The birds at the center of the study were not commercial giants but Egyptian black Baladi turkeys, the country’s indigenous breed. Glossy black feathers, moderate body size, and a reputation for surviving harsh conditions and resisting local diseases make the Baladi a valuable reservoir of genetic diversity, even though its growth rate trails that of commercial strains. The breed has long been maintained through uncontrolled natural mating in backyard flocks across the Nile Delta and Upper Egypt, with selection based mainly on visible traits such as body weight, fertility, and survivability. No nationwide breeding or conservation program exists for the breed, which is precisely why the researchers, led by Mohamed H. Khalil of Alexandria University and Mostafa K. Shebl, wanted to quantify how much genetic progress structured selection could deliver.

The experiment began in 2019 at the Poultry Research Centre of Alexandria University, where 193 birds formed the randomly mated base population. From this flock, the heaviest birds at six months of age, 13 males and 55 females, were chosen as parents of a selected line, while a random-bred control line was maintained from the same base. Over the following generation, researchers recorded body weights at hatching and at every month through six months, along with shank length and breast width at six months. The birds were reared under identical floor-pen conditions with feed formulated to standard requirements at each growth stage, ensuring that any differences between the lines reflected genetics rather than management.

The numbers tell a clear story. At six months, selected-line turkeys averaged 4404.61 grams compared with 3967.25 grams in the control line, a statistically significant difference and the only age point where the lines diverged significantly. Regression analysis estimated average monthly growth of 768.03 grams in the selected line versus 710.67 grams in the control. Sex mattered enormously too: males at six months weighed 5599.07 grams on average against 3419.71 grams for females, a striking sexual dimorphism of roughly 63.7 percent. Shank length and breast width were also significantly greater in the selected line, confirming that selection for weight pulled skeletal and muscular dimensions along with it.

To predict future gains, the team constructed a selection index, a statistical tool that combines multiple traits into a single score weighted by heritability, genetic correlations, and economic value. Using the framework of Cunningham, they built an index from body weight, shank length, and breast width at six months, solving matrix equations involving the phenotypic and genetic variance-covariance matrices to derive the optimal weighting factors. The resulting index showed a correlation of 0.84 with the aggregate breeding value, a high accuracy that means the score ranks animals’ genetic merit reliably. The expected genetic gains per generation were substantial: 441.71 grams of body weight, 0.26 centimeters of shank length, and 1.93 centimeters of breast width.

Perhaps the most revealing finding came from dissecting which traits drove the index. Removing breast width from the calculation reduced overall genetic gain by 18.57 percent, the largest contribution of any trait, followed by body weight at 7.69 percent, while shank length contributed a mere 1.28 percent. The genetic correlation between body weight and breast width was an extraordinary 0.97, meaning the two traits are almost entirely controlled by the same genes. For breeders, this makes breast width an exceptionally reliable indirect criterion for improving meat yield, and it explains why the correlated response in body dimensions followed so faithfully from selection on weight alone.

Then came the molecular layer. The researchers sampled the Pectoralis major, the main breast muscle, from 18 birds across both lines and both sexes, extracting RNA and quantifying growth hormone mRNA using quantitative reverse transcription PCR, with beta-actin as the reference gene and the 2-delta-Ct method for normalization. Growth hormone, a polypeptide secreted by the pituitary gland, sits at the heart of the somatotropic axis that governs growth, muscle development, protein synthesis, fat metabolism, and even reproduction in poultry. The selected line showed significantly higher GH expression than the control in both sexes: a relative expression of 1.43 versus 1.01 in males, and 1.76 versus 1.31 in females. Intriguingly, females expressed more GH than males despite being far lighter, echoing earlier reports of sex differences in GH expression.

The authors are careful about what this association does and does not prove. Higher GH-mRNA expression in the selected line is consistent with the idea that molecular mechanisms underpin the phenotypic gains, but the study does not establish causation. The elevated expression may simply reflect the physiology of a heavier phenotype rather than a direct consequence of selection, and the researchers explicitly note that further work is needed to clarify the biology and to validate GH expression as a molecular indicator in breeding programs. Previous studies have linked GH gene polymorphisms and expression patterns to growth traits in chickens and quail, and earlier turkey research found that growth-selected lines showed higher hepatic growth hormone receptor binding, so the new results fit a broader pattern across poultry genetics.

