Background Indicator traits based on variance and autocorrelation of longitudinal data are increasingly used to measure resilience in animal breeding. While these traits show promising heritability and can be routinely collected, their genetic architecture remains poorly understood. We conducted GWAS for three resilience indicators across German Holstein (n = 2,300), Fleckvieh (n = 2,330), and Brown Swiss (n = 1,073) dairy cattle (Bos Taurus) populations. The indicators included variance (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11940_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="18" /> </InlineMediaObject> <EquationSource Format="TEX">\({v}_{d}\)</EquationSource> </InlineEquation>) and autocorrelation (<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11940_Article_IEq2.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="36" /> </InlineMediaObject> <EquationSource Format="TEX">\({r}_{Auto}\)</EquationSource> </InlineEquation>) of deviations of observed from predicted daily milk yield and variance of relative daily milk yield (<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11940_Article_IEq3.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="16" /> </InlineMediaObject> <EquationSource Format="TEX">\({v}_{r}\)</EquationSource> </InlineEquation>). Additionally, we analysed a selection index combining these traits. Prior to GWAS, we examined population structure through multi-dimensional scaling (MDS) and LD patterns, revealing distinct genetic clusters for each breed and similar LD decay patterns. Results The GWAS results confirmed the polygenic nature of resilience, with multiple genomic regions showing significant associations. Notable signals were detected on BTA5 (<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11940_Article_IEq3.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="16" /> </InlineMediaObject> <EquationSource Format="TEX">\({v}_{r}\)</EquationSource> </InlineEquation>), BTA14 (<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11940_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="18" /> </InlineMediaObject> <EquationSource Format="TEX">\({v}_{d}\)</EquationSource> </InlineEquation>), BTA2 and BTA8 (<InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11940_Article_IEq2.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="36" /> </InlineMediaObject> <EquationSource Format="TEX">\({r}_{Auto}\)</EquationSource> </InlineEquation>) for single indicator traits. For selection index resilience, strong suggestive SNPs are located on BTA4, BTA16, BTA21, and BTA27. Detected regions overlapped with previously reported QTLs for performance, reproduction, longevity and health, providing new insights into the biological pathways underlying dairy cattle resilience. Conclusions Our findings demonstrate that resilience indicators have a complex genetic architecture with both breed-specific and shared components, supporting their potential use in selective breeding programs while highlighting the importance of careful trait definition.

Mapping genes for resilient dairy cows by means of across-breed genome-wide association analysis

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2025年10月1日

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Franziska Keßler,
Maximilian Zölch,
Robin Wellman,
Jörn Bennewitz

摘要

Background

Indicator traits based on variance and autocorrelation of longitudinal data are increasingly used to measure resilience in animal breeding. While these traits show promising heritability and can be routinely collected, their genetic architecture remains poorly understood. We conducted GWAS for three resilience indicators across German Holstein (n = 2,300), Fleckvieh (n = 2,330), and Brown Swiss (n = 1,073) dairy cattle (Bos Taurus) populations. The indicators included variance ( \({v}_{d}\) ) and autocorrelation ( \({r}_{Auto}\) ) of deviations of observed from predicted daily milk yield and variance of relative daily milk yield ( \({v}_{r}\) ). Additionally, we analysed a selection index combining these traits. Prior to GWAS, we examined population structure through multi-dimensional scaling (MDS) and LD patterns, revealing distinct genetic clusters for each breed and similar LD decay patterns.

Results

The GWAS results confirmed the polygenic nature of resilience, with multiple genomic regions showing significant associations. Notable signals were detected on BTA5 ( \({v}_{r}\) ), BTA14 ( \({v}_{d}\) ), BTA2 and BTA8 ( \({r}_{Auto}\) ) for single indicator traits. For selection index resilience, strong suggestive SNPs are located on BTA4, BTA16, BTA21, and BTA27. Detected regions overlapped with previously reported QTLs for performance, reproduction, longevity and health, providing new insights into the biological pathways underlying dairy cattle resilience.

Conclusions

Our findings demonstrate that resilience indicators have a complex genetic architecture with both breed-specific and shared components, supporting their potential use in selective breeding programs while highlighting the importance of careful trait definition.