Genome wide association studies (GWAS) Meta-analysis
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3.5 years ago

Hi,

I want to do meta-analysis (according to my research goals) by summaries of 60 articles. Due to absence of database for GWAS of livestock, can this meta-analysis be done by the statistical summaries available in the articles? Is the PLINK software capable of summaries analyzing of 60 articles? What software do you recommend for GWAS meta-analysis in estimating models with fixed and random effects?

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Hello,

I have access to the parameters of the ID SNP, BTA, GeneName, Position, MAF, Estimate, SE, P-value for GWAS meta-analysis, but I do not have OR in some articles. Is there a way to calculate it? Can something be replaced?

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Please use ADD COMMENT/ADD REPLY when responding to existing posts to keep threads logically organized. This comment belongs under @Kevin's answer.

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If you have the allele tallies, you can easily calculate odds ratios: A: SNP dataset and Z Score

CHR  SNP  BP  A1  C_A  C_U  A2  CHISQ  P  OR  SE  L95  U95


I doubt that you have that for every study, in which case your best option may be METAL.

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Hello Mr Blighe

I do not have a case and control in my study. I work with quantitative traits. Is there a way to calculate OR for quantitative traits?

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So, if not case / control, what is your outcome variable, i.e., your phenotype?

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Yes, phenotype traits.

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Cool. What are you ultimately aiming to do? - use genotypes to predict the levels of continuous traits / phenotypes?

Can you confirm what is the BTA column in your data? - this is likely the beta coefficient. If you obtain the exponent of this, then you can have the odds ratio. For example (in R):

exp(BTA)


If they are the beta coefficients, then there are likely both positive and negative values in the list.

Can you confirm?

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I want to work on the GWAS meta-analysis in QTL mapping for phenotype traits. Using systematic overviews such as meta-analysis, by aggregating existing information, increase the accuracy of the results of genomic predictions and QTL mapping.

To work with METAL, headers mentioned in METAL doucumentation :

MARKERLABEL SNP

ALLELELABELS RefAllele NonRefAllele

PVALUELABEL P-value

EFFECTLABEL Effect or OR

I do not work with the database and collect information manually. Because I did not find any database associated with GWAS livestock for research that has been done so far.

And I only have this information:

SNP

ALLELELABELS RefAllele NonRefAllele

BTA

Position

MAF

SE

P-value

I just do not have OR or Beta. I think the odds ratio in estimating models with fixed and random effects is necessary. Of course, METAL estimates only fixed effects.

If I approve the BTA column in the R software, does it respond? Does METAL not give errors despite these values (positive and negative)?

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Can you point me to one of these studies?

I am not saying that the BTA values are the ORs. I am saying that they may be the beta coefficients (based on the name) - it is your job to find out to what this column means. Obtaining the exponent of a beta coefficient produces the OR.

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Hello,

For example: (SNP name: rs41646593, BTA:1, Position (bp): 2515412, Gene name:MIS18A)

Here BTA = 1, as you said: Can you confirm what is the BTA column in your data? - this is likely the beta coefficient. If you obtain the exponent of this, then you can have the odds ratio. For example (in R):exp (BTA)

So I got in R this value to BTA: exp (1)= 2.718282

Is this the same beta coefficient or OR ?

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What are some of the other BTA values? What is the range of these?

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In my first study, I have these BTAs:

exp(1): 2.718282, exp(3):20.08554, exp(5): 148.4132, exp(8):2980.958, exp(9):8103.084, exp(11):59874.14, exp(14):1202604, exp(18):65659969, exp(19): 178482301, exp (21):1318815734, exp(23):9744803446

Ranged from 2.72 to 9744803446.

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Can you direct me to this first study so that I can take a look? The description of the results should be in the manuscript, somewhere.

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In Case-control study, effect size is mentioned in the terms of OR(odds-ratio) and in quantitative traits, effect size is mentioned in the terms of BETA. So if you have beta column then it is your effect size. If you use plink for GWAS then these are the terms for effect size.

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3.5 years ago

It depends on what exactly is available in these manuscripts? - p-values and odds ratios? You will likely have to devote some considerable time in formatting the output statistics from these, which is fine of course.

plink --meta-analysis could handle this for you. Take a look HERE

METAL is another popular program.

Kevin