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### Overview

### 1. Preliminary assumptions / limitations

#### Quality

#### Ploidy

#### Paired end reads

### 2. Calculating genotype likelihoods using Bayes' Theorem

### 3. Selecting a genotype and emitting the call record

This document describes the procedure used by HaplotypeCaller to assign genotypes to individual samples based on the allele likelihoods calculated in the previous step. For more context information on how this fits into the overall HaplotypeCaller method, please see the more general HaplotypeCaller documentation.

Note that this describes the **regular mode** of HaplotypeCaller, which does not emit an estimate of reference confidence. For details on how the reference confidence model works and is applied in `-ERC`

modes (`GVCF`

and `BP_RESOLUTION`

) please see the reference confidence model documentation.

The previous step produced a table of per-read allele likelihoods for each candidate variant site under consideration. Now, all that remains to do is to evaluate those likelihoods in aggregate to determine what is the most likely genotype of the sample at each site. This is done by applying Bayes' theorem to calculate the likelihoods of each possible genotype, and selecting the most likely. This produces a genotype call as well as the calculation of various metrics that will be annotated in the output VCF if a variant call is emitted.

Keep in mind that we are trying to infer the genotype of each sample given the observed sequence data, so the degree of confidence we can have in a genotype depends on both the quality and the quantity of the available data. By definition, low coverage and low quality will both lead to lower confidence calls. The GATK only uses reads that satisfy certain mapping quality thresholds, and only uses “good” bases that satisfy certain base quality thresholds (see documentation for default values).

Both the HaplotypeCaller and GenotypeGVCFs (but not UnifiedGenotyper) assume that the organism of study is diploid by default, but desired ploidy can be set using the `-ploidy`

argument. The ploidy is taken into account in the mathematical development of the Bayesian calculation. The generalized form of the genotyping algorithm that can handle ploidies other than 2 is available as of version 3.3-0. Note that using ploidy for pooled experiments is subject to some practical limitations due to the number of possible combinations resulting from the interaction between ploidy and the number of alternate alleles that are considered (currently, max ploidy = 20 for max alt alleles = 6). Future developments will aim to mitigate those limitations.

Reads that are mates in the same pair are not handled together in the reassembly, but if they overlap, there is some special handling to ensure they are not counted as independent observations.

We use the approach described in [Li2011] to calculate the posterior probabilities of non-reference alleles (Methods 2.3.5 and 2.3.6) extended to handle multi-allelic variation.

The basic formula we use for all types of variation under consideration (SNPs, insertions and deletions) is:

$$ P(G|D) = \frac{ P(G) P(D|G) }{ \sum_{i} P(G_i) P(D|G_i) } $$

If that is meaningless to you, please don't freak out -- we're going to break it down and go through all the components one by one. First of all, the term on the left:

$$ P(G|D) $$

is the quantity we are trying to calculate for each possible genotype: the conditional probability of the genotype **G** given the observed data **D**.

Now let's break down the term on the right:

$$ \frac{ P(G) P(D|G) }{ \sum_{i} P(G_i) P(D|G_i) } $$

We can ignore the denominator (bottom of the fraction) because it ends up being the same for all the genotypes, and the point of calculating this likelihood is to determine the most likely genotype. The important part is the numerator (top of the fraction):

$$ P(G) P(D|G) $$

which is composed of two things: the prior probability of the genotype and the conditional probability of the data given the genotype.

The first one is the easiest to understand. The prior probability of the genotype **G**:

$$ P(G) $$

represents how probably we expect to see this genotype based on previous observations, studies of the population, and so on. By default, the GATK tools use a flat prior (always the same value) but you can input your own set of priors if you have information about the frequency of certain genotypes in the population you're studying.

The second one is a little trickier to understand if you're not familiar with Bayesian statistics. It is called the conditional probability of the data given the genotype, but what does that mean? Assuming that the genotype **G** is the true genotype,

$$ P(D|G) $$

is the probability of observing the sequence data that we have in hand. That is, how likely would we be to pull out a read with a particular sequence from an individual that has this particular genotype? We don't have that number yet, so this requires a little more calculation, using the following formula:

$$ P(D|G) = \prod{j} \left( \frac{P(D_j | H_1)}{2} + \frac{P(D_j | H_2)}{2} \right) $$

You'll notice that this is where the diploid assumption comes into play, since here we decomposed the genotype **G** into:

$$ G = H_1H_2 $$

which allows for exactly two possible haplotypes. In future versions we'll have a generalized form of this that will allow for any number of haplotypes.

