Exploiting family history in aggregation unit-based genetic association tests

Area of Science:

• Genetics
• Biostatistics
• Computational Biology

Background:

• Sequencing technology advancements necessitate novel methods for disease association studies.
• Family history (FH) data offers cost-effective insights into disease risk and genetic associations.
• Integrating FH data can overcome limitations of insufficient cases or missing genotype information.

Purpose of the Study:

• To develop and evaluate novel statistical methods for rare variant association analysis using family history data.
• To compare the performance of proposed methods against existing approaches.

Main Methods:

• Proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O).
• Extended the liability threshold model of case-control status and FH (LT-FH) for aggregated unit-based analysis.
• Conducted simulations and applied methods to UK Biobank exome sequencing data for dementia and hypertension.

Main Results:

• FHAT, FHAT-O, and LT-FH provide reasonable type I error control, with FHAT and FHAT-O showing improved power over LT-FH and conventional methods.
• FHAT and FHAT-O demonstrated computational efficiency and flexibility.
• Application to UK Biobank data improved significance for known disease regions and identified novel associations for dementia and hypertension.

Conclusions:

• FHAT and FHAT-O are powerful and efficient methods for rare variant association analysis incorporating family history.
• These methods enhance the detection of genetic associations for complex diseases.
• The findings support the utility of family history data in large-scale genetic studies.