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18

There’s an XKCD comic which explains the problem. Unfortunately, that comic is too big to post here. Briefly, a p-value of 0.1 says (roughly) that there’s a 10% chance (0.1) of the observed result being as extreme1 as it is simply due to chance (sampling variation from a population), assuming the null hypothesis is true. Often, 5% is more or less ...


5

Part of the increase in power to detect a genetic association via GWAS-genotyping comes from long haplotypes. Many dog breeds went through selective breeding bottlenecks 100-200 years ago. Many lab model organisms, such as flies, worms and plants, have recombinant inbred lines that aid in GWAS discovery as well. The price one pays for this increased power, ...


4

As an elaboration of my comment. Summary: Replication is required in GWAS studies to account for non-random technical biases. An example of such bias is, for example, a chip used for genotyping giving consistently incorrect genotypes for a locus. In this situation adding more subjects will not correct for this effect and therefore the only solution is to ...


3

I found a review paper that addresses your questions, see Schaid, Chen, Larson (2018): From genome-wide associations to candidate causal variants by statistical fine-mapping Tag SNP. You correctly identified tag SNPs. From the paper: [T]he SNPs on microarrays, called tag SNPs... Lead and index SNP. These are apparantly synonyms. From the paper: ...


3

[W]hat is the Major allele [frequency]? If the Minor Allele Frequency is $p$, then, for a bi-allelic locus, the major allele frequency is obviously $1-p$. [W]hy reporting the second most frequent allele is helpful? Most polymorphic loci of interest are bi-allelic. Hence, the MAF is an indication of genetic diversity. One could similarly report the ...


3

Found a tool: MUMmer is a system for rapidly aligning entire genomes. The current version (release 3.0) can find all 20 base pair maximal exact matches between two bacterial genomes of ~5 million base pairs each in 20 seconds, using 90 MB of memory, on a typical 1.8 GHz Linux desktop computer. If you know a better programmatic approach, let me know, ...


3

In GWAS we are interested in understanding which SNP has a causal influence on a specific phenotype. At the moment large scale studies are carried out by genotyping arrays. For each SNP we put on the array, we measure the alleles present in the patient. The cost of genotyping obviously depends on how many SNPs we would like to measure. So the problem when ...


3

eQTLs (expression quantitative trait loci) are variants that affect the expression of one or more genes. There have been several 'genome-wide' studies of SNPs that directly affect expression. The actual effect sizes are hard to pin down in most of them, but in the supplementary data for this paper is a list of the SNPs with the largest effects and ...


2

I'd argue this actually belongs on CrossValidated. Essentially, the problem is one of how a GWAS study is conducted. By looking over an entire genome for associations, you're actually conducting thousands or millions of experiments, not the single experiment most statistics were designed to handle. As such, you're going to find many results that meet the ...


2

My first thought was to suggest a mixed effects model, so I will describe that first. But having had a Google there are models known as "Conditional logistic regression" models in which you can include any data structure as a covariate, which may be more appropriate, but I cannot vouch for them as I haven't used them. I would suggest reading the ...


2

One possible reason to not filter for linkage disequilibrium is to produce peaks/hits with multiple SNPs, thereby clearly indicating a region of a causal genotype. For example, in the manhattan plot below, each point represents a SNP, the higher it is in the plot the more it relates to the phenotype. We see that in each of the peaks multiple points are ...


2

'Typing' is an SNV-typing. It is an explicit SNV detection using Affymetrix GeneChip arrays, Illumina BeadArray. 'Imputing' is an imputation. It is a statistical inferring from existing genotype data using haplotype estimation. It is useful because different genotyping arrays genotype a slightly different set of SNVs and they can't detect all variants as ...


2

I will focus more on the question of rarity, which the comments have not addressed. Recall that natural selection is operating on the human population. Because of this, bad mutations of large effect (disease mutations such as CFTR in your figure) are very rare, because selection is removing them at some rate from the population (people with cystic fibrosis ...