Now, back to our calculation, what's left to figure out is this:

$$ P(D_j|H_n) $$

which as it turns out is the conditional probability of the data given a particular haplotype (or specifically, a particular allele), aggregated over all supporting reads. Conveniently, that is exactly what we calculated in Step 3 of the HaplotypeCaller process, when we used the PairHMM to produce the likelihoods of each read against each haplotype, and then marginalized them to find the likelihoods of each read for each allele under consideration. So all we have to do at this point is plug the values from that table into the equation above, and we can work our way back up to obtain:

$$ P(G|D) $$

for the genotype **G**.

We go through the process of calculating a likelihood for each possible genotype based on the alleles that were observed at the site, considering every possible combination of alleles. For example, if we see an A, T, and C at a site, the possible genotypes are AA, AT, AC, TT, TC, and CC, and we end up with 6 corresponding probabilities. We pick the largest one, which corresponds to the most likely genotype, and assign that to the sample.

Note that depending on the variant calling options specified in the command-line, we may only emit records for actual variant sites (where at least one sample has a genotype other than homozygous-reference) or we may also emit records for reference sites. The latter is discussed in the reference confidence model documentation.

Assuming that we have a non-ref genotype, all that remains is to calculate the various site-level and genotype-level metrics that will be emitted as annotations in the variant record. For details of how these metrics are calculated, please see the variant annotations documentation.

Comments (6)

### Overview

### 1. Evaluating the evidence for each candidate haplotype

### 2. Evaluating the evidence for each candidate site and corresponding alleles

This document describes the procedure used by HaplotypeCaller to evaluate the evidence for variant alleles based on candidate haplotypes determined in the previous step for a given ActiveRegion. For more context information on how this fits into the overall HaplotypeCaller method, please see the more general HaplotypeCaller documentation.

The previous step produced a list of candidate haplotypes for each ActiveRegion, as well as a list of candidate variant sites borne by the non-reference haplotypes. Now, we need to evaluate how much evidence there is in the data to support each haplotype. This is done by aligning each sequence read to each haplotype using the PairHMM algorithm, which produces per-read likelihoods for each haplotype. From that, we'll be able to derive how much evidence there is in the data to support each variant allele at the candidate sites, and that produces the actual numbers that will finally be used to assign a genotype to the sample.

We originally obtained our list of haplotypes for the ActiveRegion by constructing an assembly graph and selecting the most likely paths in the graph by counting the number of supporting reads for each path. That was a fairly naive evaluation of the evidence, done over all reads in aggregate, and was only meant to serve as a preliminary filter to whittle down the number of possible combinations that we're going to look at in this next step.

Now we want to do a much more thorough evaluation of how much evidence we have for each haplotype. So we're going to take each individual read and align it against each haplotype in turn (including the reference haplotype) using the PairHMM algorithm (see Durbin *et al.*, 1998). If you're not familiar with PairHMM, it's a lot like the BLAST algorithm, in that it's a pairwise alignment method that uses a Hidden Markov Model (HMM) and produces a likelihood score. In this use of the PairHMM, the output score expresses the likelihood of observing the read given the haplotype by taking into account the information we have about the quality of the data (i.e. the base quality scores and indel quality scores).

This produces a big table of likelihoods where the columns are haplotypes and the rows are individual sequence reads. **(example figure TBD)**

The table essentially represents how much supporting evidence there is for each haplotype (including the reference), itemized by read.

Having per-read likelihoods for entire haplotypes is great, but ultimately we want to know how much evidence there is for individual alleles at the candidate sites that we identified in the previous step. To find out, we take the per-read likelihoods of the haplotypes and **marginalize them over alleles**, which produces per-read likelihoods for each allele at a given site. In practice, this means that for each candidate site, we're going to decide how much support each read contributes for each allele, based on the per-read haplotype likelihoods that were produced by the PairHMM.

This may sound complicated, but the procedure is actually very simple -- there is no real calculation involved, just cherry-picking appropriate values from the table of per-read likelihoods of haplotypes into a new table that will contain per-read likelihoods of alleles. This is how it happens. For a given site, we list all the alleles observed in the data (including the reference allele). Then, for each read, we look at the haplotypes that support each allele; we select the haplotype that has the highest likelihood for that read, and we write that likelihood in the new table. And that's it! For a given allele, the total likelihood will be the product of all the per-read likelihoods. **(example fig TBD)**

At the end of this step, sites where there is sufficient evidence for at least one of the variant alleles considered will be called variant, and a genotype will be assigned to the sample in the next (final) step.

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Comments (7)

I'm analyzing seven trio exomes right now with the latest GATK (version 2.7-4-g6f46d11), and was surprised to find a large number of mendelian violations reported by PhaseByTransmission, even after eliminating low/no coverage events. Tracking down the problem, it seems that CombineVariants occasionally propagates the PL field to the new vcf file incorrectly, sometimes in a way which causes GT not to correspond to the lowest PL.

