The influence of evolutionary history on human health and disease.

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.

Cell-programmed nutrient partitioning in the tumour microenvironment.

Cancer cells characteristically consume glucose through Warburg metabolism1, a process that forms the basis of tumour imaging by positron emission tomography (PET). Tumour-infiltrating immune cells also rely on glucose, and impaired immune cell metabolism in the tumour microenvironment (TME) contributes to immune evasion by tumour cells2-4. However, whether the metabolism of immune cells is dysregulated in the TME by cell-intrinsic programs or by competition with cancer cells for limited nutrients remains unclear. Here we used PET tracers to measure the access to and uptake of glucose and glutamine by specific cell subsets in the TME. Notably, myeloid cells had the greatest capacity to take up intratumoral glucose, followed by T cells and cancer cells, across a range of cancer models. By contrast, cancer cells showed the highest uptake of glutamine. This distinct nutrient partitioning was programmed in a cell-intrinsic manner through mTORC1 signalling and the expression of genes related to the metabolism of glucose and glutamine. Inhibiting glutamine uptake enhanced glucose uptake across tumour-resident cell types, showing that glutamine metabolism suppresses glucose uptake without glucose being a limiting factor in the TME. Thus, cell-intrinsic programs drive the preferential acquisition of glucose and glutamine by immune and cancer cells, respectively. Cell-selective partitioning of these nutrients could be exploited to develop therapies and imaging strategies to enhance or monitor the metabolic programs and activities of specific cell populations in the TME.

Mosaic patterns of selection in genomic regions associated with diverse human traits.

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.

GSEL: a fast, flexible python package for detecting signatures of diverse evolutionary forces on genomic regions.

SUMMARY: GSEL is a computational framework for calculating the enrichment of signatures of diverse evolutionary forces in a set of genomic regions. GSEL can flexibly integrate any sequence-based evolutionary metric and analyze sets of human genomic regions identified by genome-wide assays (e.g. GWAS, eQTL, *-seq). The core of GSEL's approach is the generation of empirical null distributions tailored to the allele frequency and linkage disequilibrium structure of the regions of interest. We illustrate the application of GSEL to variants identified from a GWAS of body mass index, a highly polygenic trait. AVAILABILITY AND IMPLEMENTATION: GSEL is implemented as a fast, flexible and user-friendly python package. It is available with demonstration data at https://github.com/abraham-abin13/gsel_vec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Leptin Augments Antitumor Immunity in Obesity by Repolarizing Tumor-Associated Macrophages.

Although obesity can promote cancer, it may also increase immunotherapy efficacy in what has been termed the obesity-immunotherapy paradox. Mechanisms of this effect are unclear, although obesity alters key inflammatory cytokines and can promote an inflammatory state that may modify tumor-infiltrating lymphocytes and tumor-associated macrophage populations. To identify mechanisms by which obesity affects antitumor immunity, we examined changes in cell populations and the role of the proinflammatory adipokine leptin in immunotherapy. Single-cell RNAseq demonstrated that obesity decreased tumor-infiltrating lymphocyte frequencies, and flow cytometry confirmed altered macrophage phenotypes with lower expression of inducible NO synthase and MHC class II in tumors of obese animals. When treated with anti-programmed cell death protein 1 (PD-1) Abs, however, obese mice had a greater absolute decrease in tumor burden than lean mice and a repolarization of the macrophages to inflammatory M1-like phenotypes. Mechanistically, leptin is a proinflammatory adipokine that is induced in obesity and may mediate enhanced antitumor immunity in obesity. To directly test the effect of leptin on tumor growth and antitumor immunity, we treated lean mice with leptin and observed tumors over time. Treatment with leptin, acute or chronic, was sufficient to enhance antitumor efficacy similar to anti-PD-1 checkpoint therapy. Further, leptin and anti-PD-1 cotreatment may enhance antitumor effects consistent with an increase in M1-like tumor-associated macrophage frequency compared with non-leptin-treated mice. These data demonstrate that obesity has dual effects in cancer through promotion of tumor growth while simultaneously enhancing antitumor immunity through leptin-mediated macrophage reprogramming.

Contraceptive exposure associates with urinary tract infection risk in a cohort of reproductive-age women: a case control study.

