Summix: A method for detecting and adjusting for population structure in genetic summary data.

Publicly available genetic summary data have high utility in research and the clinic, including prioritizing putative causal variants, polygenic scoring, and leveraging common controls. However, summarizing individual-level data can mask population structure, resulting in confounding, reduced power, and incorrect prioritization of putative causal variants. This limits the utility of publicly available data, especially for understudied or admixed populations where additional research and resources are most needed. Although several methods exist to estimate ancestry in individual-level data, methods to estimate ancestry proportions in summary data are lacking. Here, we present Summix, a method to efficiently deconvolute ancestry and provide ancestry-adjusted allele frequencies (AFs) from summary data. Using continental reference ancestry, African (AFR), non-Finnish European (EUR), East Asian (EAS), Indigenous American (IAM), South Asian (SAS), we obtain accurate and precise estimates (within 0.1%) for all simulation scenarios. We apply Summix to gnomAD v.2.1 exome and genome groups and subgroups, finding heterogeneous continental ancestry for several groups, including African/African American (∼84% AFR, ∼14% EUR) and American/Latinx (∼4% AFR, ∼5% EAS, ∼43% EUR, ∼46% IAM). Compared to the unadjusted gnomAD AFs, Summix's ancestry-adjusted AFs more closely match respective African and Latinx reference samples. Even on modern, dense panels of summary statistics, Summix yields results in seconds, allowing for estimation of confidence intervals via block bootstrap. Given an accompanying R package, Summix increases the utility and equity of public genetic resources, empowering novel research opportunities.

Hydrogen bonding is the prime determinant of carboxyl pKa values at the N-termini of alpha-helices.

Experimentally determined mean pK(a) values of carboxyl residues located at the N-termini of alpha-helices are lower than their overall mean values. Here, we perform three types of analyses to account for this phenomenon. We estimate the magnitude of the helix macrodipole to determine its potential role in lowering carboxyl pK(a) values at the N-termini. No correlation between the magnitude of the macrodipole and the pK(a) values is observed. Using the pK(a) program propKa we compare the molecular surroundings of 18 N-termini carboxyl residues versus 233 protein carboxyl groups from a previously studied database. Although pK(a) lowering interactions at the N-termini are similar in nature to those encountered in other protein regions, pK(a) lowering backbone and side-chain hydrogen bonds appear in greater number at the N-termini. For both Asp and Glu, there are about 0.5 more hydrogen bonds per residue at the N-termini than in other protein regions, which can be used to explain their lower than average pK(a) values. Using a QM-based pK(a) prediction model, we investigate the chemical environment of the two lowest Asp and the two lowest Glu pK(a) values at the N-termini so as to quantify the effect of various pK(a) determinants. We show that local interactions suffice to account for the acidity of carboxyl residues at the N-termini. The effect of the helix dipole on carboxyl pK(a) values, if any, is marginal. Backbone amide hydrogen bonds constitute the single biggest contributor to the lowest carboxyl pK(a) values at the N-termini. Their estimated pK(a) lowering effects range from about 1.0 to 1.9 pK(a) units.