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Overall, PLINK consistently generated the highest regression power estimates for detecting autozygosity, outperforming GERMLINE and BEAGLE.
Logistic regression power calculation was carried out using PASS software version 13.0.10 (NCSS statistical Software, Kaysville, UT).
Our estimated d' values are included in the table of our top regression power estimates (see Results).
Not surprisingly, higher genotyping error rates generally led to lower regression power estimates in PLINK and GERMLINE.
PLINK consistently outperformed both GERMLINE and BEAGLE, producing higher regression power estimates for detecting autozygosity within 20 and 50 generations, regardless of the genotyping error rate.
Table 2 lists the top regression power results within the past 20 and 50 generations for both high and low genotyping error rates.
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Subsequently, the calculation formulae for physical indexes are proposed through the multiple linear regression and power regression.
The control law for developing optimal strategy are achieved by cluster regression for power distribution data.
And then, the calculation formulae for physical indexes are obtained by the multiple linear regression and power regression analysis by defining dimensionless ratios of the after-curing physical indexes to the initial ones.
We repeated the logistic regression and power analysis by selecting a 60% random subsample of the controls that results into similar sample size as that of the cases (results not presented), which yielded very similar results, qualitatively and quantitatively.
Thus, instead of using the standard exponential link function in poisson regression, the power of 5 was used.
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