In livestock populations, genome-wide association studies (GWAS) can produce strong, apparently localized associations even when no truly discrete nearby causal effect exists. This occurs because small effective population sizes, strong family structure, long-range linkage disequilibrium (LD), and diffuse polygenic architecture can cause the effects of many variants to accumulate and be captured jointly across broad genomic intervals, making variant-level associations difficult to interpret biologically. Using real pig genotypes, we constructed a benchmark in which phenotypes were simulated under diffuse polygenic architecture across a genome partitioned into alternating effect and null windows, with central-null regions (at least 1 Mb away from effect-containing regions) positioned to detect long-range LD-driven signal propagation. We evaluated nine configurations of six GWAS methods (BOLT-LMM, REGENIE, fastGWA, FarmCPU, BLINK, and SLEMM) under this architecture. The central finding is that strong associations, of the kind normally read as evidence of nearby moderate- or large-effect variants, are produced by many of these methods even though the simulated signal is distributed across many tiny effects and cannot be localized to any single variant. The methods differed sharply in the extent of locus-level spillover: several produced large numbers of genome-wide significant loci within central-null regions, whereas the full-GRM mixed-model benchmark (SLEMM) produced no genome-wide significant loci in central-null regions. These results show that, under a highly polygenic architecture with livestock-like LD, GWAS tool choice has major consequences for biological interpretation. When the goal is to localize biologically meaningful signals rather than to flag association peaks that may merely reflect tiny effects accumulated through LD across a broad block, methods that control long-range LD spillover should be prioritized.
Xuesong Wang, Junji Wang, F. Tiezzi et al.· bioRxiv· 0 citations
The objective of this study was to test whether the negative effects of inbreeding on production traits varied according to the level of environmental load. Traits analyzed were milk, fat, and protein yields, and somatic cell score (SCS). Environmental conditions were described using temperature (TEMP), relative humidity (RH), and the temperature–humidity index (THI), each divided into five equally sized classes. For each trait, the environmental variable with the largest impact was used to evaluate inbreeding effects across its classes. Inbreeding was measured using pedigree (FPED), the diagonal of the genomic relationship matrix (FGRM), and runs of homozygosity (FROH). Genomic-based inbreeding measures resulted in larger estimated inbreeding effects, compared to pedigree measures, with FGRM and FROH showing similar results. RH most affected milk yield, with losses of ~ 600 g/day in the highest RH class. Inbreeding led to losses of ~ 100 g/day per 1% increase, with genomic measures scaled to match FPED. For milk yield, losses were ~ 20% greater under higher environmental stress. THI was the most impactful variable for fat yield, with losses of ~ 90 g/day in the highest THI category. Inbreeding-related losses increased in more stressful environments, with average reductions of 3 g/day per 1% inbreeding, reaching 4 g/day in the highest THI class. Protein yield and SCS were mainly influenced by TEMP, with losses of ~ 70 g/day of protein and increases of 0.06 SCS units. Inbreeding caused losses of ~ 3.5 g/day in protein yield and increases of ~ 0.006 SCS units per 1% inbreeding. For these two traits, the relationship between inbreeding effects and environmental stress was less clear. For milk and fat yields, there was a significant interaction between environmental and inbreeding effects, suggesting that inbreeding depression influences heat tolerance in dairy cows. For milk and fat yields, there was a significant interaction between environmental and inbreeding effects, indicating that environmental conditions modulate the expression of inbreeding depression. These findings suggest that inbreeding depression influences heat tolerance in dairy cows.
F. Tiezzi, J. Panetto, S. Callegaro et al.· Genetics Selection Evolution· 0 citations
