.. _rstphenotype-score: LIRICAL's Phenotype Score ========================= LIRICAL calculates a likelihood ratio score for phenotypic observations for each differential diagnosis. The phenotype likelihood ratio score can be combined with LIRICAL's genotype likelihood ratio score for a combined analysis of phenotypes and genetic data (such as exome or genome sequencing) or can be used as a tool to assess phenotype data alone. This page explains how to interpret LIRICAL's phenotype score. Each disease shows a detailed explanation of the matching score. For instance, the match with `Ectodermal Dysplasia 9, Hair/nail Type `_ shown on :ref:`rstlirical-html` shows the following: :: E:Specific learning disability[HP:0001328][152.894] E:Obesity[HP:0001513][62.561] E:Rod-cone dystrophy[HP:0000510][45.396] Q`_) then the disease is implicitly annotated to the parent term *Cataract* (`HP:0000518 `_) (to see this consider that any person with a polar cataract can also be said to have a cataract). In this case, the probability of :math:`h_i` in disease :math:`\mathcal{D}` is equal to the maximum frequency of any of the ancestors of :math:`h_i` in $\mathcal{D}$. .. list-table:: Query term is parent of a disease term term (D`_), and the query term :math:`h_i` is *Orthostatic syncope* (`HP:0012670 `_), which is a child term of *Syncope*. In addition, *Syncope* has two other child terms, *Carotid sinus syncope* (`HP:0012669 `_) and *Vasovagal syncope* (`HP:0012668 `_). According to our model, we will adjust the frequency of *Syncope* in disease :math:`\mathcal{D}` (say, 0.72) by dividing it by the total number of child terms of :math:`h_j` (so in our example, we would use the frequency :math:`0.72\times 1/3=0.24`). .. list-table:: Query term is child of disease term (Q`_) is a subclass of *Retinal degeneration* (`HP:0000546 `_). 4. :math:`h_i` and some term to which :math:`\mathcal{D}` is annotated have a non-root common ancestor. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This option pertains if options (ii) and (iii) do not, i.e., :math:`h_i` is not a child term of any disease term :math:`h_j` and no disease term :math:`h_j` is a child of :math:`h_i` . If this is the case, then we find the closest common-ancestor, and determine the likelihood ratio according to the formula :math:`\rm{LR}(h_i) = \frac{P(h_i|\mathcal{D})}{P(h_i|\neg \mathcal{D})}`. Because the common ancestor is higher up in the HPO hierarchy, the likelihood ratio tends to be lower and sometimes substantially lower. In order to limit the amount of negative influence of any one query term, the likelihood ratio is defined to be at least 1/100. .. list-table:: Non-root distant match (Q~D) :widths: 100 :header-rows: 1 * - Example * - Q~D:Macular degeneration[HP:0000608]~Abnormal retinal morphology[HP:0000479][0.127] In this example, *Macular degeneration* (`HP:0000608 `_) is not a direct child of *Abnormal retinal morphology* (`HP:0000479 `_) -- it is a "grandchild", i.e., *Macular degeneration* is a direct child of &Abnormal macular morphology (`HP:0001103 `_) which in turn is a direct child of *Abnormal retinal morphology*. Therefore, it is considered to be a non-root distant match. It is assigned a likelihood ratio of 0.127. 5. :math:`h_i` does not have any non-root common ancestor with any term to which :math:`\mathcal{D}` is annotated. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In this case, a heuristic value of 1/100 is assigned for the likelihood ratio. .. list-table:: No match (NM) :widths: 100 :header-rows: 1 * - Example * - NM:Specific learning disability[HP:0001328][0.010] 6. phenotypic abnormality :math:`h_i` is explicitly excluded from disease :math:`\mathcal{D}`. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In the HPO annotation resource, each disease is represented by a list of HPO terms that characterize it together with metadata including provenance, and in some cases, frequency and onset information. Some diseases additionally have explicitly excluded terms (there are a total of 921 such annotations in the September 2019 release of the HPOA data). These annotations are used for phenotypic abnormalities that are important for the differential diagnosis. For instance, Marfan syndrome and Loeys-Dietz syndrome share many phenotypic abnormalities. The feature *Ectopia lentis* (`HP:0001083 `_) is characteristic of Marfan syndrome but is not found in Loeys-Dietz syndrome. The likelihood ratio for such query terms is assigned an arbitrary value of :math:`\frac{1}{1000}`, i.e., the ratio for a candidate diagnosis is reduced by a factor of one thousand if an HPO term is present in the proband that is explicitly excluded from the disease. .. list-table:: Excluded in query and present in disease (XP) :widths: 100 :header-rows: 1 * - Example * - XP:Ectopia lentis[HP:0001083][0.001] If a term is excluded in the query, but not annotated one way of another in the disease, then the likelihood ratio is calculated without additional heuristics. These query terms generally result in a likelihood ratio near 1 and do not affect the differential diagnostic ranking much. .. list-table:: Excluded in query and not annotated in disease (XA) :widths: 100 :header-rows: 1 * - Example * - XA:Abnormality of alkaline phosphatase activity[HP:0004379][1.008] On the other hand, if the query includes a negated term that is explicitly excluded in the disease, then the opposite value is assigned, i.e., the ratio for a candidate diagnosis is increased by a factor of one thousand if an HPO term is present in the proband that is explicitly excluded from the disease. .. list-table:: Excluded in both query and disease (XX) :widths: 100 :header-rows: 1 * - Example * - XX:Trident hand[HP:0004060][1000.000]