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Abstract Background Estimating relatedness is an important step for many genetic study designs. A variety of methods for estimating coefficients of pairwise relatedness from genotype data have been proposed. Both the kinship coefficient $$\varphi$$ φ and the fraternity coefficient $$\psi$$ ψ for all pairs of individuals are of interest. However, when dealing with low-depth sequencing or imputation data, individual level genotypes cannot be confidently called. To ignore such uncertainty is known to result in biased estimates. Accordingly, methods have recently been developed to estimate kinship from uncertain genotypes. Results We present new method-of-moment estimators of both the coefficients $$\varphi$$ φ and $$\psi$$ ψ calculated directly from genotype likelihoods. We have simulated low-depth genetic data for a sample of individuals with extensive relatedness by using the complex pedigree of the known genetic isolates of Cilento in South Italy. Through this simulation, we explore the behaviour of our estimators, demonstrate their properties, and show advantages over alternative methods. A demonstration of our method is given for a sample of 150 French individuals with down-sampled sequencing data. Conclusions We find that our method can provide accurate relatedness estimates whilst holding advantages over existing methods in terms of robustness, independence from external software, and required computation time. The method presented in this paper is referred to as LowKi (Low-depth Kinship) and has been made available in an R package ( https://github.com/genostats/LowKi ).

Noninvasive prenatal test (NIPT) reduces the associated risk of procedure-related miscarriage. However, due to accuracy, special fetuses, economic and policy gaps, NIPT still cannot replace traditional surgical methods. Developing a pipeline with low cost, low technical difficulty, stability and high accuracy is a major challenge for NIPT to be widely used. This study proposes a new pipeline for the detection of fetal trisomy which includes 3 steps: 1. 40 bp single-end sequencing, 2. P chrN calculations, and 3. logistic regression (LR) models. Part of the public dataset (100 out of 144 samples) was used to train models and select features in the machine learning pipeline. 314 samples from different sources were used for independent testing. We compare the performance of our method with the bioinformatics method widely used today. Our model shows high robustness to data from different sources. The final best model achieved an AUC of 99.85 % in predicting T21 using chr21 features which are the DNA fragment concentrations. The AUC is 99.50 %, and 97.70 % in predicting T18 and T13 with all features from 24 chromosomes. The PPV of T21, T18 and T13 was predicted to be 91.67 %, 93.33 % and 83.33 %, respectively, which was higher than that obtained by standard bioinformatics methods. The NPV to identify T21, T18, and T13 were 100 %, 99.33 %, and 98.70 %, respectively. Our approach does not need to calculate fetal fraction (FF) and can handle samples from a wide range of gestational ages (GA), twin pregnancies and fetal mosaicism. Our approach can achieve comparable accuracy with the current standard bioinformatics analysis in low-depth sequencing data. This convenient pipeline can be used independently of traditional bioinformatics methods, and its performance has been tested in real clinical practice. Our pipeline can be an important aid for the detection of fetal trisomy in clinical NIPT, which will help further popularize NIPT. [ABSTRACT FROM AUTHOR]

Background: Estimating relatedness is an important step for many genetic study designs. A variety of methods for estimating coefficients of pairwise relatedness from genotype data have been proposed. Both the kinship coefficient φ and the fraternity coefficient ψ for all pairs of individuals are of interest. However, when dealing with low-depth sequencing or imputation data, individual level genotypes cannot be confidently called. To ignore such uncertainty is known to result in biased estimates. Accordingly, methods have recently been developed to estimate kinship from uncertain genotypes. Results: We present new method-of-moment estimators of both the coefficients φ and ψ calculated directly from genotype likelihoods. We have simulated low-depth genetic data for a sample of individuals with extensive relatedness by using the complex pedigree of the known genetic isolates of Cilento in South Italy. Through this simulation, we explore the behaviour of our estimators, demonstrate their properties, and show advantages over alternative methods. A demonstration of our method is given for a sample of 150 French individuals with down-sampled sequencing data. Conclusions: We find that our method can provide accurate relatedness estimates whilst holding advantages over existing methods in terms of robustness, independence from external software, and required computation time. The method presented in this paper is referred to as LowKi (Low-depth Kinship) and has been made available in an R package (https://github.com/genostats/LowKi). [ABSTRACT FROM AUTHOR]

Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non‐model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low‐depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low‐depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species. [ABSTRACT FROM AUTHOR]