1

I'm not sure what you mean by "complex", but many diseases are known to arise due to exposure to toxins or to an absence of essential nutrients in the environment. Toxic metals: A good example of this is exposure to some metals including arsenic, lead, and mercury. See for example this information from the US NIH and the wikipedia article on metal ...


1

Microsatellites could be used for GWAS. Actually they were the basis for linkage studies and were also used in the first association studies. The main reason why they have been replaced by SNPs is that in the human (or other organims') genome, there are by far more SNPs than microsatellites. GWAS rely on linkage disequilibrium between the marker and the ...


1

Simply, First of all, you can group samples as survived animals and dead samples and you have a binary phenotype. In a different approach, you can use survival time (time to death) as an ordinal variable phenotype. If you want you can utilize survived sample by assign a large death time value to them. In the third approach, you can cluster the survival ...


1

Why haven't you tried using LiftOver to convert the coordinates? https://genome.ucsc.edu/cgi-bin/hgLiftOver


1

For statistical genetics, take a look at: Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics. Daniel Sorenson and Daniel Gianola. (2002. Springer). Warning: heavy on math (calculus). If that doesn't scare you, it's excellent guide to non-frequentist statistical methods applied to problems in genetics.


1

The NCBI dbSNP entry shows rs7442295 to be in the gene SLC2A9, not SCL2A9 stated in the original question. As the Wikipedia article for SLC2A9 says, synonymous names for SLC2A9 include: solute carrier family 2 (facilitated glucose transporter), member 9, GLUT9, GLUTX, UAQTL2, URATv1, solute carrier family 2 member 9 So GLUT9 and SLC2A9 refer to the same ...


1

No, they are different altogether. Typing in GWAS is genotyping individuals from a selected population to know say the genetic variation of SNP's (single nucleotide polymorphisms) among them via chips like Illumina Core-Exome etc. Imputing is the statistical process of imputation of SNP's of an individual from a reference source like the HapMap reference ...


1

As with all serious scientific result GWAS results need to validated by others. In this case it think is extremely important because these studies link mutations to diseases or in more general given genotypes to phenotypes, thus pointing out possible causes. So validating these results with the use of independent "samples" is indeed crucial. But as I said ...


1

You've picked a fascinating and very important organism to study. Unfortunately there are many many steps with many software packages that you'll need and each next step will depend on what you find making any particular tutorial nigh on pointless. I'm sorry to say that I don't think anyone will dump an entire project proposal or workflow as an answer. I'll ...


1

When you design the arrays, you need to have probes on the surface complementary to the sequence you want to detect. Depending on what you want to detect, you need to design these probes with known sequence on a known position. If you want to detect single nucleotide polymorphisms (SNP), then you need a library of known SNPs on your ChIP, which are basically ...


1

Too long for a comment, but sort of: Mainly you do this because these populations have segregated long time ago and developed differently, so it is more likely to identify these polymorphisms. It is also helpful to use populations for this which mixed not too much with other populations (thats why north-americans are usually not used here, as America was ...


1

In short, yes. If a gwas study links a SNP to a particular phenotype then yes, it is an effect of a single copy. Bear in mind, however, that a SNP is not a knockout or even a knockdown. It can be, but it is not always the case. SNPs can produce a change in the protein sequence or in the regulation of the production of that protein. Both types of variation ...


1

There is no literature report saying such a thing. However, I did a cursory check for GWAS study on neuroblastoma. Selected SNPs with p-value>0.05 Converted p-values to a score — -log10(p-value) Mapped the SNPs to genes while calculating cumulative score for a gene I just sorted the genes based on their names, assuming that many paralogs have similar ...


1

question looks like it's been dormant for a while, but i think there's some discussion to be had here- I would argue that in many (most?) of the model organisms, power would be much greater than humans. Frequently (worms, mice, plants, yeast) you can work with basically isogenic inbred lines. I would argue this is much more important than long haplotypes: a)...


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