2d:null 2e:["$","title",null,{"children":"Lynch Syndrome Population Prevalence | Tristan Snowsill | Observable"}] 33:T45a,md`# Lynch Syndrome Population Prevalence The population prevalence of Lynch syndrome (the probability that a randomly sampled person from the population would have a pathogenic MMR variant associated with Lynch syndrome) can be estimated using the following equation: ${tex`\Pr(genotype) = \frac{\Pr(genotype \mid cancer) \Pr(cancer)}{\Pr(cancer \mid genotype)}`} Where *cancer* can be any associated site (e.g., colorectal, endometrial). We can estimate ${tex`\Pr(genotype \mid cancer)`} from reflex testing for Lynch syndrome in unselected cancer patients and estimate ${tex`\Pr(cancer)`} from national statistics. We will most likely either use lifetime cancer estimates or cumulative risk estimates to a certain age. In this page we have prepopulated with data for cumulative risk to age 70 for colorectal cancer. ${tex`\Pr(cancer \mid genotype)`} is the penetrance of the genotype, estimated from prospective data (e.g., PLSD) and/or retrospective data with correction for ascertainment bias. Note that in this page we use probabilities (from 0 to 1) for risks instead of percentages (from 0 to 100). `34:T487,viewof userInput = form(html`

Input

Risk of cancer in whole population (0-1)

Genotype	Prevalence in cancer pts.	Penetrance
path_MLH1
path_MSH2
path_MSH6
path_PMS2

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Genotype	Prevalence in cancer pts.	Population prevalence
path_MLH1	${niceOutput(userInput.prevMLH1)}	${niceOutput(results.prevMLH1)}
path_MSH2	${niceOutput(userInput.prevMSH2)}	${niceOutput(results.prevMSH2)}
path_MSH6	${niceOutput(userInput.prevMSH6)}	${niceOutput(results.prevMSH6)}
path_PMS2	${niceOutput(userInput.prevPMS2)}	${niceOutput(results.prevPMS2)}
Total	${niceOutput(userInput.prevMLH1 + userInput.prevMSH2 + userInput.prevMSH6 + userInput.prevPMS2)}	${niceOutput(results.prevMLH1 + results.prevMSH2 + results.prevMSH6 + results.prevPMS2)}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"md`### Other stuff`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"results = ({\n prevMLH1: userInput.populationRisk * userInput.prevMLH1 / userInput.penetranceMLH1,\n prevMSH2: userInput.populationRisk * userInput.prevMSH2 / userInput.penetranceMSH2,\n prevMSH6: userInput.populationRisk * userInput.prevMSH6 / userInput.penetranceMSH6,\n prevPMS2: userInput.populationRisk * userInput.prevPMS2 / userInput.penetrancePMS2\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"niceOutput = function(p) {\n let p_str = Intl.NumberFormat('en-gb', {maximumSignificantDigits: 3}).format(p);\n let inv_p_str = Intl.NumberFormat('en-gb', {maximumSignificantDigits: 4, maximumFractionDigits: 1}).format(1/p);\n return `${p_str} (1 in ${inv_p_str})`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"import {form} from \"@mbostock/form-input\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 32:"$31:body"