Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before ...the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual's DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.
Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly ...understood. To address this, we developed an efficient computational framework to calculate positive and negative enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which included reproductive traits. Many traits also exhibited substantial positive enrichment for population differentiation, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for signatures of balancing selection (51% of GWASs) and absence of enrichment for evolutionary signals in regions associated with late-onset Alzheimer's disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate that diverse modes of evolution are likely to have shaped trait-associated loci. This atlas of evolutionary signatures across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and evolutionary processes in the human genome.
This study assessed the prevalence of hearing loss (HL) in patients with type 2 diabetes mellitus (T2DM) and its relationship with the presence and severity of diabetic neuropathy.
Patients between ...the ages of 30 and 60 years (both ages inclusive) with T2DM were recruited and divided into three groups. Group I included patients without neuropathy. Group II had patients with mild neuropathy. Group III had patients with moderate and severe neuropathy. After informed consent hearing threshold was assessed using pure tone audiometry (PTA).
Of the 200 patients recruited, the prevalence of HL was overall 81%. The prevalence was 66.7% in group I, 80.9% in group II, and 87.6% in group III (p = 0.009). Among patients with moderate to severe neuropathy (group III), 33.3% had clinically significant HL (CSHL) (p = 0.015). Age, gender, presence of neuropathy, and severity of neuropathy were associated with an increased risk of developing HL.
Among patients with diabetes, age, nephropathy, and neuropathy were associated with HL. The severity of HL worsened with the worsening severity of neuropathy and increase in glycated hemoglobin (Hba1c) levels. Patients with moderate to severe neuropathy might benefit from screening for HL.
Currently, there is no comprehensive framework to evaluate the evolutionary forces acting on genomic regions associated with human complex traits and contextualize the relationship between evolution ...and molecular function. Here, we develop an approach to test for signatures of diverse evolutionary forces on trait-associated genomic regions. We apply our method to regions associated with spontaneous preterm birth (sPTB), a complex disorder of global health concern. We find that sPTB-associated regions harbor diverse evolutionary signatures including conservation, excess population differentiation, accelerated evolution, and balanced polymorphism. Furthermore, we integrate evolutionary context with molecular evidence to hypothesize how these regions contribute to sPTB risk. Finally, we observe enrichment in signatures of diverse evolutionary forces in sPTB-associated regions compared to genomic background. By quantifying multiple evolutionary forces acting on sPTB-associated regions, our approach improves understanding of both functional roles and the mosaic of evolutionary forces acting on loci. Our work provides a blueprint for investigating evolutionary pressures on complex traits.
To assess the accuracy of machine learning models in predicting kidney stone composition using variables extracted from the electronic health record (EHR).
We identified kidney stone patients (
= ...1296) with both stone composition and 24-hour (24H) urine testing. We trained machine learning models (XGBoost XG and logistic regression LR) to predict stone composition using 24H urine data and EHR-derived demographic and comorbidity data. Models predicted either binary (calcium
noncalcium stone) or multiclass (calcium oxalate, uric acid, hydroxyapatite, or other) stone types. We evaluated performance using area under the receiver operating curve (ROC-AUC) and accuracy and identified predictors for each task.
For discriminating binary stone composition, XG outperformed LR with higher accuracy (91%
71%) with ROC-AUC of 0.80 for both models. Top predictors used by these models were supersaturations of uric acid and calcium phosphate, and urinary ammonium. For multiclass classification, LR outperformed XG with higher accuracy (0.64
0.56) and ROC-AUC (0.79
0.59), and urine pH had the highest predictive utility. Overall, 24H urine analyte data contributed more to the models' predictions of stone composition than EHR-derived variables.
Machine learning models can predict calcium stone composition. LR outperforms XG in multiclass stone classification. Demographic and comorbidity data are predictive of stone composition; however, including 24H urine data improves performance. Further optimization of performance could lead to earlier directed medical therapy for kidney stone patients.
Sex and sexual differentiation are pervasive across the tree of life. Because females and males often have substantially different functional requirements, we expect selection to differ between the ...sexes. Recent studies in diverse species, including humans, suggest that sexually antagonistic viability selection creates allele frequency differences between the sexes at many different loci. However, theory and population-level simulations indicate that sex-specific differences in viability would need to be very large to produce and maintain reported levels of between-sex allelic differentiation. We address this contradiction between theoretical predictions and empirical observations by evaluating evidence for sexually antagonistic viability selection on autosomal loci in humans using the largest cohort to date (UK Biobank, n = 487,999) along with a second large, independent cohort (BioVU, n = 93,864). We performed association tests between genetically ascertained sex and autosomal loci. Although we found dozens of genome-wide significant associations, none replicated across cohorts. Moreover, closer inspection revealed that all associations are likely due to cross-hybridization with sex chromosome regions during genotyping. We report loci with potential for mis-hybridization found on commonly used genotyping platforms that should be carefully considered in future genetic studies of sex-specific differences. Despite being well powered to detect allele frequency differences of up to 0.8% between the sexes, we do not detect clear evidence for this signature of sexually antagonistic viability selection on autosomal variation. These findings suggest a lack of strong ongoing sexually antagonistic viability selection acting on single locus autosomal variation in humans.
Background
Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease of immense public health relevance. Understanding illness perceptions in the NAFLD population will provide sound ...scientific evidence for planning high-quality patient-centered care and implementing effective interventions. The Brief Illness Perception Questionnaire (BIPQ) is a robust psychometric tool to systematically assess the dimensions of illness perceptions in various chronic ailments.
