Clinical exome sequencing is nondiagnostic for about 75% of patients evaluated for a possible Mendelian disorder. We examined the ability of systematic reevaluation of exome data to establish ...additional diagnoses.
The exome and phenotypic data of 40 individuals with previously nondiagnostic clinical exomes were reanalyzed with current software and literature.
A definitive diagnosis was identified for 4 of 40 participants (10%). In these cases the causative variant is de novo and in a relevant autosomal-dominant disease gene. The literature to tie the causative genes to the participants’ phenotypes was weak, nonexistent, or not readily located at the time of the initial clinical exome reports. At the time of diagnosis by reanalysis, the supporting literature was 1 to 3 years old.
Approximately 250 gene–disease and 9,200 variant–disease associations are reported annually. This increase in information necessitates regular reevaluation of nondiagnostic exomes. To be practical, systematic reanalysis requires further automation and more up-to-date variant databases. To maximize the diagnostic yield of exome sequencing, providers should periodically request reanalysis of nondiagnostic exomes. Accordingly, policies regarding reanalysis should be weighed in combination with factors such as cost and turnaround time when selecting a clinical exome laboratory.
Variant pathogenicity classifiers such as SIFT, PolyPhen-2, CADD, and MetaLR assist in interpretation of the hundreds of rare, missense variants in the typical patient genome by deprioritizing some ...variants as likely benign. These widely used methods misclassify 26 to 38% of known pathogenic mutations, which could lead to missed diagnoses if the classifiers are trusted as definitive in a clinical setting. We developed M-CAP, a clinical pathogenicity classifier that outperforms existing methods at all thresholds and correctly dismisses 60% of rare, missense variants of uncertain significance in a typical genome at 95% sensitivity.
Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics ...approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.
Microbiota regulate intestinal physiology by modifying host gene expression along the length of the intestine, but the underlying regulatory mechanisms remain unresolved. Transcriptional specificity ...occurs through interactions between transcription factors (TFs) and cis-regulatory regions (CRRs) characterized by nucleosome-depleted accessible chromatin. We profiled transcriptome and accessible chromatin landscapes in intestinal epithelial cells (IECs) from mice reared in the presence or absence of microbiota. We show that regional differences in gene transcription along the intestinal tract were accompanied by major alterations in chromatin accessibility. Surprisingly, we discovered that microbiota modify host gene transcription in IECs without significantly impacting the accessible chromatin landscape. Instead, microbiota regulation of host gene transcription might be achieved by differential expression of specific TFs and enrichment of their binding sites in nucleosome-depleted CRRs near target genes. Our results suggest that the chromatin landscape in IECs is preprogrammed by the host in a region-specific manner to permit responses to microbiota through binding of open CRRs by specific TFs.
Despite strides in characterizing human history from genetic polymorphism data, progress in identifying genetic signatures of recent demography has been limited. Here we identify very recent ...fine-scale population structure in North America from a network of over 500 million genetic (identity-by-descent, IBD) connections among 770,000 genotyped individuals of US origin. We detect densely connected clusters within the network and annotate these clusters using a database of over 20 million genealogical records. Recent population patterns captured by IBD clustering include immigrants such as Scandinavians and French Canadians; groups with continental admixture such as Puerto Ricans; settlers such as the Amish and Appalachians who experienced geographic or cultural isolation; and broad historical trends, including reduced north-south gene flow. Our results yield a detailed historical portrait of North America after European settlement and support substantial genetic heterogeneity in the United States beyond that uncovered by previous studies.
There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer ...diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.
Although many human diseases have a genetic component involving many loci, the majority of studies are statistically underpowered to isolate the many contributing variants, raising the question of ...the existence of alternate processes to identify disease mutations. To address this question, we collect ancestral transcription factor binding sites disrupted by an individual's variants and then look for their most significant congregation next to a group of functionally related genes. Strikingly, when the method is applied to five different full human genomes, the top enriched function for each is invariably reflective of their very different medical histories. For example, our method implicates "abnormal cardiac output" for a patient with a longstanding family history of heart disease, "decreased circulating sodium level" for an individual with hypertension, and other biologically appealing links for medical histories spanning narcolepsy to axonal neuropathy. Our results suggest that erosion of gene regulation by mutation load significantly contributes to observed heritable phenotypes that manifest in the medical history. The test we developed exposes a hitherto hidden layer of personal variants that promise to shed new light on human disease penetrance, expressivity and the sensitivity with which we can detect them.
ObjectivesThe enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving ...pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data.DesignA cross-sectional study.SettingAncestryDNA customers in the USA who consented to research.ParticipantsThe AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test.ResultsWe replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study.ConclusionsThe results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.
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