Scientific basis

Research & references.

Genemap doesn't invent its mathematics. The engine builds on six decades of quantitative-genetics literature plus contemporary work on epigenetics, microbiome inheritance, and methane intensity. Every load-bearing module cites the work it implements.

Selection-index theory 5.0a

Bioeconomic weights derived per-farm from breeding-value EBVs.

Hazel, L.N.1943The genetic basis for constructing selection indexes.Genetics 28: 476–490.
Hazel, L.N. & Lush, J.L.1942The efficiency of three methods of selection.Journal of Heredity 33: 393–399.
Smith, H.F.1936A discriminant function for plant selection.Annals of Eugenics 7: 240–250.
Goddard, M.E.1998Consensus and debate in the definition of breeding objectives.Journal of Dairy Science 81 (S2): 6–18.

Discounted gene flow 5.0b

Lifetime value of every gene, NPV-discounted across daughter and granddaughter pathways.

Brascamp, E.W.1978Methods on economic evaluation of efficiency of selection: a review.Livestock Production Science 5: 263–275.
McClintock, A.E. & Cunningham, E.P.1974Selection in dual-purpose cattle populations.Animal Production 18: 237–247.
Hill, W.G.1974Prediction and evaluation of response to selection with overlapping generations.Animal Production 18: 117–139.

BLUP & mixed-model evaluation BLUP.1

Henderson's mixed-model equations for proper contemporary-group adjustment.

Henderson, C.R.1975Best linear unbiased estimation and prediction under a selection model.Biometrics 31: 423–447.
Henderson, C.R.1984Applications of linear models in animal breeding.University of Guelph, Canada.
Patterson, H.D. & Thompson, R.1971Recovery of inter-block information when block sizes are unequal.Biometrika 58: 545–554.
Misztal, I., Tsuruta, S., Strabel, T., et al.2002BLUPF90 and related programs.7th WCGALP Proceedings 28: 743–744.

Genomic prediction · ssGBLUP 5.0d

Per-farm GWAS / per-trait DGV ridge regression against realised profit.

Meuwissen, T.H.E., Hayes, B.J. & Goddard, M.E.2001Prediction of total genetic value using genome-wide dense marker maps.Genetics 157: 1819–1829.
VanRaden, P.M.2008Efficient methods to compute genomic predictions.Journal of Dairy Science 91: 4414–4423.
Aguilar, I., Misztal, I., Johnson, D.L., et al.2010Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.Journal of Dairy Science 93: 743–752.
Hayes, B.J., Bowman, P.J., Chamberlain, A.J. & Goddard, M.E.2009Invited review: genomic selection in dairy cattle: progress and challenges.Journal of Dairy Science 92: 433–443.

Mate allocation & programme planning MA.1 · MAS.1 · NP.1

Min-cost-flow assignment of bulls to cow groups, ZPLAN-style nucleus-pyramid optimisation.

Jansen, G.B. & Wilton, J.W.1985Selecting mating pairs with linear programming techniques.Journal of Dairy Science 68: 1302–1305.
Kinghorn, B.P.2011An algorithm for efficient constrained mate selection.Genetics Selection Evolution 43: 4.
Niebel, E. & van Vleck, L.D.1983Comparison of mating systems for genetic gain.Journal of Dairy Science 66: 1942–1949.
Karras, K., Niebel, E., Graser, H.U., et al.1997ZPLAN: a PC computer programme to optimise livestock selection schemes.Hohenheim, Germany.

Epigenetics & methylation EP.1

Frontier signal: DNA methylation at known stress-response loci as a per-animal economic modifier.

Tao, S., Connor, E.E., Bubolz, J.W., et al.2014Effect of heat stress during the dry period on gene expression in mammary tissue and peripheral blood mononuclear cells.Journal of Dairy Science 97: 4615–4623.
Boddicker, R.L., Seibert, J.T., Johnson, J.S., et al.2014Gestational heat stress alters postnatal offspring body composition indices and metabolic parameters in pigs.PLoS ONE 9: e110859.
Skibiel, A.L., Peñagaricano, F., Amorín, R., et al.2018In utero heat stress alters the offspring epigenome.Scientific Reports 8: 14609.
Reynolds, L.P. & Caton, J.S.2012Role of the pre- and post-natal environment in developmental programming of health and productivity.Molecular and Cellular Endocrinology 354: 54–59.
Funston, R.N., Larson, D.M. & Vonnahme, K.A.2010Effects of maternal nutrition on conceptus growth and offspring performance.Journal of Animal Science 88 (E. Suppl.): E205–E215.

Rumen microbiome & FCE EP.1.C

Maternally-transmitted microbiome as a feed-conversion modifier and methane proxy.

Roehe, R., Dewhurst, R.J., Duthie, C.A., et al.2016Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane emitting and efficiently feed converting hosts.PLoS Genetics 12: e1005846.
Difford, G.F., Plichta, D.R., Løvendahl, P., et al.2018Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows.PLoS Genetics 14: e1007580.
Stergiadis, S., Cabeza-Luna, I., Mora-Ortiz, M., et al.2021Unravelling the role of rumen microbial communities, genes, and activities on milk fatty acid profile using a combination of omics approaches.Frontiers in Microbiology 11: 590441.

Methane intensity & carbon CH4.1

Per-animal residual methane intensity as a selectable trait with $/kg CO2e weighting.

Hayes, B.J., Donoghue, K.A., Reich, C.M., et al.2020Genomic prediction of feed efficiency and methane production in beef cattle.Journal of Animal Science 98 (S3): 132.
de Haas, Y., Pszczola, M., Soyeurt, H., et al.2017Invited review: phenotypes to genetically reduce greenhouse gas emissions in dairying.Journal of Dairy Science 100: 855–870.
Pickering, N.K., Oddy, V.H., Basarab, J., et al.2015Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants.Animal 9: 1431–1440.

Pelt-primary genetics SHEEP system

Pälskvalitet, Lockighet, Lyster, Färg as evaluable traits for Swedish Gotland and Karakul producers.

Näsholm, A. & Danell, B.2007Genetic parameters and breeding objectives for traits in Gotland sheep.Small Ruminant Research 71: 110–118.
Eythorsdottir, E. & Thorgeirsson, S.1999The effect of slaughter weight and breed type on woolskin quality in Icelandic lambs.Livestock Production Science 60: 261–267.

Tropical adaptation & Bos indicus

Indigenous breed support for Bonsmara, Brahman, Nguni, Drakensberger, Tuli, Nelore.

Burrow, H.M.2012Importance of adaptation and genotype × environment interactions in tropical beef breeding systems.Animal 6: 729–740.
Frisch, J.E. & Vercoe, J.E.1984An analysis of growth of different cattle genotypes reared in different environments.Journal of Agricultural Science 103: 137–153.

Methodology page: the full Genemap engine architecture is documented at /methodology.html. The mathematics is also documented as code — see /core/js/ai-enterprise-cattle.js and the related modules in the source tree. We welcome academic collaboration; reach out via research@genemap.com.au for citation, replication or partnership enquiries.