Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), Affiliated to the University of Lübeck, Via Galvani 31, 39100, Bolzano, Italy.
Clinical Pathology Laboratory, Hospital of Merano, Merano, Italy.
Hum Genet. 2017 Jun;136(6):743-757. doi: 10.1007/s00439-017-1785-8. Epub 2017 Apr 3.
After the success of genome-wide association studies to uncover complex trait loci, attempts to explain the remaining genetic heritability (h ) are mainly focused on unraveling rare variant associations and gene-gene or gene-environment interactions. Little attention is paid to the possibility that h estimates are inflated as a consequence of the epidemiological study design. We studied the time series of 54 biochemical traits in 4373 individuals from the Cooperative Health Research In South Tyrol (CHRIS) study, a pedigree-based study enrolling ten participants/day over several years, with close relatives preferentially invited within the same day. We observed distributional changes of measured traits over time. We hypothesized that the combination of such changes with the pedigree structure might generate a shared-environment component with consequent h inflation. We performed variance components (VC) h estimation for all traits after accounting for the enrollment period in a linear mixed model (two-stage approach). Accounting for the enrollment period caused a median h reduction of 4%. For 9 traits, the reduction was of >20%. Results were confirmed by a Bayesian Markov chain Monte Carlo analysis with all VCs included at the same time (one-stage approach). The electrolytes were the traits most affected by the enrollment period. The h inflation was independent of the h magnitude, laboratory protocol changes, and length of the enrollment period. The enrollment process may induce shared-environment effects even under very stringent and standardized operating procedures, causing h inflation. Including the day of participation as a random effect is a sensitive way to avoid overestimation.
在全基因组关联研究成功揭示复杂性状基因座之后，解释剩余遗传遗传性（h）的尝试主要集中在揭示罕见变异关联以及基因-基因或基因-环境相互作用上。很少有人关注 h 估计值因流行病学研究设计而膨胀的可能性。我们研究了来自南蒂罗尔合作健康研究（CHRIS）研究的 4373 个人的 54 种生化特征的时间序列，这是一项基于家系的研究，每年招募十名参与者/天，在同一天优先邀请近亲。我们观察到测量特征随时间的分布变化。我们假设这种变化与家系结构的结合可能会产生具有随之而来的 h 膨胀的共享环境成分。我们在线性混合模型（两阶段方法）中对所有特征进行了方差分量（VC）h 估计，以考虑到登记期。考虑到登记期，中位数 h 降低了 4%。对于 9 种特征，降幅超过 20%。通过同时包含所有 VC 的贝叶斯马尔可夫链蒙特卡罗分析（单阶段方法）证实了结果。电解质是受登记期影响最大的特征。h 膨胀与 h 幅度、实验室方案变化和登记期长度无关。即使在非常严格和标准化的操作程序下，登记过程也可能会引起共享环境效应，导致 h 膨胀。将参与日作为随机效应包含在内是避免高估的一种敏感方法。
