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Maximum likelihood parentage assignment using quantitative genotypes

Abstract

The cost of parentage assignment precludes its application in many selective breeding programmes and molecular ecology studies, and/or limits the circumstances or number of individuals to which it is applied. Pooling samples from more than one individual, and using appropriate genetic markers and algorithms to determine parental contributions to pools, is one means of reducing the cost of parentage assignment. This paper describes and validates a novel maximum likelihood (ML) parentage-assignment method, that can be used to accurately assign parentage to pooled samples of multiple individuals—previously published ML methods are applicable to samples of single individuals only—using low-density single nucleotide polymorphism (SNP) ‘quantitative’ (also referred to as ‘continuous’) genotype data. It is demonstrated with simulated data that, when applied to pools, this ‘quantitative maximum likelihood’ method assigns parentage with greater accuracy than established maximum likelihood parentage-assignment approaches, which rely on accurate discrete genotype calls; exclusion methods; and estimating parental contributions to pools by solving the weighted least squares problem. Quantitative maximum likelihood can be applied to pools generated using either a ‘pooling-for-individual-parentage-assignment’ approach, whereby each individual in a pool is tagged or traceable and from a known and mutually exclusive set of possible parents; or a ‘pooling-by-phenotype’ approach, whereby individuals of the same, or similar, phenotype/s are pooled. Although computationally intensive when applied to large pools, quantitative maximum likelihood has the potential to substantially reduce the cost of parentage assignment, even if applied to pools comprised of few individuals.

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Fig. 1: Inputs and parameters required to compute quantitative genotypes (bold borders) from intensity-based assays.
Fig. 2: Inputs and parameters required to undertake maximum likelihood parentage assignment using quantitative genotypes.
Fig. 3: Overlapping histograms of Δ LOD values for assignments to 9600 simulated unknown parents of pools.
Fig. 4: Overlapping histograms of Δ LOD values for assignments to 9600 simulated unknown parents of pools.

Data availability

An R package (R Core Team 2020) entitled ‘SNPpools’, available at https://github.com/mghamilton/SNPpools, was developed to implement and validate with simulations the quantitative ML method.

References

  1. Anderson EC, Garza JC (2006) The power of single-nucleotide polymorphisms for large-scale parentage inference. Genetics 172(4):2567–2582

    CAS  Article  Google Scholar 

  2. Barratt BJ, Payne F, Rance HE, Nutland S, Todd JA, Clayton DG (2002) Identification of the sources of error in allele frequency estimations from pooled DNA indicates an optimal experimental design. Ann Hum Genet 66(5-6):393–405

    CAS  Article  Google Scholar 

  3. Bell AM, Henshall JM, Porto-Neto LR, Dominik S, McCulloch R, Kijas J et al. (2017) Estimating the genetic merit of sires by using pooled DNA from progeny of undetermined pedigree. Genet Sel Evol 49:ARTN 28

  4. Burdon R, Shelbourne C (1971) Breeding populations for recurrent selection: Conflicts and possible solutions. N Z J Sci 1:174–193

    Google Scholar 

  5. Burdon RD (1977) Genetic correlation as a concept for studying genotype- environment interaction in forest tree breeding. Silvae Genet 26(5/6):168–175

    Google Scholar 

  6. Chakraborty R, Shaw M, Schull WJ (1974) Exclusion of paternity: the current state of the art. Am J Hum Genet 26(4):477

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Clark LV, Lipka AE, Sacks EJ (2019) polyRAD: genotype calling with uncertainty from sequencing data in polyploids and diploids. G3 9(3):663–673

    CAS  Article  Google Scholar 

  8. Dai P, Kong J, Liu J, Lu X, Sui J, Meng X et al. (2020) Evaluation of the utility of genomic information to improve genetic evaluation of feed efficiency traits of the Pacific white shrimp Litopenaeus vannamei. Aquaculture 527:735421

