Type 1 error (model 2) and power (model 6) for multiple-imputation based pooling method with sequencing error rate of 0.5%, 1%, and 2%.

<p>Number of simulations is 1000.</p><p>Type 1 error (model 2) and power (model 6) for multiple-imputation based pooling method with sequencing error rate of 0.5%, 1%, and 2%.</p

Additional file 1 of Construction and validation of a cuproptosis-related five-lncRNA signature for predicting prognosis, immune response and drug sensitivity in breast cancer

Additional file 1: Table S1. Risk differential analysis of all differentially expressed genes in high-risk and low-risk groups

Type 1 error for multiple-imputation based pooling method (“pool_MI”), individual sequencing of all samples (“seq_all”) and pooling without considering other risk factors (“pool_univariate”).

<p>The significance level  = .05 for model 1–3, and 10⁻⁴ for model 4. Number of simulations is 1000 for model 1–3, and 100,000 for model 4.</p><p>Type 1 error for multiple-imputation based pooling method (“pool_MI”), individual sequencing of all samples (“seq_all”) and pooling without considering other risk factors (“pool_univariate”).</p

Design of DNA Pooling to Allow Incorporation of Covariates in Rare Variants Analysis

<div><p>Background</p><p>Rapid advances in next-generation sequencing technologies facilitate genetic association studies of an increasingly wide array of rare variants. To capture the rare or less common variants, a large number of individuals will be needed. However, the cost of a large scale study using whole genome or exome sequencing is still high. DNA pooling can serve as a cost-effective approach, but with a potential limitation that the identity of individual genomes would be lost and therefore individual characteristics and environmental factors could not be adjusted in association analysis, which may result in power loss and a biased estimate of genetic effect.</p><p>Methods</p><p>For case-control studies, we propose a design strategy for pool creation and an analysis strategy that allows covariate adjustment, using multiple imputation technique.</p><p>Results</p><p>Simulations show that our approach can obtain reasonable estimate for genotypic effect with only slight loss of power compared to the much more expensive approach of sequencing individual genomes.</p><p>Conclusion</p><p>Our design and analysis strategies enable more powerful and cost-effective sequencing studies of complex diseases, while allowing incorporation of covariate adjustment.</p></div

Characteristics of simulation models.

<p>*. The simulation model consists of 5 covariates, each with OR of 1.5. In analysis, we assume that only the last two covariates are considered.</p><p>$. In analysis, a more stringent threshold (10⁻⁴) is used for significance, compared to other simulation models (.05).</p><p>n: number of cases, assuming case:control ratio of 1∶1; RAF: risk allele frequency; OR_g: odds ratio for risk allele; OR_z: odds ratios for covariates; corr: correlation coefficient between causal variant and the last covariate; α_max: variation in sample proportions (see “<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114523#s2" target="_blank">Methods</a>”). Model 1–4 were simulated under the null hypothesis of no association; and model 5–12 were under the alternative hypothesis. Model 1 and 5 were treated as baseline models, and changes of parameters in other models were highlighted.</p><p>Characteristics of simulation models.</p

Power for multiple-imputation based pooling method (“pool_MI-prob”), individual sequencing of all samples (“seq_all”) and pooling without considering other risk factors (“pool_univariate”).

<p>*. Power adjusted for the nominal false positive rates.</p><p>The significance level  = .05 (10⁻⁴ for model 11). Number of simulations is 1000.</p><p>Power for multiple-imputation based pooling method (“pool_MI-prob”), individual sequencing of all samples (“seq_all”) and pooling without considering other risk factors (“pool_univariate”).</p

Power for individual sequencing of all samples, pooling with individual genotype imputed, and pooling without considering other risk factors.

<p>The simulation setting is described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114523#pone-0114523-t001" target="_blank">Table 1</a>, model 5, but with different odds ratio for the covariates (OR_z). Number of simulations is 200 for each setting.</p

Design of DNA pooling with sample matching.

<p>After sample matching and pool creation, the pools are grouped into <i>K</i> groups, with allele frequency in each group denoted by (<i>p</i>₁, …, <i>p_K</i>). Pools from the same groups are randomly distributed into <i>M</i> lanes, with sequencing errors (<i>e</i>₁, …, <i>e_M</i>).</p

Power for individual sequencing of all samples, pooling with individual genotype imputed, and pooling without considering other risk factors.

<p>The simulation setting is described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114523#pone-0114523-t001" target="_blank">Table 1</a>, model 5, but with different odds ratio for the risk allele (OR_g). Number of simulations is 200 for each setting.</p

Power for individual sequencing of all samples, pooling with individual genotype imputed, and pooling without considering other risk factors.

<p>The simulation setting is similar to that described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114523#pone-0114523-t001" target="_blank">Table 1</a>, model 5, but with different risk allele frequency (RAF) with n = 5000 cases/controls, and OR_g = 2. Number of simulations is 200 for each setting.</p