Applies the chi-square test to check if markers are following the expected segregation pattern, i. e., 1:1:1:1 (A), 1:2:1 (B), 3:1 (C) and 1:1 (D) according to OneMap's notation. It does not use Yate's correction.
test.segregation.of.a.marker(x, marker)an object of class onemap, with data and additional information.
the marker which will be tested for its segregation.
a list with the H0 hypothesis being tested, the chi-square statistics, the associated p-values, and the % of individuals genotyped.
It returns NA if the numbers of expected and observed classes are
different or if dominant and co-dominant coding is mixed in the same marker.
data(example.out) # Loads a fake outcross dataset installed with onemap test.segregation.of.a.marker(example.out,1)
First, the function selects the correct segregation pattern, then it defines the H0 hypothesis, and then tests it, together with percentage of missing data.