Assortative Mating on Ancestry-Variant Traits in Admixed Latin American Populations

Assortative mating is a universal feature of human societies, and individuals from ethnically diverse populations are known to mate assortatively based on similarities in genetic ancestry. However, little is currently known regarding the exact phenotypic cues, or their underlying genetic architecture, which inform ancestry-based assortative mating. We developed a novel approach, using genome-wide analysis of ancestry-specific haplotypes, to evaluate ancestry-based assortative mating on traits whose expression varies among the three continental population groups – African, European, and Native American – that admixed to form modern Latin American populations. Application of this method to genome sequences sampled from Colombia, Mexico, Peru, and Puerto Rico revealed widespread ancestry-based assortative mating. We discovered a number of anthropometric traits (body mass, height, and facial development) and neurological attributes (educational attainment and schizophrenia) that serve as phenotypic cues for ancestry-based assortative mating. Major histocompatibility complex (MHC) loci show population-specific patterns of both assortative and disassortative mating in Latin America. Ancestry-based assortative mating in the populations analyzed here appears to be driven primarily by African ancestry. This study serves as an example of how population genomic analyses can yield novel insights into human behavior

Bioinformatic platforms and methods for worldwide polygenic risk scores

Genetic diversity underpins much of observed human phenotypic diversity and plays an important role in human health and disease. This dissertation is focused on exploring the genetic architecture of phenotypic diversity among global populations and studying common complex disease in genetically diverse but geographically close communities. This work is motivated by prevalent health disparities that disproportionately affect disadvantaged populations across the world, and in particular, those in the Americas. I utilize thousands of genomes from diverse populations worldwide, along with hundreds of genome-wide association studies (GWAS) on thousands of human traits, to address three overarching questions: (1) which phenotypes vary among populations, and what explains that variance?; (2) is it possible to predict and stratify risk for common complex diseases across diverse populations?; and (3) can we apply already discovered genetic associations to risk prediction in new and ancestrally distinct populations? Polygenic risk scores (PGS) are increasingly used to quantify individuals’ genetic predisposition for disease. I developed the first of its kind web platform for PGS computation and visualization, GADGET, The Global Distribution of Genetic Traits webserver (https://gadget.biosci.gatech.edu/). GADGET enables biomedical researchers to easily test hypotheses and generate publication-ready visualizations of PGS for thousands of individuals in 27 global populations. I also developed a specialized, country and population-specific PGS server, the Colombian Phenotype-Genotype Browser (CPGB; https://map.chocogen.com/), to support precision public health efforts in Colombia. Next, I leveraged the PGS curation from GADGET to explore the differentiation of single loci and polygenic traits between neighboring populations of Afro-Colombians in Chocó and Euro-Colombians in Antioquia. I developed PGS and found that they largely reflect the observed health disparities for seven high-cost and high-burden common complex diseases in Colombia. Interestingly, PGS for type 2 diabetes (T2D) significantly over-predicted risk in Afro-Colombians. Further analysis of T2D in Colombia revealed the importance of environmental and lifestyle effects on T2D. In Colombia, in contrast to much of the developed world, low socioeconomic status was correlated with decreased prevalence for T2D. My final study brings the focus back to the US and developed a correction method for applying already ascertained SNP-trait associations, again for T2D, in diverse populations. I predicted T2D risk in Mexican-Americans and European-Americans and validated my predictions at the population level using epidemiological data. A simulation-based correction method utilizing the derived allele frequency spectrum for trait-associated variants was used to correct PGS bias between ancestrally divergent populations. Together, these studies underscore how genetic diversity contributes to global phenotypic variance. Differences in population PGS distributions are generally an accurate indicator of relative disparities between populations in a country; although, differences in ancestry impact the accuracy of individuals’ PGS. In cases where predictions do not match observed disparities, there are significant socioeconomic and environmental effects that mediate the genetic component of disease risk. Finally, simulation-based controls showed promise for helping to account for and correct bias in PGS when transferring associations between populations with distinct ancestry.Ph.D

Characterization of a nontypeable Haemophilus influenzae thermonuclease.