Here's an example, showing just the GT, AD, and PL columns for a few positions in one trio. For each position, the first line contains the genotypes from the original vcf file, and the second shows the genotypes from the merged file.

#CHROM POS ID REF ALT 100403001-1 100403001-1A 100403001-1B 1 5933530 rs905469 A G 0/0:37,0:0,99,1192 0/0:35,0:0,90,1101 0/0:44,0:0,117,1412 1 5933530 rs905469 A G 0/0:37,0:189,15,1192 0/0:35,0:0,90,1101 0/0:44,0:0,117,1412 1 10412636 rs4846215 A T 0/0:119,0:0,358,4297 0/0:113,0:0,337,4060 0/0:102,0:0,304,3622 1 10412636 rs4846215 A T 0/0:119,0:110,9,0 0/0:113,0:0,337,4060 0/0:102,0:0,304,3622 1 11729035 rs79974326 G C 0/0:50,0:0,141,1709 0/0:53,0:0,150,1788 0/0:71,0:0,187,2246 1 11729035 rs79974326 G C 0/0:50,0:1930,0,3851 0/0:53,0:0,150,1788 0/0:71,0:0,187,2246 1 16735764 rs182873855 G A 0/0:54,0:0,138,1691 0/0:57,0:0,153,1841 0/0:47,0:0,120,1441 1 16735764 rs182873855 G A 0/0:54,0:174,0,1691 0/0:57,0:0,153,1841 0/0:47,0:0,120,1441 1 17316577 rs77880760 G T 0/0:42,0:0,123,1470 0/0:38,0:0,111,1317 0/0:53,0:0,153,1817 1 17316577 rs77880760 G T 0/0:42,0:233,17,1470 0/0:38,0:0,111,1317 0/0:225,25:0,153,181 1 28116000 rs2294229 A G 0/0:37,0:0,105,1291 0/0:37,0:0,111,1379 0/0:30,0:0,87,1066 1 28116000 rs2294229 A G 0/0:37,0:0,105,1291 0/0:37,0:0,111,1379 0/0:30,0:1844,159,0 1 31740706 rs3753373 A G 0/0:123,0:0,349,4173 0/0:110,0:0,319,3793 0/0:111,0:0,328,3885 1 31740706 rs3753373 A G 0/0:123,0:117,6,0 0/0:110,0:0,319,3793 0/0:111,0:0,328,3885

Most genotypes are propagated correctly, and in fact, which a propagated incorrectly changes from run to run.

In my case, I'm merging files from disjoint regions, so I can work around the problem, but it would be nice if this were fixed.

Thanks, Kevin

Comments (4)

Greetings,

I am trying to incorporate genotype likelihoods into a downstream analysis. I have two questions:

1) Why is the most likely genotype scaled to a Phred score of zero?

2) Is there a way to undo the scaling? I have seen downstream tools undo the scaling, but I don't know how they do it. Is there an equation that will return an estimated genotype likelihood from the scaled genotype likelihoods?

Thank you for your time.

Zev Kronenberg

Comments (1)

Dear GATK team and community members,

I used ProduceBeagleInput to create a genotype likelihoods file, and ran beagle.jar according to the example in http://gatkforums.broadinstitute.org/discussion/43/interface-with-beagle-software. Beagle gave a warning that it is better to use a reference panel for imputing genotypes and phasing. So I downloaded the recommended reference panel (http://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes.phase1_release_v3/), but Beagle requires that the alleles be in the same order on both reference and sample files. The tool to do this is check_strands.py (http://faculty.washington.edu/sguy/beagle/strand_switching/README), but it requires both sample and reference files be in .bgl format. This is a little disappointing since not being able to use the reference panel means Beagle's calculations won't be as accurate, although I'm not sure by how much.

I understand that this might be out of the scope of responsibility for the GATK team, but I will greatly appreciate if someone can provide suggestions to allow GATK's input to Beagle be phased using a reference panel. Or hopefully, the GATK team will write a tool to produce .bgl files?

Regards, Jamie

Comments (5)

The printed values missed the PL value, for examples, the format is:

```
GT:AD:DP:GQ:PL
['0/2', '1,0,10', '11', '8.12']
['0/2', '211,39,0', '250', '99']
['0/1', '10,1', '11', '14.38']
['0/1', '4,2', '4', '24.38']
['0/0', '27,0', '27', '78.26']
['1/1', '164,2', '183', '99']
['0/1', '242,1', '249', '99']
['0/1', '225,0', '233', '99']
['0/0', '84,5', '82', '81.18']
```

For every case, the PL value is missing. It happens most often when there are more than one alternative alleles.

Thank you,

Jim