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Quantitative trait locus (QTL) mapping techniques are commonly used to investigate genetic architectures, but the scope of inferences drawn from QTL studies are often restricted by the limitations of the experimental designs.
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Regions outside of the scope of inference showed less agreement between the two models.
In particular, we focus on issues of confounding, randomization, nature of inference, single- versus multiple-trait modeling, cow- versus herd-level modeling, and scope of inference, all within the context of dairy production systems.
However, current GWAS based on a simple analysis of associations between genotype and phenotype ignores the biochemical reactions of drug response, thus limiting the scope of inference about its genetic architecture.
LiDAR thus increases the scope of inference for future investigations of how species respond to vegetation structure, which can now readily and contiguously be assessed at relevant grain sizes and across large areas.
Meta-analysis functions to increase the precision of empirical estimates and to broaden the scope of inference, making it a powerful tool for informing forest management and conservation actions around the world.
On the other hand, multiple farm studies are attractive, because they facilitate a broader scope of inference on treatment effects across a wider range of management or climatic conditions and genetic backgrounds compared with single-site university studies.
Comparisons across populations have a broader scope of inference than single-population studies, but correlations across populations can be affected by uncontrolled heterogeneity among sites.
Accounting for variability influenced inferences drawn from experimental findings or scope of inference.
Thus, inherent limitations in the scope of inference result and must be explicitly recognized.
Joint analysis of multiple biparental families offers an alternative approach to QTL mapping with a wider scope of inference.
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