Graphics for relatedness research

Galván-Femenía, Iván; Graffelman, Jan; Barcelo-i-Vidal, Carles

doi:10.1111/1755-0998.12674

Cited by 7 publications

(6 citation statements)

References 39 publications

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“…The classification rate for 6th degree pairs can still be improved if more than 35,000 independent MAF 0.50 variants would be used (see Figure 4). The ( p 0 , p 2 ), ( m, s ), and (truek^0, truek^1) scatterplots display estimates in a constrained space (Galván-Femenía et al, 2017), where Euclidean distances between points cannot be safely interpreted. This is particularly true for the higher degree relationships that merge toward the vertex of the triangular region inside the scatterplot.…”

Section: Discussionmentioning

confidence: 99%

“…Related pairs can also be distinguished, albeit at lower resolution, by using the co-ancestry coefficient defined as θ = k 1 /2+ k 2 or the kinship coefficient defined as ϕ = θ/2. Galván-Femenía et al (2017) give an overview of graphics used in relatedness research. Figure 1 shows a panel plot of some standard graphics used in IBS and IBD studies for all the pairs of individuals from the CEU population of the 1.000 Genomes project.…”

Section: Methodsmentioning

confidence: 99%

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A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

et al. 2019

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The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degree.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

et al. 2019

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“…Specifically, we used the measure PI_HAT, which estimates the overall proportion of the genome that is identical by descent (IBD) between pairs of individuals, as well as Z scores, which reflect the probability of sharing zero, one or two alleles IBD. The latter probabilities depend directly on relatedness and therefore provide a more precise method for inferring the type of relationship between individuals (Galván-Femenía et al . 2017).…”

Section: Methodsmentioning

confidence: 99%

A 90K SNP array uncovers inbreeding and cryptic relatedness in an Antarctic fur seal breeding colony

Humble

Paijmans

Forcada

et al. 2020

Preprint

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High density single nucleotide polymorphism (SNP) arrays allow large numbers of individuals to be rapidly and cost-effectively genotyped at large numbers of genetic markers. However, despite being widely used in studies of humans and domesticated plants and animals, SNP arrays are lacking for most wild organisms. We developed a custom 90K Affymetrix Axiom array for an intensively studied pinniped, the Antarctic fur seal (Arctocephalus gazella). SNPs were discovered from a combination of genomic and transcriptomic resources and filtered according to strict criteria. Out of a total of 85,359 SNPs tiled on the array, 75,601 (88.6%) successfully converted and were polymorphic in 274 animals from a breeding colony at Bird Island in South Georgia. Evidence was found for inbreeding, with three genomic inbreeding coefficients being strongly intercorrelated and the proportion of the genome in ROH being non-zero in all individuals. Furthermore, analysis of genomic relatedness coefficients identified multiple second and third order relatives among a sample of ostensibly unrelated individuals. Such “cryptic relatedness” within fur seal breeding colonies may increase the likelihood of consanguinous matings and could therefore have implications for understanding fitness variation and mate choice. Finally, we demonstrate the cross-amplification potential of the array in three related species. Overall, our SNP array will facilitate future studies of Antarctic fur seals and has the potential to serve as a more general resource for the wider pinniped research community.

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“…Representation of pairwise IBD estimates was done in R v3.5.0 (39) with the method described by Galván-Femenía et al. (40). Using the IBD estimates obtained in the previous step, we used PRIMUS v1.9.0 (41) to predict pairwise relationships and to reconstruct the whole pedigree, considering as unrelated those pairs of individuals with a coefficient of relatedness lower than 0.2 (option -t 0.2).…”

Section: Methodsmentioning

confidence: 99%

Assessment of kinship detection using RNA-seq data

Blay

Casas

Galván-Femenía

et al. 2019

Nucleic Acids Research

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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.

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Graphics for relatedness research

Cited by 7 publications

References 39 publications

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

A 90K SNP array uncovers inbreeding and cryptic relatedness in an Antarctic fur seal breeding colony

Assessment of kinship detection using RNA-seq data

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