Common sequence variants affect molecular function more than rare variants?

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Evolutionary Biology

Background:

• Individuals differ by thousands of single amino acid variants (SAVs), potentially impacting molecular function.
• Experimental validation of SAV functional impact is not comprehensive; computational prediction methods are essential.
• Existing prediction tools show agreement for rare SAVs but diverge significantly for common SAVs.

Purpose of the Study:

• To computationally predict the functional impacts of single amino acid variants (SAVs) within humans and between species.
• To compare the performance and predictions of different state-of-the-art SAV prediction tools.
• To investigate the evolutionary patterns of SAV functional impact within and across species.

Main Methods:

• Utilized four prediction methods: CADD, PolyPhen-2, SIFT, and SNAP2.
• Analyzed SAVs within the human population using large datasets (ExAC, 60,706 individuals).
• Compared predicted functional impacts of SAVs within humans versus SAVs between human and other species.

Main Results:

• Four prediction methods showed agreement on rare SAVs but substantial differences for common SAVs.
• SNAP2 predicted a higher functional impact for common SAVs compared to rare SAVs, differing from other methods.
• A significantly higher fraction of SAVs were predicted to have functional effects within humans than between species, with this difference increasing for more distant species.

Conclusions:

• SNAP2's unique development focus may provide more accurate predictions for common SAVs.
• SAVs conserved across species may be functionally constrained, while intra-species variants drive significant adaptive or deleterious changes.
• The study highlights distinct evolutionary pressures on variants within and between species, impacting protein function.