For the Baladi breed, the practical implications are immediate. A selection index with 0.84 accuracy offers smallholder-oriented breeding programs a way to improve growth while preserving the breed’s adaptive traits, and the identification of breast width as the highest-value index trait gives breeders a concrete measurement to prioritize. The GH expression findings open a longer-term possibility: combining conventional phenotypic selection with molecular markers to accelerate gains in indigenous livestock, where genomic tools remain scarce. As global demand for poultry protein grows and indigenous breeds face pressure from commercial crosses, studies like this one show that the old tools of quantitative genetics and the new tools of gene expression can work together, weighing birds in the pen and reading their genes in the laboratory to chart a faster, more informed path of genetic improvement.

Subject of Research: Selection index prediction of genetic gain and growth hormone gene expression in Egyptian Baladi turkeys

Article Title: GH-mRNA expression and prediction of genetic improvement using selection index for body weight traits in turkeys

Article References: Khalil, M. H., Abd El-Motaleb, A. A., Abdel-Rahman, M. M., Amin, E. M., Abousoliman, I., & Shebl, M. K. (2026). GH-mRNA expression and prediction of genetic improvement using selection index for body weight traits in turkeys. Archives Animal Breeding, 69(3), 411-420. https://doi.org/10.5194/aab-69-411-2026

Image Credits: AI Generated

DOI: 10.5194/aab-69-411-2026

Keywords: turkeys, growth hormone, selection index, genetic improvement, body weight, Baladi, gene expression, qRT-PCR, poultry breeding, heritability, breast width, molecular marker

The research team, led by Roberto Fritsche-Neto of North Carolina State University together with colleagues in Brazil, used stochastic computer simulations to model a rice breeding program over 30 years. Rather than relying on historical field data, which reflects past conditions and cannot easily be re-run under alternative strategies, the researchers built a virtual breeding pipeline in which they could systematically vary the selection method while holding everything else constant. The simulation was anchored in the real-world parameters of the Louisiana State University AgCenter rice breeding program and conducted with the AlphaSimR package, giving the results a practical grounding that purely theoretical exercises often lack.

The simulated program targeted three traits simultaneously: increasing grain yield, reducing the chalkiness rate that degrades rice grain quality, and keeping plant height stable to avoid lodging. Grain yield received the heaviest weight, at 70 percent of the selection objective, while chalkiness and plant height each received 15 percent. The team compared seven selection pipelines built around three classical indices: an empirical index with fixed weights, the Smith–Hazel index that uses genetic and phenotypic covariance matrices, and the Pesek–Baker index, which is constructed from desired gains rather than economic weights. Each index was tested under traditional phenotypic selection and under genomic selection, with the genomic component applied either before index construction or after the index had already been calculated from phenotypes.

The technical distinction between those two architectures turned out to be central. In the first approach, univariate genomic prediction models were fitted separately for each trait using ridge regression best linear unbiased prediction, generating genomic estimated breeding values for yield, chalkiness, and height individually before combining them into an index. In the second, the multi-trait index was first computed from phenotypic data and then treated as a single synthetic trait for genomic prediction. The researchers also refreshed the training population each cycle using a grandparents-parents-offspring strategy, one of the most effective approaches for maintaining prediction accuracy across recurrent cycles, and ran 100 independent replicates of every scenario to capture the stochastic uncertainty inherent in real breeding.

When the 30-year trajectories were compared, no single strategy dominated every trait, but a clear pattern emerged. Applying genomic selection directly to a phenotypically constructed Pesek–Baker index produced the highest gains for grain yield, reaching values close to 10.3 on the standardized scale. When genomic prediction was performed trait by trait before index construction, the Smith–Hazel index delivered the best yield response, near 10.0. For chalkiness, however, the trait-specific prediction approach paired with the Pesek–Baker index was the clear winner, driving the population mean down to roughly negative 4.0, the most favorable reduction observed, compared with only about negative 1.6 under the traditional empirical index.

