ from the numerous genetic variants associated with lung function, with FEV1 being estimated at a single time point (John et al. 2017; Tang et al. 2014). Hence, gene-environment interactions and significant genetic heterogeneity in lung function have been observed in diseases such as asthma or COPD (Hansel et al. 2013; Imboden et al. 2012). Accordingly, the present study included the middle-aged general population with similar environmental exposure without specific lung diseases, thus suggesting that intact FEV1 decreased due to aging. Therefore, the present results show that FEV1 has significant SNP heritability for longitudinal changes (FDR = 0.0012 for FEV1).

This study has several limitations. First, the analysis of new variants in the present GWAS was not replicated for other cohorts. Second, the two-stage approach is statistically inefficient even though it is computationally fast. However, the sample size was very large, which hopefully minimized this problem. Furthermore, we considered subjects with at least three or more measurements, which potentially minimize statistical power loss. Third, gene-environment interactions were not analyzed, although the estimation of random effects in the mixed model was elusive. Fourth, GCTA itself has limitations for reasons such as data overfitting and skewed singular values (Kumar et al. 2016). Though our study optimized parameters to attain accurate results using GCTA, our sample size might have resulted in certain variations in comparison with other large studies. Furthermore, the issue regarding missing heritability was inevitable to an extent because the Affymetrix genotypic array represents only common variants for SNPs, while rare genetic SNP variants were not included herein (Bandyopadhyay et al. 2017).

Despite the aforementioned limitations, our study elucidates heritability estimates via a two-stage approach using a mixed model in GCTA and a GWAS, which further determines longitudinal change effects independently with a linear model, followed by estimating heritability using regression coefficients. This approach provides a reasonable and easy method to estimate heritability in longitudinal data and potentially assess both heritability of the phenotypic mean and changes through several periods. Essentially, our results show that significant SNP heritability is objectively confirmed for longitudinal changes in lung function decline including FEV1 in comparison with other health-related indices. Therefore, genetic studies on longitudinal FEV1 decline among the middle-aged general population and chromosome 2, which attributes the most in genetic variance should be encouraged.