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1. Privacy-aware estimation of relatedness in admixed populations

   https://doi.org/10.1093/bib/bbac473  

   Wang, Su ; Kim, Miran ; Li, Wentao ; Jiang, Xiaoqian ; Chen, Han ; Harmanci, Arif ( November 2022 , Briefings in Bioinformatics)

   Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.

   Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.

   Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.

   Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.

    

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2. Comparing genome‐based estimates of relatedness for use in pedigree‐based conservation management

   https://doi.org/10.1111/1755-0998.13630  

   Hauser, Samantha S. ; Galla, Stephanie J. ; Putnam, Andrea S. ; Steeves, Tammy E. ; Latch, Emily K. ( May 2022 , Molecular Ecology Resources)

   Researchers have long debated which estimator of relatedness best captures the degree of relationship between two individuals. In the genomics era, this debate continues, with relatedness estimates being sensitive to the methods used to generate markers, marker quality, and levels of diversity in sampled individuals. Here, we compare six commonly used genome‐based relatedness estimators (kinship genetic distance [KGD], Wang maximum likelihood [TrioML], Queller and Goodnight [R_xy], Kinship INference for Genome‐wide association studies [KING‐robust), and pairwise relatedness [R_AB], allele‐sharing coancestry [AS]) across five species bred in captivity–including three birds and two mammals–with varying degrees of reliable pedigree data, using reduced‐representation and whole genome resequencing data. Genome‐based relatedness estimates varied widely across estimators, sequencing methods, and species, yet the most consistent results for known first order relationships were found usingR_xy,R_AB, and AS. However, AS was found to be less consistently correlated with known pedigree relatedness than eitherR_xyorR_AB. Our combined results indicate there is not a single genome‐based estimator that is ideal across different species and data types. To determine the most appropriate genome‐based relatedness estimator for each new data set, we recommend assessing the relative: (1) correlation of candidate estimators with known relationships in the pedigree and (2) precision of candidate estimators with known first‐order relationships. These recommendations are broadly applicable to conservation breeding programmes, particularly where genome‐based estimates of relatedness can complement and complete poorly pedigreed populations. Given a growing interest in the application of wild pedigrees, our results are also applicable to in situ wildlife management.

    

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3. Double‐digest RAD sequencing outperforms microsatellite loci at assigning paternity and estimating relatedness: A proof of concept in a highly promiscuous bird

   https://doi.org/10.1111/1755-0998.12771  

   Thrasher, Derrick J. ; Butcher, Bronwyn G. ; Campagna, Leonardo ; Webster, Michael S. ; Lovette, Irby J. ( March 2018 , Molecular Ecology Resources)

   Information on genetic relationships among individuals is essential to many studies of the behaviour and ecology of wild organisms. Parentage and relatedness assays based on large numbers of single nucleotide polymorphism (SNP) loci hold substantial advantages over the microsatellite markers traditionally used for these purposes. We present a double‐digest restriction site‐associated DNA sequencing (ddRAD‐seq) analysis pipeline that, as such, simultaneously achieves the SNP discovery and genotyping steps and which is optimized to return a statistically powerful set of SNP markers (typically 150–600 after stringent filtering) from large numbers of individuals (up to 240 per run). We explore the trade‐offs inherent in this approach through a set of experiments in a species with a complex social system, the variegated fairy‐wren (Malurus lamberti) and further validate it in a phylogenetically broad set of other bird species. Through direct comparisons with a parallel data set from a robust panel of highly variable microsatellite markers, we show that this ddRAD‐seq approach results in substantially improved power to discriminate among potential relatives and considerably more precise estimates of relatedness coefficients. The pipeline is designed to be universally applicable to all bird species (and with minor modifications to many other taxa), to be cost‐ and time‐efficient, and to be replicable across independent runs such that genotype data from different study periods can be combined and analysed as field samples are accumulated.

    

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4. Model‐based genotype and ancestry estimation for potential hybrids with mixed‐ploidy

   https://doi.org/10.1111/1755-0998.13330  

   Shastry, Vivaswat ; Adams, Paula E. ; Lindtke, Dorothea ; Mandeville, Elizabeth G. ; Parchman, Thomas L. ; Gompert, Zachariah ; Buerkle, C. Alex ( February 2021 , Molecular Ecology Resources)

   Non‐random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed‐ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, calledentropy, which can use low‐depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed‐ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade‐off between sequencing depth and genome coverage and found lower error associated with low‐depth sequencing across a larger fraction of the genome than with high‐depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploidArabidopsis arenosa.

    

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5. Best practices for genotype imputation from low‐coverage sequencing data in natural populations

   https://doi.org/10.1111/1755-0998.13854  

   Watowich, Marina M. ; Chiou, Kenneth L. ; Graves, Brian ; Montague, Michael J. ; Brent, Lauren J. N. ; Higham, James P. ; Horvath, Julie E. ; Lu, Amy ; Martinez, Melween I. ; Platt, Michael L. ; et al ( August 2023 , Molecular Ecology Resources)

   Monitoring genetic diversity in wild populations is a central goal of ecological and evolutionary genetics and is critical for conservation biology. However, genetic studies of nonmodel organisms generally lack access to species‐specific genotyping methods (e.g. array‐based genotyping) and must instead use sequencing‐based approaches. Although costs are decreasing, high‐coverage whole‐genome sequencing (WGS), which produces the highest confidence genotypes, remains expensive. More economical reduced representation sequencing approaches fail to capture much of the genome, which can hinder downstream inference. Low‐coverage WGS combined with imputation using a high‐confidence reference panel is a cost‐effective alternative, but the accuracy of genotyping using low‐coverage WGS and imputation in nonmodel populations is still largely uncharacterized. Here, we empirically tested the accuracy of low‐coverage sequencing (0.1–10×) and imputation in two natural populations, one with a large (n = 741) reference panel, rhesus macaques (Macaca mulatta), and one with a smaller (n = 68) reference panel, gelada monkeys (Theropithecus gelada). Using samples sequenced to coverage as low as 0.5×, we could impute genotypes at >95% of the sites in the reference panel with high accuracy (medianr² ≥ 0.92). We show that low‐coverage imputed genotypes can reliably calculate genetic relatedness and population structure. Based on these data, we also provide best practices and recommendations for researchers who wish to deploy this approach in other populations, with all code available on GitHub (https://github.com/mwatowich/LoCSI‐for‐non‐model‐species). Our results endorse accurate and effective genotype imputation from low‐coverage sequencing, enabling the cost‐effective generation of population‐scale genetic datasets necessary for tackling many pressing challenges of wildlife conservation.

    

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