It is increasingly recognized that participation bias can pose problems for genetic studies. Recently, to overcome the challenge that genetic information of non-participants is unavailable, it is shown that by comparing the IBD (identity by descent) shared and not-shared segments among the participants, one can estimate the genetic component underlying participation. That, however, does not directly address how to adjust estimates of heritability and genetic correlation for phenotypes correlated with participation. Here, for phenotypes whose mean differences between population and sample are known, we demonstrate a way to do so by adopting a statistical framework that separates out the genetic and non-genetic correlations between participation and these phenotypes. Crucially, our method avoids making the assumption that the effect of the genetic component underlying participation is manifested entirely through these other phenotypes. Applying the method to 12 UK Biobank phenotypes, we found 8 have significant genetic correlations with participation, including body mass index, educational attainment, and smoking status. For most of these phenotypes, without adjustments, estimates of heritability and the absolute value of genetic correlation would have underestimation biases.

翻译：日益认识到参与偏差可能给遗传学研究带来问题。最近，为克服非参与者遗传信息不可得的挑战，研究表明通过比较参与者之间共享与非共享的IBD（血缘同源）片段，可以估计参与行为背后的遗传成分。然而，该方法并未直接解决如何针对与参与行为相关的表型调整遗传力与遗传相关性的估计值。本文针对群体与样本间均值差异已知的表型，通过采用将参与行为和这些表型之间的遗传与非遗传相关性分离的统计框架，提出了一种调整方法。关键的是，我们的方法避免了"参与行为的遗传成分效应完全通过这些其他表型体现"的假设。将该方法应用于英国生物银行的12个表型，发现其中8个（包括体重指数、教育程度和吸烟状况）与参与行为存在显著的遗传相关性。对于大多数此类表型，若不进行调整，遗传力的估计值及遗传相关性的绝对值将存在低估偏差。