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What are the differences between the two methods?

What advantages does one have over the other, and what are their limitations relative to one another?

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    $\begingroup$ This question is too broad, and is prone to subjective answers that aren't very helpful. Please try to narrow your question, preferably thinking about a specific task that you would like to carry out. $\endgroup$
    – gringer
    Dec 9 '17 at 19:46
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The basic difference between GWAS and QTL mapping is that GWAS studies the association between alleles and and a binary trait, such as being a sufferer of a disease, while QTL analysis deals with the contribution of a locus to variation in continuous trait like height.

Thus GWAS normally deals with frequencies (i.e. is the frequency disease probands greater in those with the minor allele compared to the major allele at locus X), whereas QTLs deal in correlations (Is the number of minor alleles at locus Y correlated with height).

Methodologically the primary difference would be the distribution that links the allele to the outcome (disese/continuous phenotype). GWAS is going to use Chi-squared statistics or logistic regression, QTL with more likely use straight up normal distribution based linear regression.

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    $\begingroup$ This is incorrect. $\endgroup$ Jan 26 '18 at 21:50
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Most papers that I've looked at distinguish between looking for genotype/trait associations in mapping populations created by experimental crosses (QTL mapping) and looking for genotype/trait associations in populations of unrelated individuals (GWAS). No distinction is made between susceptibility to disease (often referred to as a case-control GWAS) and continuous traits (all of the big studies on genetic associations with height use the term GWAS, for example).

Many of the papers in my first link use both methods, so you can take a look at them to get an idea of the advantages and limitations of each.

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