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8

Could you please show us the context in which this appears, as you seem to be interpreting this differently to Devon. If it's appearing as you say, GA and AG, then Devon is right, this usually means that the sequence goes ###AG### or ###GA###, which are two very different sequences. If, however, as you're implying, the sequences are actually ###G### or ###...


8

International Mouse Phenotyping Consortium is building a database of phenotypes and knock-outs of mouse. I believe that this database will be fairly complete (20000 knock-outs), but these are knock-outs, not SNPs... There are several mouse GWAS studies, but I am not aware of a database that would pull all the results together. Arabidopsis big GWAS project ...


6

What you are attempting to do is known as LD-pruning. As @Emily_Ensembl said, it is not customary to do this for standard association tests: it is possible that one of the SNPs you remove is causal, or a better proxy for the causal locus, and would give (slightly) better association signal than the other. Even for SNPs in perfect LD, pruning is unwise ...


5

When you use linear mixed models to estimate heritability you assume that the underlying trait is normally distributed which is called a disease liability scale. For continuous traits this is not a problem but for binary traits, this becomes an issue because you have a 0/1 value for a phenotype and usually there is a higher proportion of cases in the study ...


5

Most likely, the GWASs that generated your summary statistics used other imputation panels than 1000G, like HRC. Clearly, PLINK can't estimate the LD for SNPs that aren't found in the reference, and most likely just ignores them. I think the easiest solution is to use whatever individual-level dataset you have to estimate the correlations - even if it's ...


4

Is "user5054" the same as "user5504"? No? Exactly. Not only does order matter, it's incredibly vitally important. AG and GA are completely and totally different from each other. If this is in a coding region, then the resulting amino acid is undoubtedly changed (fun fact, the only exception is TAG and TGA). If it's at a splice site then splicing is likely ...


4

A few days ago, I was trying to find some GWAS datasets to download. I hope this site for 3000 rice will help: http://snp-seek.irri.org/. You can click Genotype in the index page for "Query for SNPs from the 3000 genome project". UPDATE: With the help of user manual, I can get IRIS ID from Genotype. According to this paper, iris id may associate with some ...


4

From my memory of what a statistician told me, a PCA aims to determine independent linear combinations of variables (i.e. genotypes) that account for the most variation in the dataset. With 10 million SNPs, the vectors describing the genotypes of all individuals reside somewhere inside a 10-million-dimension hypersphere, and the first principle component ...


3

If you want to do GWAS or mendelian randomisation, you can do it with Plink (v2) which should be faster than R.


3

You can use imputation for guessing at what the missing SNPs might be based on known LD patterns in populations. This procedure will give you an idea of whether the recombinational history of genomic regions is important, rather than specific SNPs. Given that you're looking at LD-based measures, this should be okay (although the logic behind the calculation ...


3

Most association analyses are carried out at a single SNP level, so AG and GA are likely to indicate a heterozygous genotype at a particular location. However, the precise notation matters. As @Emily_Ensembl has alluded to, for VCF files, A/G indicates unphased SNPs (order unknown, and shouldn't be considered in analyses), whereas A|G indicates phased SNPs (...


3

I cannot think of any principled rationale for choosing this filtering strategy. However, I am going to take a guess that this filtering strategy is supposed to filter out SNPs for which imputation did not work well? In that case the appropriate statistic to filter on is the INFO score as described here. You might consider picking a higher threshold than ...


3

To answer the first part of your question, the dependent and independent variable of X-WAS is kind of arbitrary and dependent on the question you asked. But it gradually becomes a convention in the field after the initial name and concept are accepted by the community. For example, GWAS from the very beginning is written as condition ~ SNP, and there is no ...


3

The first approach only address the question of how likely are you to end up with the observed over-representation given the MAF distribution. My suggestion is to use the second approach, but I am not sure if you would call it bootstrap. Bootstrap in general means sampling with replacement to estimate the uncertainty of a parameter, in this case, OR. So even ...