    CAS  Article  Google Scholar 

  9. de Bem Oliveira I, Resende Jr MFR, Ferrao LFV, Amadeu RR, Endelman JB, Kirst M et al. (2019) Genomic prediction of autotetraploids; influence of relationship matrices, allele dosage, and continuous genotyping calls in phenotype prediction. G3 9(4):1189–1198

    Article  Google Scholar 

  10. Flanagan SP, Jones AG (2019) The future of parentage analysis: From microsatellites to SNPs and beyond. Mol Ecol 28(3):544–567

    Article  Google Scholar 

  11. Grandke F, Singh P, Heuven HC, de Haan JR, Metzler D (2016) Advantages of continuous genotype values over genotype classes for GWAS in higher polyploids: a comparative study in hexaploid chrysanthemum. BMC Genom 17:672

    Article  Google Scholar 

  12. Grattapaglia D, Diener PSD, dos Santos GA (2014) Performance of microsatellites for parentage assignment following mass controlled pollination in a clonal seed orchard of loblolly pine (Pinus taeda L.). Tree Genet Genomes 10(6):1631–1643

    Article  Google Scholar 

  13. Hamilton MG, Kube PD, Elliott NG, McPherson LJ, Krsinich A (2009) Development of a breeding strategy for hybrid abalone. Proc Assoc Adv Anim Breed Genet 18:350–353

  14. Hamilton MG, Mekkawy W, Benzie JAH (2019a) Sibship assignment to the founders of a Bangladeshi Catla catla breeding population. Genet Sel Evol 51(1):17

    Article  Google Scholar 

  15. Hamilton MG, Mekkawy W, Kilian A, Benzie JAH (2019b) Single nucleotide polymorphisms (SNPs) reveal sibship among founders of a Bangladeshi rohu (Labeo rohita) breeding population. Front Genet. 10:597

    CAS  Article  Google Scholar 

  16. Hansen OK, Kjaer ED (2006) Paternity analysis with microsatellites in a Danish Abies nordmanniana clonal seed orchard reveals dysfunctions. Can J Res-Rev Can Rech 36(4):1054–1058

    Article  Google Scholar 

  17. Harrison HB, Saenz-Agudelo P, Planes S, Jones GP, Berumen ML (2013) On minimizing assignment errors and the trade-off between false positives and negatives in parentage analysis. Mol Ecol 22(23):5738–5742

    Article  Google Scholar 

  18. Hauser L, Baird M, Hilborn R, Seeb LW, Seeb JE(2011) An empirical comparison of SNPs and microsatellites for parentage and kinship assignment in a wild sockeye salmon (Oncorhynchus nerka) population Mol Ecol Resour 11(Suppl 1):150–161

    Article  Google Scholar 

  19. Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31(2):423–447

    CAS  Article  Google Scholar 

  20. Henderson CR (1988) Use of an average numerator relationship matrix for multiple-sire joining. J Anim Sci 66(7):1614–1621

    Article  Google Scholar 

  21. Henderson CR, Quaas RL (1976) Multiple trait evaluation using relatives’ records. J Anim Sci 43(6):1188–1197

    Article  Google Scholar 

  22. Henshall JM, Dierens L, Sellars MJ (2014) Quantitative analysis of low-density SNP data for parentage assignment and estimation of family contributions to pooled samples. Genet Sel Evol 46:ARTN 51

    Article  Google Scholar 

  23. Henshall JM, Hawken RJ, Dominik S, Barendse W (2012) Estimating the effect of SNP genotype on quantitative traits from pooled DNA samples. Genet Sel Evol 44:ARTN 12

  24. Holman LE, Onoufriou A, Hillestad B, Johnston IA (2017) A workflow used to design low density SNP panels for parentage assignment and traceability in aquaculture species and its validation in Atlantic salmon. Aquaculture 476:59–64

