The effect of correlation <i>r</i>_<i>g</i> between the direct and indirect effect sizes on PGS regression coefficients from different study designs.

y-axis shows the ratio of the PGS regression coefficients from between family design (red) or sibling difference design (blue) vs. 1, which is the effect size of the PGSdir (see Table 1). We used the same simulation settings as those used in Fig 3. Since we set the variance of the direct effect size , when the variance of the indirect effect size is also 1 and their correlation is -1, the direct and indirect effect sizes become exactly the opposite of each other, therefore PGSmix = 0 and the linear regression cannot be run under this scenario. Therefore, we do not include results when and ρg = −1.</p

The effect of correlation <i>r</i>_<i>g</i> between the direct and indirect effect sizes on the <i>R</i>² of PGS regression analyses from different study designs.

y-axis shows the ratio of R2 from between family design (red) or sibling difference design (blue) vs. the proportion of phenotypic variance due to the direct effect in the population. The yellow box shows the ratio of R2 from the sibling difference design based on PGSmix vs. that in a sibling difference design based on PGSdir. Each boxplot shows the simulation results of 200 repeats. In this figure, the variance of direct effect size and the variance of the environmental effect size were fixed at 1 and 3, respectively. The correlation between two sibling’s environments was fixed at 0.5. The two panels correspond to the results when the variance of the indirect effect size is 0.5 and 1, respectively.</p

Summary of calculations for regression estimates.

Researchers often claim that sibling analysis can be used to separate causal genetic effects from the assortment of biases that contaminate most downstream genetic studies (e.g. polygenic score predictors). Indeed, typical results from sibling analysis show large (>50%) attenuations in the associations between polygenic scores and phenotypes compared to non-sibling analysis, consistent with researchers’ expectations about bias reduction. This paper explores these expectations by using family (quad) data and simulations that include indirect genetic effect processes and evaluates the ability of sibling analysis to uncover direct genetic effects of polygenic scores. We find that sibling analysis, in general, fail to uncover direct genetic effects; indeed, these models have both upward and downward biases that are difficult to sign in typical data. When genetic nurture effects exist, sibling analysis creates “measurement error” that attenuates associations between polygenic scores and phenotypes. As the correlation between direct and indirect effect changes, this bias can increase or decrease. Our findings suggest that interpreting results from sibling analysis aimed at uncovering direct genetic effects should be treated with caution.</div

The effect of environmental correlation <i>r</i>_<i>e</i> between siblings on the <i>R</i>² of PGS regression analyses from different study designs.

y-axis shows the ratio of R2 from between family design (red) or sibling difference design (blue) vs. the proportion of phenotypic variance due to the direct effect in the population. Each boxplot shows the simulation results of 200 repeats. In each repeat, we simulated the true SNP effect sizes (βdir and βind) from a bivariate normal distribution, and the environmental effect sizes for siblings from a bivariate normal distribution. We then calculated the phenotype, PGS, and run the linear regression analyses. In this figure, the PGS was calculated using (βdir+βind). The variance of direct effect size and the variance of the environmental effect size were fixed at 1 and 3, respectively. The indirect effect size and the correlation rg between direct and indirect effect were both set to 0. When re = 1, the environmental terms for two siblings become identical, thus their phenotypic difference becomes ΔPGSdir and their PGS difference is also ΔPGSdir since here we set βind = 0. Thus, its R2 is always 1 in each repeat whereas the proportion of the phenotypic variance by the direct effect is ¼. Therefore, the ratio is always 4 for the last setting as shown in the figure.</p

S1 File -

Researchers often claim that sibling analysis can be used to separate causal genetic effects from the assortment of biases that contaminate most downstream genetic studies (e.g. polygenic score predictors). Indeed, typical results from sibling analysis show large (>50%) attenuations in the associations between polygenic scores and phenotypes compared to non-sibling analysis, consistent with researchers’ expectations about bias reduction. This paper explores these expectations by using family (quad) data and simulations that include indirect genetic effect processes and evaluates the ability of sibling analysis to uncover direct genetic effects of polygenic scores. We find that sibling analysis, in general, fail to uncover direct genetic effects; indeed, these models have both upward and downward biases that are difficult to sign in typical data. When genetic nurture effects exist, sibling analysis creates “measurement error” that attenuates associations between polygenic scores and phenotypes. As the correlation between direct and indirect effect changes, this bias can increase or decrease. Our findings suggest that interpreting results from sibling analysis aimed at uncovering direct genetic effects should be treated with caution.</div

The effect of indirect effect size variance on the <i>R</i>² of PGS regression analyses from different study designs.

y-axis shows the ratio of R2 from between family design (red) or sibling difference design (blue) vs. the proportion of phenotypic variance due to the direct effect in the population. The yellow box shows the ratio of R2 from the sibling difference design based on PGSmix vs. that in a sibling difference design based on PGSdir. Each boxplot shows the simulation results of 200 repeats. In this figure, the variance of direct effect size and the variance of the environment effect size were fixed at 1 and 3, respectively. The correlation rg between direct and indirect effect sizes and the correlation re between two sibling’s environments were fixed at 0 and 0.5, respectively. When the indirect effect is 0 (both and βind are 0), the sibling difference analyses become identical regardless of whether the PGS is computed based on (βdir+βind) or βdir, thus the yellow box is fixed at 1 when in this figure.</p

Comparison of patient characteristics between patients with and without delay in discharge at the time of admission to intensive care units.

<p>Comparison of patient characteristics between patients with and without delay in discharge at the time of admission to intensive care units.</p

Incidence of delirium after ICU discharge decision to 48 hours in ward.

<p>Incidence of delirium after ICU discharge decision to 48 hours in ward.</p

Comparisons of duration of delay, reasons for delay and discharge decision revoked.

<p>Comparisons of duration of delay, reasons for delay and discharge decision revoked.</p

Description of patient recruitment.

<p>Description of patient recruitment.</p