Let's analyse that. Suppose the frequency of the blue eye gene and the brown eye gene are the same in the general population, and suppose the prior probability of cheating is 10%. Then we can simulate this:

    from random import random, randint
    p_blue = 0.5
    p_cheat = 0.1
    
    def randbool(p): return random() < p
    def randgenes(): return randbool(p_blue), randbool(p_blue)
    def mate(mother, father): return mother[randint(0,1)], father[randint(0,1)]
    def blueeyes(p): return p[0] and p[1]
    
    n_cheat = 0
    n_faithful = 0
    for i in range(0,1000000):
        mother = (True,False)
        grandfather = randgenes()
        grandmother = randgenes()
        husband = mate(grandfather, grandmother)
        lover = randgenes()
        cheated = randbool(p_cheat)
        if cheated:
            kid1 = mate(mother, lover)
            kid2 = mate(mother, lover)
        else:
            kid1 = mate(mother, husband)
            kid2 = mate(mother, husband)
    
        if blueeyes(grandmother) or blueeyes(grandfather) or blueeyes(husband) or not blueeyes(kid1) or not blueeyes(kid2): continue
    
        if cheated: n_cheat += 1
        else: n_faithful += 1
    
    print(n_cheat)
    print(n_faithful)

We get 4619 times cheated and 14226 times faithful. The probability of cheating went up substantially relative to the prior of 10%, but it's still below 30%. I'd say that the chaos wasn't justified.