Abstract
Analysis of RNA sequencing (RNA-seq) data from related individuals is widely used in clinical and molecular genetics studies. Prediction of kinship from RNA-seq data would be useful for confirming the expected relationships in family based studies and for highlighting samples from related individuals in case-control or population based studies. Currently, reconstruction of pedigrees is largely based on SNPs or microsatellites, obtained from genotyping arrays, whole genome sequencing and whole exome sequencing. Potential problems with using RNA-seq data for kinship detection are the low proportion of the genome that it covers, the highly skewed coverage of exons of different genes depending on expression level and allele-specific expression. In this study we assess the use of RNA-seq data to detect kinship between individuals, through pairwise identity by descent (IBD) estimates. First, we obtained high quality SNPs after successive filters to minimize the effects due to allelic imbalance as well as errors in sequencing, mapping and genotyping. Then, we used these SNPs to calculate pairwise IBD estimates. By analysing both real and simulated RNA-seq data we show that it is possible to identify up to second degree relationships using RNA-seq data of even low to moderate sequencing depth.
Highlights
RNA sequencing is used in genetics for a variety of purposes such as to test the heritability of gene expression traits [1], to search for mutations causing mendelian disorders [2], to understand the effects of disease-associated mutations (e.g. [3]), to study the mechanisms of epigenetic inheritance of phenotypic traits [4], among others
Analysis workflow for kinship detection using RNA sequencing (RNA-seq) from related individuals
We have shown here that kinship detection based on estimates of identity by descent probabilities using RNAseq data is possible allowing the detection of up to second degree relationships
Summary
RNA sequencing is used in genetics for a variety of purposes such as to test the heritability of gene expression traits [1], to search for mutations causing mendelian disorders [2], to understand the effects of disease-associated mutations (e.g. [3]), to study the mechanisms of epigenetic inheritance of phenotypic traits [4], among others. [3]), to study the mechanisms of epigenetic inheritance of phenotypic traits [4], among others. In these studies, multiple RNA samples are extracted from different related or unrelated individuals and they are processed in parallel. In family studies, matching samples to individuals in a pedigree allows the correct association of phenotypic traits to expression patterns. In population studies, biased recruitment schemes can enrich datasets with cryptic relationships [5]. This is a common scenario in association analyses and it could happen for RNA-seq collections from unrelated individuals. Researchers check the correctness of RNAseq sample relatedness indirectly because it is so far unclear whether kinship can be detected directly from RNAseq data
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