You made it sound much simpler. But I have a question. What do y1, y2,.....,yn represent? Are they phenotypic scores from different patients(cases) of a particular phenotype? Or something else?? Thank you so much btw.
That's right! Y stands for whatever your response variable is. So it could be whether or not you have a disease or it could be how tall you are: any phenotype!
Good point! Epigenetic modifications (which are any non-DNA changes that affect gene expression, like changes to proteins that make DNA more or less accessible for transcription) can be caused by environmental variables like age, sex, or pollution. For example, both people who went through the Holocaust and their children had higher incidences of schizophrenia, likely in part due to epigenetic modifications (see doi.org/10.1017/S0033291715002354).
Hi Abdullah, this video is part of the GWAS module. You can see the whole playlist at ruclips.net/p/PLvdBkUzU6hPIkloQgFoSRqcC_SlsXvUOP. It's usually easier to access the full videos through our website, big-bio.org.
Welcome! This video is part of the GWAS module. You can see the whole playlist at ruclips.net/p/PLvdBkUzU6hPIkloQgFoSRqcC_SlsXvUOP. It's usually easier to access the full videos through our website, big-bio.org.
Hi Yashee! Thanks for watching! Using an association study design you cannot conclusively assign causality. This means that while we can find that having a given variant is significantly correlated with having diabetes, we can't say that it CAUSES diabetes (or as you put it, is responsible for diabetes). We can just say that you are more likely to have diabetes if you have this variant. Further experiments, like mutating the variant in mice or cells or using other statistical methods would be necessary to confirm causality. This is one of the major limitations of GWAS: although it gives us an idea of where to look for genetic causes of disease it doesn't conclusively prove anything without other types of research.
Thank you very much. It was very simple and informative.
You made it sound much simpler. But I have a question. What do y1, y2,.....,yn represent? Are they phenotypic scores from different patients(cases) of a particular phenotype? Or something else?? Thank you so much btw.
That's right! Y stands for whatever your response variable is. So it could be whether or not you have a disease or it could be how tall you are: any phenotype!
What about epigenetics? Can’t the phenotype cause changes in gene expression over time?
Good point! Epigenetic modifications (which are any non-DNA changes that affect gene expression, like changes to proteins that make DNA more or less accessible for transcription) can be caused by environmental variables like age, sex, or pollution. For example, both people who went through the Holocaust and their children had higher incidences of schizophrenia, likely in part due to epigenetic modifications (see doi.org/10.1017/S0033291715002354).
how can i see the full talk thanks.
Hi Abdullah, this video is part of the GWAS module. You can see the whole playlist at ruclips.net/p/PLvdBkUzU6hPIkloQgFoSRqcC_SlsXvUOP. It's usually easier to access the full videos through our website, big-bio.org.
Hi there, I am currently getting into GWAS and I would like to know where I can find the full video.
Welcome! This video is part of the GWAS module. You can see the whole playlist at ruclips.net/p/PLvdBkUzU6hPIkloQgFoSRqcC_SlsXvUOP. It's usually easier to access the full videos through our website, big-bio.org.
@@BigBiovideos Thank you very much
excellent
But how one can say that this allele is responsible for diabetes.
Hi Yashee! Thanks for watching! Using an association study design you cannot conclusively assign causality. This means that while we can find that having a given variant is significantly correlated with having diabetes, we can't say that it CAUSES diabetes (or as you put it, is responsible for diabetes). We can just say that you are more likely to have diabetes if you have this variant. Further experiments, like mutating the variant in mice or cells or using other statistical methods would be necessary to confirm causality. This is one of the major limitations of GWAS: although it gives us an idea of where to look for genetic causes of disease it doesn't conclusively prove anything without other types of research.
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