Nontypeable Haemophilus influenzae (NTHi) has been shown to form biofilms, comprised of extracellular DNA (eDNA), in the middle ear and bronchus during clinical infections. Studies in our laboratory have shown that NTHi possesses a homolog of Staphylococcus aureus thermonuclease (staphylococcal thermonuclease), NTHi nuclease (NTHi Nuc, HI_1296). This enzyme had similar size, heat stability, and divalent cation requirements to those of the staphylococcal homolog as determined by light scattering and circular dichroism spectroscopy. Small angle X-ray scattering (SAXS) analysis suggested an overall shape and substrate-binding site comparable to those of staphylococcal nuclease. However, NTHi Nuc was approximately 25-fold more active in fluorescence resonance energy transfer (FRET) activity assay than staphylococcal thermonuclease. Homology modeling implicates shorter NTHi Nuc loops near the active site for this enhanced activity

Personalized Feedback about Immunity Corrects Risk Misestimation and Motivates Vaccination

Few individuals are up-to-date on COVID-19 vaccines, leading to widespread gaps in protection. Current vaccine communication strategies emphasize availability, with limited effectiveness for motivating recurring vaccinations and vaccine-hesitant individuals. To address this gap, we developed an intervention that targeted beliefs about immunity, providing personalized feedback about likely protection against COVID-19. In an online sample of participants (N=882, stratified by age and gender), this intervention effectively changed immunity beliefs and increased vaccination intentions. Two months later, belief changes endured, and self-reported vaccine uptake was approximately 8x higher than the US-national rate during the same time period. Importantly, our intervention was substantially more effective than existing, non-personalized interventions used by national public health organizations. We then scaled our intervention to a public website and replicated our findings in a nationally-representative sample (N=553). Overall, our novel psychological intervention changed immunity beliefs and motivated vaccination, of relevance for COVID-19, influenza, and future pandemic threats

localcovid19now: processing and mapping COVID-19 case data at subnational scales

The localcovid19now R package provides functionality to load, unify and visualize recent COVID-19 case data at subnational scales in order to provide localized situational reports and improve understanding of the scale of local COVID-19 transmission. The package loads data from a variety of data sources and returns the most recent estimate of recorded per capita active COVID-19 infections, the date of the most recent report, and the geometry of each region. These data can then be visualized via mapping documented per capita active infections. We also provide functionality to visualize the risk of exposure to COVID-19 given a particular event size

Light Scattering demonstrating effect of pH on NT<i>Hi</i> Nuc size.

<p>Light Scattering demonstrating effect of pH on NT<i>Hi</i> Nuc size.</p

CD spectra of NT<i>Hi</i> Nuc from 190–260 nm.

<p>This figure shows representative curves. When NT<i>Hi</i> Nuc was heated from 25°C (solid line) to 60°C (dotted line), the spectra was lost, indicating loss of secondary structure. When the sample was then cooled to 25°C (dashed line), NT<i>Hi</i> Nuc underwent reversible thermal folding. Samples were referenced to buffer.</p

Effect of inhibitor on NT<i>Hi</i> Nuc activity.

<p>NT<i>Hi</i> Nuc was incubated with 2 μM FRET substrate and increasing concentrations of inhibitor (pdTp). Enzyme activity was measured in FRET assay. n = 3 for 0.03nM of pdTp and n = 2 for 0.015nM of pdTp. Relative fluorescent units decreased as pdTp concentration increased, thus indicating that the inhibitor acts as a competitive inhibitor for NT<i>Hi</i> Nuc when low concentration of the substrate was present, as measured by FRET. Bars represent the average from three independent experiments for 0.03nM of pdTp and two independent experiments for 0.015nM of pdTp ± SD. p-values were determined using two-sided student’s t test with assumption of equal variance (*** p-value < 0.0005).</p

Effect of pH on thermal stability.

<p>Effect of pH on thermal stability.</p

Effect of EDTA on NT<i>Hi</i> Nuc activity.

<p>FRET assay shows single-stranded nuclease activity of NT<i>Hi</i> Nuc activity. NTHi Nuc requires divalent cation as it lost activity with addition of EDTA. The respective lines are 0.03 nM NT<i>Hi</i> Nuc alone (●), 0.03 nM NT<i>Hi</i> Nuc and 0.3nM EDTA (■), 0.3nM EDTA alone (▲) and distilled water (▼). The experiment was repeated three times.</p