SNVstory: inferring genetic ancestry from genome sequencing data.

BACKGROUND: Genetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages, based on ancestral disease predispositions. Nevertheless, the current reliance on self-reported ancestry can introduce subjectivity and exacerbate health disparities. While genomic sequencing data enables objective determination of a patient's genetic ancestry, existing approaches are limited to ancestry inference at the continental level. RESULTS: To address this challenge, and create an objective, measurable metric of genetic ancestry we present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. We also introduce a novel method for simulating individual samples from aggregate allele frequencies from known populations. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We successfully evaluated SNVstory using a clinical exome sequencing dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry, and demonstrate the capability of the algorithm to estimate ancestry from 36 different populations with high accuracy. CONCLUSIONS: SNVstory represents a significant advance in methods to assign genetic ancestry, opening the door to ancestry-informed care. SNVstory, an open-source model, is packaged as a Docker container for enhanced reliability and interoperability. It can be accessed from https://github.com/nch-igm/snvstory .

Exome sequencing in multiplex families with left-sided cardiac defects has high yield for disease gene discovery.

Congenital heart disease (CHD) is a common group of birth defects with a strong genetic contribution to their etiology, but historically the diagnostic yield from exome studies of isolated CHD has been low. Pleiotropy, variable expressivity, and the difficulty of accurately phenotyping newborns contribute to this problem. We hypothesized that performing exome sequencing on selected individuals in families with multiple members affected by left-sided CHD, then filtering variants by population frequency, in silico predictive algorithms, and phenotypic annotations from publicly available databases would increase this yield and generate a list of candidate disease-causing variants that would show a high validation rate. In eight of the nineteen families in our study (42%), we established a well-known gene/phenotype link for a candidate variant or performed confirmation of a candidate variant's effect on protein function, including variants in genes not previously described or firmly established as disease genes in the body of CHD literature: BMP10, CASZ1, ROCK1 and SMYD1. Two plausible variants in different genes were found to segregate in the same family in two instances suggesting oligogenic inheritance. These results highlight the need for functional validation and demonstrate that in the era of next-generation sequencing, multiplex families with isolated CHD can still bring high yield to the discovery of novel disease genes.

Synonymous variants that disrupt messenger RNA structure are significantly constrained in the human population.

BACKGROUND: The role of synonymous single-nucleotide variants in human health and disease is poorly understood, yet evidence suggests that this class of "silent" genetic variation plays multiple regulatory roles in both transcription and translation. One mechanism by which synonymous codons direct and modulate the translational process is through alteration of the elaborate structure formed by single-stranded mRNA molecules. While tools to computationally predict the effect of non-synonymous variants on protein structure are plentiful, analogous tools to systematically assess how synonymous variants might disrupt mRNA structure are lacking. RESULTS: We developed novel software using a parallel processing framework for large-scale generation of secondary RNA structures and folding statistics for the transcriptome of any species. Focusing our analysis on the human transcriptome, we calculated 5 billion RNA-folding statistics for 469 million single-nucleotide variants in 45,800 transcripts. By considering the impact of all possible synonymous variants globally, we discover that synonymous variants predicted to disrupt mRNA structure have significantly lower rates of incidence in the human population. CONCLUSIONS: These findings support the hypothesis that synonymous variants may play a role in genetic disorders due to their effects on mRNA structure. To evaluate the potential pathogenic impact of synonymous variants, we provide RNA stability, edge distance, and diversity metrics for every nucleotide in the human transcriptome and introduce a "Structural Predictivity Index" (SPI) to quantify structural constraint operating on any synonymous variant. Because no single RNA-folding metric can capture the diversity of mechanisms by which a variant could alter secondary mRNA structure, we generated a SUmmarized RNA Folding (SURF) metric to provide a single measurement to predict the impact of secondary structure altering variants in human genetic studies.

Mathematical analysis of a model for glucose regulation.

Diabetes affects millions of Americans, and the correct identification of individuals afflicted with this disease, especially of those in early stages or in progression towards diabetes, remains an active area of research. The minimal model is a simplified mathematical construct for understanding glucose-insulin interactions. Developed by Bergman, Cobelli, and colleagues over three decades ago, this system of coupled ordinary differential equations prevails as an important tool for interpreting data collected during an intravenous glucose tolerance test (IVGTT). In this study we present an explicit solution to the minimal model which allows for separating the glucose and insulin dynamics of the minimal model and for identifying patient-specific parameters of glucose trajectories from IVGTT. As illustrated with patient data, our approach seems to have an edge over more complicated methods currently used. Additionally, we also present an application of our method to prediction of the time to baseline recovery and calculation of insulin sensitivity and glucose effectiveness, two quantities regarded as significant in diabetes diagnostics.