Plant height told a subtler story. Because the breeding objective was stability rather than directional change, strategies that pushed height upward were actually performing poorly against the stated goal. The Smith–Hazel index combined with genomic selection produced the largest increases in plant height, which ran counter to the program’s intent. The Pesek–Baker index, whether used traditionally or with trait-specific genomic prediction, kept height changes moderate, plateauing near 3.5, while the empirical index was the most conservative of all. Taken together, the results underscore a principle that breeders know intuitively but rarely test so rigorously: the best pipeline is the one that matches the full set of objectives, not the one that maximizes a single headline trait.

The dynamics of selection accuracy and additive genetic variance added a deeper layer to the findings. As expected in recurrent selection, accuracy generally declined over cycles as favorable alleles fixed and the population became more uniform. But the rate and pattern of that decline depended sharply on how genomic information was deployed. When genomic prediction was applied directly to an already aggregated index, accuracy for chalkiness and plant height collapsed dramatically, approaching zero within the first breeding cycle before a partial recovery. The authors attribute this to information compression: folding three traits with different heritabilities, correlations, and selection directions into one value before prediction strips away the trait-specific genetic signals the model needs to learn.

In contrast, estimating genomic values for each trait separately before building the index preserved that information and produced responses more consistent with the multi-trait objectives, with gentler accuracy losses and greater stability after the initial cycles. Additive genetic variance told a parallel story, with genomic scenarios generally depleting variability faster than traditional ones, and the greatest losses appearing in the Smith–Hazel and index-first genomic pipelines. The researchers caution that residual variance alone is not proof of future gain, but the joint pattern of eroding accuracy and shrinking variance in the index-first scenarios points to a genuine methodological limitation rather than simple exhaustion of genetic variation.

The practical implications reach well beyond rice. The study’s authors argue that genomic selection should not be viewed as a wholesale replacement for phenotypic selection but as a tool whose value depends on where it is inserted in the pipeline. For programs juggling traits that must move in opposite directions, such as raising yield while lowering chalkiness, the trait-specific prediction route followed by index construction appears the more reliable architecture. The Pesek–Baker index, built explicitly around desired gains, proved the most faithful vehicle for translating a breeder’s ideotype into a selection criterion, delivering strong yield gains, the deepest chalkiness reduction, and minimal unwanted shifts in plant height. Similar logic applies in crops like malting barley, where quality traits must be held within narrow thresholds rather than pushed to extremes.

The authors acknowledge limitations that shape how far these conclusions can be generalized. The simulations used a fixed marker density of 540 SNPs, and prediction accuracy depends on linkage disequilibrium between markers and causal loci, so different genotyping platforms could shift the magnitude of the observed responses. The genetic architecture was purely additive, with no dominance or epistasis, and only three traits were modeled. Future work, the team notes, should expand the trait set and validate the findings in real breeding populations. Still, for a field racing to feed a growing population under a changing climate, the message is concrete and actionable: in multi-trait breeding, the sequence in which genomic prediction and selection indices are combined is not a technical footnote but a decision that can determine both the magnitude and the direction of genetic progress.

Subject of Research: Integration of genomic prediction with multi-trait selection indices in simulated rice breeding programs

Article Title: Combining genomic prediction and multi-trait indices through stochastic simulations: do index type and deployment order affect genetic gain?

Article References: Fritsche-Neto, R., Queiroz, L. G. C., Viana, J., Gupta, K., Grover, K., & DoVale, J. C. (2026). Combining genomic prediction and multi-trait indices through stochastic simulations: do index type and deployment order affect genetic gain?. Theoretical and Applied Genetics, 139(10), Article 284. https://doi.org/10.1007/s00122-026-05398-0

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05398-0

Keywords: genomic selection, selection index, plant breeding, rice, stochastic simulation, Pesek–Baker index, Smith–Hazel index, genetic gain, grain yield, chalkiness, additive genetic variance, RR-BLUP