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RetroFunRVS is a retrospective family-based burden test incorporating functional annotations. One critical feature of the method is to consider only affected members among families. See Mangnier & Bureau, 2022 for theoretical justification, statistical derivations, and power simulation results.
RetroFunRVS works directly with .ped files. Basically, the six first columns of a .ped file are composed by pedigree information (family id, individual id, father and mother id, sex and status), followed by 2 x number of variants, where 1 corresponds to the reference allele while 2 is the alternative. In addition, RetroFunRVS needs sharing probabilities to compute score statistic and its variance, which can be obtained with RVS package (Sherman et al., 2019). An example .ped file can be found on Github and the corresponding sharing probabilities for each pedigree as well.
The first step is to import data and proceed to some cleaning. In this version, we take unique combinations of variants across individuals, while homozyguous variants are converted into heterozyguous. However, other options are available for the correction parameter. If, cryptic relatedness or inbreeding are expected across families, correction = none, while correction=remove will drop all the variants with allele counts = 2. You can import your .ped file using agg.genos.by.fam function, only specifying the path.
The returned object is a list with the aggregated genotypes in each family and the corresponding index variants. These latters will be used to weight variants and incorporate functional annotations in the further steps.
library(RetroFunRVS)
set.seed(1234)
pedfile.clean = agg.genos.by.fam("sample_ped.ped")
pedfile.clean
Since the data have been imported, we need to compute the score test statistic. To do so, we firstly need to have the genotype null value for each family. The function only takes a pedigree object, while carrier configurations and the corresponding probabilities will be computed internally (See Sherman et al., 2019 for technical details), and from now we are able to have the null value using compute.null function. Consistent with the previous function, cryptic relatedness or consanguinity can be assumed using the distinguishHomo=TRUE and cryptic.relatedness=TRUE parameters. However, careful attention has to be provided since for large pedigrees the function can be time consuming. The function provides internal validations for checking the presence of consanguinity.
The object returned by the function is a data.frame with the family id, the expected value under the null, the corresponding variance and covariance.
load("ped2021.RData")
null = compute.null(ped2021)
head(null)
Since you have pre-processed your .ped file and obtained the expected null value, you can obtain your p-values using the core RetroFun.RVS function. Basically, the function returns individual p-values (e.g., for each functional annotation) and the ACAT-combined p-values (Liu et al., 2019) for a set of p-values within an arbitrary region. Fisher's method-combined p-values are also provided but have to used if independent annotations are expected.
In a similar vein as our paper, we randomly generated two functional annotations, corresponding to regions with functional impacts (e.g., Cis-Regulatory Hubs). It is worthy noting that correlation structure between variants can be specified using the independence parameter.
Similar to He et al. 2017, we integrated the original burden test, ensuring the robustness of the method. In doing so, RetroFun-RVS higlights a minimal loss of power when no functional annotation is predictive with the trait.
#Fist annotation with equal weights
first.annot = sample(c(0,1),510, replace = T)
#Second annotation with unequal weights
second.annot = sample(c(0,1), 510, replace = T, prob=c(0.8, 0.2))
#Annotation matrix, the first column should be composed only with ones
Z = matrix(c(rep(1,510),first.annot, second.annot),ncol=3,nrow=510)
#Equal weights, assuming correlation between variants
RetroFun.RVS(null, pedfile.clean, Z, diag(1, nrow=510,ncol=510), independence = F)
#Here we can assume weights depending on MAF
beta.weights = diag(dbeta(runif(510, 0.00001, 0.01), 1,25), ncol=510, nrow=510)
RetroFun.RVS(null, pedfile.clean, Z, beta.weights, independence = F)
#No annotation is predictive with the trait
third.annot = sample(c(0,1),510, replace = T, prob = c(0.9,0.1))
fourth.annot = sample(c(0,1),510, replace = T, prob = c(0.95,0.05))
Z.nonpred = matrix(c(rep(1,510),third.annot, fourth.annot),ncol=3,nrow=510)
RetroFun.RVS(null, pedfile.clean, Z.nonpred, diag(1, nrow=510,ncol=510), independence = F)
Under certain circumstances (In the presence of small families, families with a few affected or functional annotations encompassing few variants), asymptotic derivation may lead to type-I error rate inflation. To address this limitation, we provided a non-parametric bootstrap procedure to compute empirical p-values. The function bootRetroFunRVS generates bootstrap samples for a given set of variants and families. A particular attention has to be provided to the order of the two objects containing genotype configurations and sharing probabilities.
load("SPAPsimpleprob.RData")
n.unique.config.by.fam = sapply(forsim.N.list, unique)
prob.sharing.by.famid = lapply(1:length(forsim.pattern.prob.list), function(x) tapply(forsim.pattern.prob.list[[x]], forsim.N.list[[x]], sum))
names(prob.sharing.by.famid) = names(forsim.pattern.prob.list)
#First as suggested in the bootRetroFunRVS documentation n.unique.config.by.fam and prob.sharing.by.fam have to be in the same order
n.unique.config.by.fam = lapply(n.unique.config.by.fam, function(fam) sort(fam))
Finally, p-values can be obtained comparing bootstrap statistics with observed statistic for each functional annotation. Here we provide a example on one replicate.
#Bootstrap burden statistic
resample.genos = bootRetroFunRVS(agg.genos.by.fam = pedfile.clean ,n.unique.config.by.fam = n.unique.config.by.fam,Z_annot = Z,W = rep(1,510),prob.sharing.by.fam = prob.sharing.by.famid, nullval = null ,nrep = 1)
#Observed burden statistic
compute.Burden.by.Annot(null,pedfile.clean, Z, diag(1, nrow=510,ncol=510))$B
For any questions regarding the package or the method, please send an email to loic.mangnier@gmail.com.
RetroFun-RVS: a retrospective family-based framework for rare variant analysis incorporating functional annotations, Loic Mangnier, Alexandre Bureau, bioRxiv 2022.06.21.497085; doi: https://doi.org/10.1101/2022.06.21.497085
Zihuai He, Bin Xu, Seunggeun Lee, Iuliana Ionita-Laza, Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotations and Meta-analysis of Noncoding Variation in Metabochip Data, The American Journal of Human Genetics, Volume 101, Issue 3, 2017, Pages 340-352, ISSN 0002-9297, https://doi.org/10.1016/j.ajhg.2017.07.011.
Liu Y, Chen S, Li Z, Morrison AC, Boerwinkle E, Lin X. ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies. Am J Hum Genet. 2019 Mar 7;104(3):410-421. doi: 10.1016/j.ajhg.2019.01.002. PMID: 30849328; PMCID: PMC6407498.
Thomas Sherman, Jack Fu, Robert B Scharpf, Alexandre Bureau, Ingo Ruczinski, Detection of rare disease variants in extended pedigrees using RVS, Bioinformatics, Volume 35, Issue 14, July 2019, Pages 2509–2511, https://doi.org/10.1093/bioinformatics/bty976