Daniel Gianola (born 1947) is a geneticist based at the University of Wisconsin-Madison (US), reputed for his contributions in quantitative genetics to the fields of animal and plant breeding.

In the early 1980s, Gianola extended best linear unbiased prediction to the non-linear domain for analysis of categorical traits (fertility, survival, resistance to diseases), using the classical threshold model of Sewall Wright.

He also revived early work by Sewall Wright on structural equation models and cast their application in the context of modern quantitative genetics and statistical methodology.

His group in Wisconsin was the first in the world applying non-parametric methods, such as reproducing Kernel Hilbert spaces regression and Bayesian neural networks, to genome-enabled selection in animal breeding, agriculture and whole-genome (i.e., using a massive number of DNA markers) prediction of complex traits or diseases.

After graduating in agricultural engineering from Universidad de la Republica at 23 years of age, Gianola moved to the USA to pursue postgraduate studies (MS and Ph.D degrees at the University of Wisconsin-Madison in 1973 and 1975, working with Professors W. J. Tyler and A.