Mercy Wamalwa, Yes I agree with Mazen Kaktout; I would add that in a biparental design, increasing the population size increases the probability of encountering more linkage blocks that differ between the individuals in that population. One have to keep an eye on the marker density too.
Mercy Wamalwa, Yes I agree with Mazen Kaktout; I would add that in a biparental design, increasing the population size increases the probability of encountering more linkage blocks that differ between the individuals in that population. One have to keep an eye on the marker density too.
Hello Rashed Iam trying to carry out a qtl analysis with a population size of 72 genotypes bi-parental population. Kindly share the outcome of your analysis if it would be possible for me to do the analysis with such population size. Thanks
Hello Rashed Iam trying to carry out a qtl analysis with a population size of 72 genotypes bi-parental population. Kindly share the outcome of your analysis if it would be possible for me to do the analysis with such population size. Thanks
Whatever your population size is, you can perform the marker trait association analysis to detect QTLs for your target traits. Of course if you have suitable marker density. But your population size would affect the reliability of your results. For example, in my research I anlized with a population size of 50 F2 plants and then increased into 100 plants and performed the analysis again. Some QTL peaks detected at the lower size disappeared when the population size was increased. And some others were further confirmed. On the other hand, some QTls were not detected with 50 F2 plants analyses but they were significant with 100 plants. Some journals considers 100 indiciduals is a minimum size to consider the QTL analysis and the paper for publication. And this is widely accepted I guess.
Whatever your population size is, you can perform the marker trait association analysis to detect QTLs for your target traits. Of course if you have suitable marker density. But your population size would affect the reliability of your results. For example, in my research I anlized with a population size of 50 F2 plants and then increased into 100 plants and performed the analysis again. Some QTL peaks detected at the lower size disappeared when the population size was increased. And some others were further confirmed. On the other hand, some QTls were not detected with 50 F2 plants analyses but they were significant with 100 plants. Some journals considers 100 indiciduals is a minimum size to consider the QTL analysis and the paper for publication. And this is widely accepted I guess.
Hi Kazeem Ajasa, I hope you got an answer to your question (being nearly 2 years since). For the benefit of others, you need to understand the basis of QTL mapping. One traditionally "conventional mistake" researchers make is to assume that QTL mapping is interchangeable with biparental linkage mapping. To answer your question in many words, you can get QTL in any analysis (with the correct algorithm) if there exist polymorphic features and trait. A polymorphic feature can only be determined to be 'polymorphic' 1) by comparing its nature in A versus its nature in B (e.g., parent A versus parent B); Or, 2) by comparing its nature between groups of A, B, C, D...and so on (e.g., different unrelated individuals or individuals of different genetic backgrounds).
From (1) above, you get populations such as F2, BCs, RILs and so on. From (2) above, you get panels of populations for association studies, GBS, etc. In all these, you can end up with QTL.
Ask yourself, from which individuals are the sequences, or polymorphic features derived and how are these individuals related to each other?
Hi Kazeem Ajasa, I hope you got an answer to your question (being nearly 2 years since). For the benefit of others, you need to understand the basis of QTL mapping. One traditionally "conventional mistake" researchers make is to assume that QTL mapping is interchangeable with biparental linkage mapping. To answer your question in many words, you can get QTL in any analysis (with the correct algorithm) if there exist polymorphic features and trait. A polymorphic feature can only be determined to be 'polymorphic' 1) by comparing its nature in A versus its nature in B (e.g., parent A versus parent B); Or, 2) by comparing its nature between groups of A, B, C, D...and so on (e.g., different unrelated individuals or individuals of different genetic backgrounds).
From (1) above, you get populations such as F2, BCs, RILs and so on. From (2) above, you get panels of populations for association studies, GBS, etc. In all these, you can end up with QTL.
Ask yourself, from which individuals are the sequences, or polymorphic features derived and how are these individuals related to each other?
My question is that must the population to be used for QTL be from a cross on any line or single population
My question is that must the population to be used for QTL be from a cross on any line or single population
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Thank you Mazel for your quick reply. I will consider this and go ahead with the analysis not for publication but maybe for my thesis.
Many Thanks
Thank you Mazel for your quick reply. I will consider this and go ahead with the analysis not for publication but maybe for my thesis.
Many Thanks
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You are welcome
You are welcome
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Mercy Wamalwa, Yes I agree with Mazen Kaktout; I would add that in a biparental design, increasing the population size increases the probability of encountering more linkage blocks that differ between the individuals in that population. One have to keep an eye on the marker density too.
