19. Discovering Quantitative Trait Loci (QTLs)

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  • Опубликовано: 4 ноя 2024

Комментарии • 14

  • @sasakevin3263
    @sasakevin3263 2 года назад +1

    Wonderful lecture!!! I like the way this professor uses to explain all those concepts

  • @Mrobo04
    @Mrobo04 4 года назад +3

    boy I loved this one

  • @lotussreerengini5815
    @lotussreerengini5815 7 лет назад +3

    Beautiful lecture - Thank you.

  • @deeptirao5982
    @deeptirao5982 4 года назад +3

    Hi, can anyone prescribe a good book that covers this topic and the math involved?

  • @chenxizhou262
    @chenxizhou262 4 года назад

    Should the Variance in 20:45 be N*0.5*(1-0.5) ? because the P(x, N) is binomial distribution ?

  • @tempvariable
    @tempvariable 5 лет назад +1

    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?

    • @mohitjesani2797
      @mohitjesani2797 4 года назад +2

      dikey y @exactly, the more are they, the less each one will contribute to phenotype, and vice versa.

    • @tempvariable
      @tempvariable 4 года назад +1

      @@mohitjesani2797 Thank you

  • @tempvariable
    @tempvariable 5 лет назад

    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 ?

  • @tempvariable
    @tempvariable 5 лет назад

    Is the y label on 1:02:02 correct? I thought "number of traits" should have been "number of QTLs".

  • @salihhassan9888
    @salihhassan9888 5 лет назад +2

    Interest lecturers

  • @MrQhenryQ
    @MrQhenryQ 7 лет назад +1

    E[x]=0.5N Var[x] = 0.25N