2

In simple GWAS setups, each SNP is analyzed independently. In those cases you can filter out SNPs with poor INFO scores at any point. For analyses that combine information across SNPs (for example FaSTLMM), I would recommend filtering out SNPs before running the step that combines information across SNPs. So, if you filter right after running Impute2, you ...


2

Your approach is correct - it's not the only one permitted under their conditions, but it would be the standard way given only that information. F-test compares a "full model" (predicting the phenotype using genotype and an intercept) versus a "reduced model" (predicting the phenotype using only the intercept, i.e. the mean value of phenotype). The p-value ...


2

Instead of the "observed phenotype", it's asking for a "corrected phenotype", which is the residuals from pheno ~ covariates. In R, one would get that as follows: fit = lm(phenotype ~ covariates, data) corrected_phenotype = resid(fit) Of course covariates is just a place hold for something like age + gender or something along those lines.


2

Yes, that can be done. A common approach for these types of analysis is to carry out a transformation on the data first in order for it to have a normal distribution (the most general approach called an inverse normal transformation, among other names), then run the test on the transformed data. As an example, for data that are positive and zero-skewed, a ...


2

Yes, this exists, and can be efficiently generated by plink 2.0's --export ind-major-bed command. (The third byte is 0 instead of 1, and the specification is otherwise identical to that of regular plink .bed files with samples and variants swapped.)


2

Try the latest plink 2.0 alpha build for this. It’s an incomplete program, but its implementation of —linear is far better than v1.9’s.


2

Those odds ratio aren't calculated from the raw allele frequencies. They're calculated from coefficients estimated from complex linear models with a bunch of covariates that are run on subsets of the data then meta-analysed together. It is a bit surprising that the odds ratio is less than one when the frequency is higher in cases, but the p-value indicates ...


2

You can use BEDOPS' closest-features tool. You only need to have the SNPs coordinates in .bed format and a .bed file with gene locations. BEDOPS will look for the closest genes upstream and downstream of your SNPs. The command will look something like this: closest-features yourSNPs.bed yourAnnotation.bed Then, you can obtain your functional annotations ...


2

The solution you likely want is here, pd.set_eng_float_format(accuracy=x, use_eng_prefix=True) x = whatever is required The function set_eng_float_format has been moved around a bit and is now a top level function You might be dealing with maximum likelihood, so convert to log likelihood -200 that sort of thing Best idea gwas_rs_id['logged'] = np.log(...


2

PCA = principle component analysis and a multivariate statistic, today it is trendily retermed "unsupervised learning" and here is likely being deployed for individuals within your data set. It works by identifying the maximum variance within multidimensional space, shearing it and describing this as the first principle component. The second principle ...


2

I've got the answer to my question at https://www.biostars.org/p/410466/ by chrchang522 "The main regression executed by Plink was introduced by EIGENSTRAT in ~2006; see https://www.nature.com/articles/ng1847 . This is actually straightforward to write in R/Python from scratch; the harder part is optimizing the implementation for large datasets. The ...


2

Based on the QQ plots, filter out variants with p > 0.01, then display the beta statistic (or whatever other statistic you're using to demonstrate association). If you want to incorporate the p-value in the displayed results, show the minimum test statistic from a 95% confidence interval. Here's an attempt I made at doing that with some GWAS summary ...


2

You can use --pheno to specify a phenotype file you want to use, the 1st and 2nd columns being FID and IID. These are just used on the fly and cannot be written to the BED files as I understand. --make-pheno is used to write binary phenotypes, which is unsuitable if your phenotype is actually continuous. But I still do not quite understand what you want to ...


2

In the end I manage to do 2 cohorts 1: Long Covid Patients (1300N) 2: NON Long Covid Patients that had Covid (1300). It was the most rational thing to do, and professor liked it. So that's how I manage in th end.


2

This is a good idea - low pass sequencing has shown to be better than genotype arrays for things such as GWAS or QTL mapping. The primary way of processing low-pass sequencing data is to use imputation - partciularly methods such as GLIMPSE or QUILT and a large reference panel such as the HRC or TopMed in order to refine the genotype calls. This is a figure ...


2

Yes, they are base pair positions


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