All Publications

  • Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation. eLife Smith, C. J., Sinnott-Armstrong, N., Cichonska, A., Julkunen, H., Fauman, E. B., Wurtz, P., Pritchard, J. K. 2022; 11


    Pleiotropy and genetic correlation are widespread features in GWAS, but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.

    View details for DOI 10.7554/eLife.79348

    View details for PubMedID 36073519

  • Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits. American journal of human genetics Patel, R. A., Musharoff, S. A., Spence, J. P., Pimentel, H., Tcheandjieu, C., Mostafavi, H., Sinnott-Armstrong, N., Clarke, S. L., Smith, C. J., V.A. Million Veteran Program,,, Durda, P. P., Taylor, K. D., Tracy, R., Liu, Y., Johnson, W. C., Aguet, F., Ardlie, K. G., Gabriel, S., Smith, J., Nickerson, D. A., Rich, S. S., Rotter, J. I., Tsao, P. S., Assimes, T. L., Pritchard, J. K. 2022


    Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

    View details for DOI 10.1016/j.ajhg.2022.05.014

    View details for PubMedID 35716666

  • Short-Term Dairy Product Elimination and Reintroduction Minimally Perturbs the Gut Microbiota in Self-Reported Lactose-Intolerant Adults. mBio Smith, C. J., Dethlefsen, L., Gardner, C., Nguyen, L., Feldman, M., Costello, E. K., Kolodny, O., Relman, D. A. 2022: e0105122


    An outstanding question regarding the human gut microbiota is whether and how microbiota-directed interventions influence host phenotypic traits. Here, we employed a dietary intervention to probe this question in the context of lactose intolerance. To assess the effects of dietary dairy product elimination and (re)introduction on the microbiota and host phenotype, we studied 12 self-reported mildly lactose-intolerant adults with triweekly collection of fecal samples over a 12-week study period: 2weeks of baseline diet, 4weeks of dairy product elimination, and 6weeks of gradual whole cow milk (re)introduction. Of the 12 subjects, 6 reported either no dairy or only lactose-free dairy product consumption. A clinical assay for lactose intolerance, the hydrogen breath test, was performed before and after each of these three study phases, and 16S rRNA gene amplicon sequencing was performed on all fecal samples. We found that none of the subjects showed change in a clinically defined measure of lactose tolerance. Similarly, fecal microbiota structure resisted modification. Although the mean fraction of the genus Bifidobacterium, a group known to metabolize lactose, increased slightly with milk (re)introduction (from 0.0125 to 0.0206; Wilcoxon P=0.068), the overall structure of each subject's gut microbiota remained highly individualized and largely stable in the face of diet manipulation. IMPORTANCE Lactose intolerance is a gastrointestinal disorder diagnosed with a lactose hydrogen breath test. Lifestyle changes such as diet interventions can impact the gut microbiome; however, the role of the microbiome in lactose intolerance is unclear. Our study assessed the effects of a 12-week dietary dairy product elimination and (re)introduction on the microbiome and clinical lactose intolerance status in 12 adult self-reported lactose-intolerant individuals. We found each subject's gut microbiome remained highly individualized and largely stable in the face of this diet manipulation. We also report that none of the subjects showed change in a clinically defined measure of lactose tolerance.

    View details for DOI 10.1128/mbio.01051-22

    View details for PubMedID 35695459