Results of quantitative genetic sensitivity analysis performed on reconstructed pedigrees based on large-scale genealogies

Investigating the evolution of complex traits in nature requires accurate assessment of their genetic basis. Quantitative genetic (QG) modeling is frequently applied to estimate the additive genetic variance (VA) in traits, combining phenotypic and pedigree data from a sample of individuals. Whether reconstructed from social links or molecular markers, empirical pedigrees differ in completeness, genealogical error rates and other attributes that can impact QG estimation. Here we investigate this impact using human genealogical data for six French-Canadian (FC) populations originating from the same genetic founding source but differing in their pedigrees’ attributes. First, we simulated phenotypic values along pedigrees and under different trait architecture and ‘true’ parameter values (e.g. VA). Then we fitted mixed effects ‘animal’ models to these simulated data, to assess how QG estimation was impacted by pedigree attributes. Our results show that pedigree siz..., The dataset contains the output data from analysis done on a set of reconstructed pedigrees from the French-Canadian genealogies provided by the BALSAC group. The raw data is private and is only available upon request at the moment. Therefore the pedigree data is not provided and only the output data and related code analysis can be found in this dataset. , , # Results of quantitative genetic sensitivity analysis performed on reconstructed pedigrees based on large-scale genealogies

[https://doi.org/10.5061/dryad.fn2z34v5q](https://doi.org/10.5061/dryad.fn2z34v5q)

We have submitted our fitted model output data to recreate the published figures (**scenario_1_data.zip**,**scenario_1_data_increased_error_rate.zip**,**scenario_2_data.zip**,**scenario_3_data.zip**), R scripts (**pedgiree.R**, **bayestest.R**,**genlib.R**).

## Descriptions

### **scenario\_1\_data.zip**

This zip files contains 12 rds files (6 pedigrees x 2 tested values) for Scenario 1 outlined in published paper corresponding to a univariate phenotype simulation with additive and environmental variances components. Each RDS file should contain 5 columns, each column contains the *1,000 saved MCMC samples* from the posterior distribution of the estimated additive variance of the simulated phenotype corresponding to a single *phenotype simulation + MCMCglmm run*.

### **scenario...