Nugroho, T. | Search bioRxiv

bioRxiv2026

Diffuse phenotypic structure in native chickens across Java and Wallacea is consistent with human-mediated connectivity and decentralised selection

Objective: This study aimed to investigate morphometric and plumage-colour variation among native chickens from Wallacea and Java, Indonesia, and to evaluate the contributions of geographic and production-system factors to phenotypic diversity. Methods: Native chickens were sampled from 259 households across eight provinces in Indonesia. Household-level information on production systems and farmer selection practices was collected through structured surveys. Morphometric traits and HSV-derived plumage-colour variables were analysed using principal component analysis (PCA), permutational multivariate analysis of variance (PERMANOVA), and hierarchical variance partitioning to assess phenotypic variation across locations, provinces, and islands. Results: Substantial phenotypic variation was observed among sampled populations, with significant province- and island-level differences detected for several morphometric traits. However, extensive overlap among locations was evident in both morphometric and plumage-colour space, indicating diffuse rather than discrete structuring. Within-location variation accounted for the largest proportion of total phenotypic variance across most traits, consistent with decentralised smallholder management and continued human-mediated exchange. Conclusion: Geographic fragmentation and agroecological heterogeneity alone appear insufficient to generate strongly differentiated phenotypic groups when connectivity among populations is maintained. Whether this phenotypic connectivity corresponds to contemporary gene flow, retained ancestral variation, or convergent environmental responses remains to be tested using genomic data.

ecology↗

bioRxiv2026

Detection and evaluation of copy number variation using both linked-read and short-read sequencing in New Zealand dairy cattle

In recent years, genetic studies have made significant progress in identifying single-nucleotide polymorphisms (SNPs) associated with cattle health and production traits. However, it is still challenging to identify and validate more complicated forms of variation, such as copy number variation (CNV) and other types of structural variation (SV). In this study, SV regions were identified using 37 New Zealand dairy cattle with linked-read sequence data. A transmission-based framework was used to validate these variants at the population scale. 62,438 putative autosomal SV regions were identified with the LongRanger pipeline following the 10x Genomics recommendations. Copy number states for these regions were subsequently estimated via a read-depth based genotyping method using CNVpytor in a population-representative cohort of 2306 animals using Illumina short-read sequencing technology. Mendelian inheritance of copy number states was assessed using linear mixed models incorporating pedigree information, and transmission levels were used to quantify the biological validity of each CNV region. Transmission levels ranged widely, with a mean of 0.5162 across all regions, where higher transmission levels were proportionally enriched for larger SVs. A total of 7218 CNV regions exhibited high transmission levels (>0.9), indicating strong evidence of inheritance. Among these, 7136 overlapped CNV regions reported in one or more public datasets, while 82 high-confidence regions represent previously unreported variants. High-transmission CNV regions tended to show clear, discrete inheritance patterns in trio families, providing the biological evidence that these CNVs are inherited within the population. Together, these results demonstrate that integrating linked-read sequencing with population-scale transmission-based validation provides a robust framework for identifying high-confidence CNV regions. This catalogue of validated CNV regions represents an important resource for downstream functional analyses and the incorporation of structural variation into genomic selection and breeding programs.

bioinformatics↗