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Wonderful lecture!!! I like the way this professor uses to explain all those concepts
boy I loved this one
Beautiful lecture - Thank you.
Hi, can anyone prescribe a good book that covers this topic and the math involved?
@@maxmiller9311 Thanks a ton
Should the Variance in 20:45 be N*0.5*(1-0.5) ? because the P(x, N) is binomial distribution ?
1:01:26 "The more QTLs that contribute to a particular trait, the smaller they might be." Does he mean the effect of each QTL might me smaller?
dikey y @exactly, the more are they, the less each one will contribute to phenotype, and vice versa.
@@mohitjesani2797 Thank you
At 1:03:10, what does the instructor referring to by "theoratical h^2 they computed" is it h^2 computed from residuals of H^2? In other words what does the y axis on 1:03:28 correspond to ?
Is the y label on 1:02:02 correct? I thought "number of traits" should have been "number of QTLs".
Interest lecturers
E[x]=0.5N Var[x] = 0.25N
Wonderful lecture!!! I like the way this professor uses to explain all those concepts
boy I loved this one
Beautiful lecture - Thank you.
Hi, can anyone prescribe a good book that covers this topic and the math involved?
@@maxmiller9311 Thanks a ton
Should the Variance in 20:45 be N*0.5*(1-0.5) ? because the P(x, N) is binomial distribution ?
1:01:26 "The more QTLs that contribute to a particular trait, the smaller they might be." Does he mean the effect of each QTL might me smaller?
dikey y @exactly, the more are they, the less each one will contribute to phenotype, and vice versa.
@@mohitjesani2797 Thank you
At 1:03:10, what does the instructor referring to by "theoratical h^2 they computed" is it h^2 computed from residuals of H^2? In other words what does the y axis on 1:03:28 correspond to ?
Is the y label on 1:02:02 correct? I thought "number of traits" should have been "number of QTLs".
Interest lecturers
E[x]=0.5N Var[x] = 0.25N