    CAS  Article  Google Scholar 

  25. Jones AG, Small CM, Paczolt KA, Ratterman NL (2010) A practical guide to methods of parentage analysis. Mol Ecol Resour 10(1):6–30

    Article  Google Scholar 

  26. Kalinowski ST, Taper ML, Marshall TC (2010) Corrigendum: revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment (vol 16, pg 1099 2007). Mol Ecol 19(7):1512–1512

    Article  Google Scholar 

  27. Kinghorn BP, Bastiaansen JWM, Ciobanu DC, van der Steen HAM (2010) Quantitative genotyping to estimate genetic contributions to pooled samples and genetic merit of the contributing entities. Acta Agr Scand a 60(1):3–12

    CAS  Google Scholar 

  28. Liu S, Palti Y, Gao G, Rexroad CE (2016) Development and validation of a SNP panel for parentage assignment in rainbow trout. Aquaculture 452:178–182

    CAS  Article  Google Scholar 

  29. Marshall TC, Slate J, Kruuk LEB, Pemberton JM (1998) Statistical confidence for likelihood-based paternity inference in natural populations. Mol Ecol 7(5):639–655

    CAS  Article  Google Scholar 

  30. Meagher TR, Thompson E (1986) The relationship between single parent and parent pair genetic likelihoods in genealogy reconstruction. Theor Popul Biol 29(1):87–106

    Article  Google Scholar 

  31. R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

  32. Rahman A, Hellicar A, Smith D, Henshall JM (2015) Allele frequency calibration for SNP based genotyping of DNA pools: A regression based local-global error fusion method. Comput Biol Med 61:48–55

    CAS  Article  Google Scholar 

  33. Semagn K, Babu R, Hearne S, Olsen M (2014) Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Mol Breed 33(1):1–14

    CAS  Article  Google Scholar 

  34. Sonesson AK (2005) A combination of walk-back and optimum contribution selection in fish: a simulation study. Genet Sel Evol 37(6):587–599

    Article  Google Scholar 

  35. Spielmann A, Harris SA, Boshier DH, Vinson CC (2015) orchard: paternity program for autotetraploid species. Mol Ecol Resour 15(4):915–920

    CAS  Article  Google Scholar 

  36. Vandeputte M, Haffray P (2014) Parentage assignment with genomic markers: a major advance for understanding and exploiting genetic variation ofquantitative traits in farmed aquatic animals. Front Genet 5:ARTN 432

  37. Wang J, Scribner KT (2014) Parentage and sibship inference from markers in polyploids. Mol Ecol Resour 14(3):541–553

    Article  Google Scholar 

  38. Weinman LR, Solomon JW, Rubenstein DR (2015) A comparison of single nucleotide polymorphism and microsatellite markers for analysis of parentage and kinship in a cooperatively breeding bird. Mol Ecol Resour 15(3):502–511

    CAS  Article  Google Scholar 

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Acknowledgements

This work was supported by the CSIRO Agriculture and Food project ‘Genomics platforms to assist applied aquaculture breeding’ (AgSIP53). John Henshall shared his R scripts relating to quantitative analysis of low-density SNP data for parentage assignment and estimation of family contributions to pooled samples—code from these scripts was not used in the SNPpools package or for simulations but was used to further the author’s understanding of the methods presented in Henshall et al. (2014). Harry King, Peter Kube, James Kijas, Klara Verbyla, Sonja Dominik shared their insights into the potential application of SNP pooling in selective breeding programmes. James Kijas assisted with comments on draft versions of the manuscript. The CGIAR Research Program on Fish Agrifood Systems (FISH), led by WorldFish and supported by contributors to the CGIAR Trust Fund, financially supported completion of the manuscript subsequent to the author’s departure from CSIRO.

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Correspondence to Matthew Gray Hamilton.

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Hamilton, M.G. Maximum likelihood parentage assignment using quantitative genotypes. Heredity 126, 884–895 (2021). https://doi.org/10.1038/s41437-021-00421-0

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