Table Box 1.

Alogrithm to Prioritize Genes that Change in Disease and Response to Treatment

I.	Statistical considerations based on gene changes in multiple studies
	A gene fold change from control and the significance (p-value) and reproducibility of that change are assigned a 'statistical score':
		Fold change score=the fold change if gene increases, and 1/% of control value if the gene decreases
		p-Value score = −log10(p-value)
		Reproducibility score = × 2 for I replication of co-directional, statistically significant gene change, × 4 for 2 replications
Statistical score = (fold change score × p-value score) reproducibility score
II.	Biological considerations based on human and animal studies of the gene that changed
	A.	Gene change is in the same direction of mRNA of protein change in human disease
	B.	Gene change is in the same direction of mRNA of protein change in animal model of the disease
	C.	Gene or its protein product change in the opposite direction in animals by treatments for that disorder, or when manipulated in transgenic animal, it models part of the disease pathology
	D.	Gene is part of a biochemical pathway associated with human disease or its treatment
	E.	Gene's human homolog is in a chromosomal hot spot for disease as identified by linkage analysis
	Biological score = A (10)+B (8)+C (8)+D (5)+E (2); maximum = 33
	The values in parentheses are summed for each criterion that is true for A–E
III.	Pharmacological considerations
	The score is based on how 'drugable' the target is, and whether it has received support in the past as a drug target
	A. Gene changes in disease or in response to therapeutic agents are found to be under the control of a drugable target (ie, receptor or enzyme antagonist)
	B. For antagonist approach, knockout of the target mimics the desired gene changes, or overexpression mimics the disease phenotype. Gene changes are reversed in knockout model by effective drugs.
	C. Gene or its protein product is changed by effective treatments for that disorder or when manipulated in transgenic animal, predictably affect disease pathology.
Pharmacological score = A (10)+B (9)+C (8); maximum = 27
The values in parentheses are summed for each criterion that is true for A–C
Algorithm score = Statistical score+biological score+pharmacological score
Example of a robust gene:
A gene is doubled in bipolar disorder with a p-value of 10−5, and its increase is replicated in two other studies. If the gene fulfills all biological and pharmacological criteria, its algorithm score will be about the maximum, or (2 × 5) × 4+33+27 = 100

This algorithm includes features that have been used to identify genes associated with schizophrenia (Altar et al, 2008) and the therapeutic response to ECT (Altar et al, 2005). The statistical, experimental, biological, and pharmacological considerations are quantified and summed to prioritize the significance of each gene as a target for CNS drug discovery.