Mercy Wamalwa, Yes I agree with Mazen Kaktout; I would add that in a biparental design, increasing the population size increases the probability of encountering more linkage blocks that differ between the individuals in that population. One have to keep an eye on the marker density too.
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Thank you so much for your replies. They are really helpful and i'll try to update you once i finished data analysis.
Many thanks ...
Thank you so much for your replies. They are really helpful and i'll try to update you once i finished data analysis.
Many thanks ...
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Hello Rashed
Iam trying to carry out a qtl analysis with a population size of 72 genotypes bi-parental population. Kindly share the outcome of your analysis if it would be possible for me to do the analysis with such population size.
Thanks
Hello Rashed
Iam trying to carry out a qtl analysis with a population size of 72 genotypes bi-parental population. Kindly share the outcome of your analysis if it would be possible for me to do the analysis with such population size.
Thanks
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Hello Rashed
My name is Reynaldo from Chile, Did you publish your result? just let me know
Hello Rashed
My name is Reynaldo from Chile, Did you publish your result? just let me know
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What is the minimum number of polymorphic SSR marker we can use to map a DH population of hexaploid wheat?
What is the minimum number of polymorphic SSR marker we can use to map a DH population of hexaploid wheat?
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Hello Mercy,
Whatever your population size is, you can perform the marker trait association analysis to detect QTLs for your target traits. Of course if you have suitable marker density. But your population size would affect the reliability of your results. For example, in my research I anlized with a population size of 50 F2 plants and then increased into 100 plants and performed the analysis again. Some QTL peaks detected at the lower size disappeared when the population size was increased. And some others were further confirmed. On the other hand, some QTls were not detected with 50 F2 plants analyses but they were significant with 100 plants.
Some journals considers 100 indiciduals is a minimum size to consider the QTL analysis and the paper for publication. And this is widely accepted I guess.
Good luck with your analysis.
Hello Mercy,
Whatever your population size is, you can perform the marker trait association analysis to detect QTLs for your target traits. Of course if you have suitable marker density. But your population size would affect the reliability of your results. For example, in my research I anlized with a population size of 50 F2 plants and then increased into 100 plants and performed the analysis again. Some QTL peaks detected at the lower size disappeared when the population size was increased. And some others were further confirmed. On the other hand, some QTls were not detected with 50 F2 plants analyses but they were significant with 100 plants.
Some journals considers 100 indiciduals is a minimum size to consider the QTL analysis and the paper for publication. And this is widely accepted I guess.
Good luck with your analysis.
More
VOTE
Hi Kazeem Ajasa, I hope you got an answer to your question (being nearly 2 years since).
For the benefit of others, you need to understand the basis of QTL mapping. One traditionally "conventional mistake" researchers make is to assume that QTL mapping is interchangeable with biparental linkage mapping.
To answer your question in many words, you can get QTL in any analysis (with the correct algorithm) if there exist polymorphic features and trait. A polymorphic feature can only be determined to be 'polymorphic'
1) by comparing its nature in A versus its nature in B (e.g., parent A versus parent B); Or,
2) by comparing its nature between groups of A, B, C, D...and so on (e.g., different unrelated individuals or individuals of different genetic backgrounds).
From (1) above, you get populations such as F2, BCs, RILs and so on. From (2) above, you get panels of populations for association studies, GBS, etc. In all these, you can end up with QTL.
Ask yourself, from which individuals are the sequences, or polymorphic features derived and how are these individuals related to each other?
Let me know if you have further queries.
Hi Kazeem Ajasa, I hope you got an answer to your question (being nearly 2 years since).
For the benefit of others, you need to understand the basis of QTL mapping. One traditionally "conventional mistake" researchers make is to assume that QTL mapping is interchangeable with biparental linkage mapping.
To answer your question in many words, you can get QTL in any analysis (with the correct algorithm) if there exist polymorphic features and trait. A polymorphic feature can only be determined to be 'polymorphic'
1) by comparing its nature in A versus its nature in B (e.g., parent A versus parent B); Or,
2) by comparing its nature between groups of A, B, C, D...and so on (e.g., different unrelated individuals or individuals of different genetic backgrounds).
From (1) above, you get populations such as F2, BCs, RILs and so on. From (2) above, you get panels of populations for association studies, GBS, etc. In all these, you can end up with QTL.
Ask yourself, from which individuals are the sequences, or polymorphic features derived and how are these individuals related to each other?
Let me know if you have further queries.
More
VOTE