A data-adaptive sum test for disease association with multiple common or rare variants

Area of Science:

• Genetics
• Statistical genetics
• Genomic association studies

Background:

• Complex diseases often show weak associations with common genetic variants.
• Advances in next-generation sequencing increase the availability of rare variant data.
• Existing methods may not optimally detect associations involving both common and rare variants.

Purpose of the Study:

• To develop a powerful statistical test for detecting disease associations with both common and rare genetic variants.
• To improve upon existing methods for genetic association analysis.

Main Methods:

• Data-adaptive modification of the Sum test, originally designed for common variants.
• Application to multiple common or rare variants within a candidate region.
• Evaluation of the test's performance across diverse genetic scenarios.

Main Results:

• The proposed test is user-friendly, requiring only 1 degree of freedom and no multiple testing adjustment.
• It demonstrates high statistical power for detecting associations with common variants, rare variants, or both.
• In certain scenarios, the new test outperforms several widely used association methods.

Conclusions:

• The developed statistical test offers a powerful and flexible approach for genetic association studies.
• It effectively integrates information from multiple variants, including both common and rare types.
• This method provides a valuable tool for identifying genetic underpinnings of complex diseases.