Methods
In a cross-sectional study enrolling patients with newly diagnosed NAFLD, the sociodemographic, anthropometric, biochemical, and radiological determinants of enhanced illness perceptions (measured by the BIPQ score) were investigated using univariate and multivariable binary logistic regression analyses. Finally, the association between individual domains of the BIPQ and willingness to participate in comprehensive medical management was explored.
Results
In total, 264 patients (mean age 53 ± 11.9 years, 59.8% males) were enrolled in the final analysis. The mean and median BIPQ scores in the study population were 30.3 ± 12.8 and 31.0 (IQR, 22.0–40.0), respectively. The variables having a significant independent association with heightened perceptions (BIPQ > 31) were family history of liver disease (aOR, 5.93; 95% CI, 1.42–24.74), obesity (aOR, 3.33; 95% CI, 1.57–7.05), diabetes mellitus (aOR, 2.35; 95% CI, 1.01–5.49), and transaminitis (aOR, 2.85; 95% CI, 1.42–5.69). Patients with a higher level of illness perceptions (31.6 ± 12.9 vs 27.8 ± 12.3,
p
= 0.022) were more likely to express a willingness to participate in the comprehensive management plan, with 3 of the 8 domains (consequence, identity, and treatment control) mainly affecting willingness.
Conclusion
A family history of liver disease, obesity, diabetes, and transaminitis were independently associated with increased illness perceptions. A belief in serious consequences, a strong illness identity, and higher perceived treatment control were significantly associated with the willingness to undergo comprehensive care for NAFLD.
Duckweed species (Lemnaceae) are suitable for remediation and valorization of agri‐feed industry wastewaters and therefore can contribute to a more sustainable, circular economy where waste is a ...resource. Industrial applications will, however, require space efficient cultivation methods that are not affected by prevailing weather conditions. Here, the development and operation of a multi‐tiered duckweed bioreactor is described. The developed prototype bioreactor depicted in this paper is composed of four cultivation layers (1 m2 each) with integrated LED lighting (generating up to 150 μmol m−2 s−1), a system of pumps and valves to manage the recirculatory flow (2.5 L min−1) of wastewater, and an automatic harvesting system. Using a nutrient poor medium, good growth of the duckweed species Lemna minor was achieved in the bioreactor, and this was matched by strong nutrient depletion from the medium, especially for phosphorus (45‐mg total phosphorus TP removed per m−2 day−1). A fully automatic harvesting arm reliably captured similar amounts of duckweed biomass across multiple harvesting cycles, revealing a future scenario whereby labor and interventions by human operators are minimized. Further developments to advance the system towards fully automated operation will include, for example, the use of specific nutrient sensors to monitor and control medium composition. It is envisaged that multi‐tiered, indoor bioreactors can be employed in the agri‐feed industry where wastewaters are, in many cases, continuously generated throughout the year and need remediating immediately to avoid costly storage. Given the extensive use of automation technology in conventional wastewater treatment plants, multi‐tiered duckweed bioreactors can be realistically integrated within the operating environment of such treatment plants.
Practitioner Points
Duckweed is suitable for remediation and valorization of agri‐feed wastewater.
Industrial duckweed applications require space efficient cultivation methods.
Development and operation of a multi‐tiered duckweed bioreactor is detailed.
Flow dynamics and automatic harvesting in the bioreactor are optimized.
It is concluded that a multi‐tiered bioreactor can be used in industry.
Duckweed species (Lemnaceae) are suitable for remediation and valorization of wastewaters. However, industrial duckweed applications require space efficient cultivation methods. Here, development and operation of an indoor, multi‐tiered duckweed bioreactor is described. It is envisaged that multi‐tiered bioreactors can be integrated in wastewater treatment systems of agri‐feed industries.
Non-protein-coding genetic variants are a major driver of the genetic risk for human disease; however, identifying which non-coding variants contribute to diseases and their mechanisms remains ...challenging. In silico variant prioritization methods quantify a variant’s severity, but for most methods, the specific phenotype and disease context of the prediction remain poorly defined. For example, many commonly used methods provide a single, organism-wide score for each variant, while other methods summarize a variant’s impact in certain tissues and/or cell types. Here, we propose a complementary disease-specific variant prioritization scheme, which is motivated by the observation that variants contributing to disease often operate through specific biological mechanisms. We combine tissue/cell-type-specific variant scores (e.g., GenoSkyline, FitCons2, DNA accessibility) into disease-specific scores with a logistic regression approach and apply it to ∼25,000 non-coding variants spanning 111 diseases. We show that this disease-specific aggregation significantly improves the association of common non-coding genetic variants with disease (average precision: 0.151, baseline = 0.09), compared with organism-wide scores (GenoCanyon, LINSIGHT, GWAVA, Eigen, CADD; average precision: 0.129, baseline = 0.09). Further on, disease similarities based on data-driven aggregation weights highlight meaningful disease groups, and it provides information about tissues and cell types that drive these similarities. We also show that so-learned similarities are complementary to genetic similarities as quantified by genetic correlation. Overall, our approach demonstrates the strengths of disease-specific variant prioritization, leads to improvement in non-coding variant prioritization, and enables interpretable models that link variants to disease via specific tissues and/or cell types.
Non-coding genetic variants constitute the majority of disease-associated genetic variation in humans. In this study, Liang et al. show that variant prioritization within a specific disease context improves performance and that it enables the linking of variants to disease via specific tissues and cell types.
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