PURPOSE: Although non-barrier contraception is commonly prescribed, the risk of urinary tract infections (UTI) with contraceptive exposure is unclear. MATERIALS AND METHODS: Using data from Vanderbilt University Medical Centre's deidentified electronic health record (EHR), women ages 18-52 were randomly sampled and matched based on age and length of EHR. This case-control analysis tested for association between contraception exposure and outcome using UTI-positive (UTI+) as cases and upper respiratory infection+ (URI+) as controls. RESULTS: 24,563 UTI + cases (mean EHR: 64.2 months; mean age: 31.2 years) and 48,649 UTI-/URI + controls (mean EHR: 63.2 months; mean age: 31.9 years) were analysed. In the primary analysis, UTI risk was statistically significantly increased for the oral contraceptive pill (OCP; OR = 1.10 [95%CI = 1.02-1.11], p ≤ 0.05), intrauterine device (IUD; OR = 1.13 [95%CI = 1.04-1.23], p ≤ 0.05), etonogestrel implant (Nexplanon®; OR = 1.56 [95% CI = 1.24-1.96], p ≤ 0.05), and medroxyprogesterone acetate injectable (Depo-Provera®; OR = 2.16 [95%CI = 1.99-2.33], p ≤ 0.05) use compared to women not prescribed contraception. A secondary analysis that included any non-IUD contraception, which could serve as a proxy for sexual activity, demonstrated a small attenuation for the association between UTI and IUD (OR = 1.09 [95%CI = 0.98-1.21], p = 0.13). CONCLUSION: This study notes potential for a small increase in UTIs with contraceptive use. Prospective studies are required before this information is applied in clinical settings. CONDENSATION: Although non-barrier contraception is commonly prescribed, the risk of urinary tract infections (UTI) with contraceptive exposure is poorly understood. This large-cohort, case-control study notes potential for a small increase in UTIs with contraceptive use.

Prevalence of Hearing Loss in Type 2 Diabetes Mellitus and Its Association with Severity of Diabetic Neuropathy and Glycemic Control.

OBJECTIVES: 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. MATERIALS AND METHODS: 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). RESULTS: 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. CONCLUSION: 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.

Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth.

BACKGROUND: Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy. METHODS: Here, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth. RESULTS: We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system. CONCLUSIONS: By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.

Perception of Illness and Its Association with Treatment Willingness in Patients with Newly Diagnosed Nonalcoholic Fatty Liver Disease.

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.

Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.

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.

Observational study on the clinical profile and treatment outcome on long-term follow-up of COVID-19 associated mucormycosis.

MATERIALS AND METHODS: Patients diagnosed with COVID-19 associated mucormycosis were followed up for 6 months to study the clinical profile, readmissions, long-term treatment outcome and the mortality rate. RESULTS: Among 37 patients with COVID-19 associated mucormycosis, the mortality rate was 33.3 %, 42.9% and 100 % among patients with mild, moderate and severe COVID-19 infection. One month after discharge, among the 20 patients who survived, 10 (50 %) patients had worsening symptoms and required readmission. Nine patients required readmission for amphotericin and 1 patient was admitted for surgical intervention. On follow-up at 1 month, 30 % (6/20) patients became asymptomatic. However, at 3 months, 45 % (9/20) of the patients were asymptomatic. At 6 months of follow-up, 80 % (16/20) were asymptomatic. At 6 months, one each had residual abnormalities like visual loss in one eye, visual field deficit, change in voice and residual weakness of the limbs along with cranial nerve paresis. CONCLUSION: The follow-up study revealed that a significant number of patients required readmission within the first month, but most of the patients became asymptomatic by 6 months. The readmission rate was higher in patients who received a shorter duration of amphotericin.

Genetic effects on the timing of parturition and links to fetal birth weight.

The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n = 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed six associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n = 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal-fetal relationship between gestational duration and birth weight.

Complications of arthroscopic lysis and lavage in internal derangement of the temporomandibular joint - A single institutional experience with review of literature.

PURPOSE: To evaluate the complications of arthroscopic lysis and lavage with joint sweep (ALL) procedure in the management of disc derangement of the temporomandibular joint. METHODS: Patients with internal derangement of the TMJ who were treated by ALL in a tertiary institution from July 2018 to December 2021 were studied retrospectively. RESULTS: The study included 39 patients (males, n = 14; females, n = 25) and 50 joints. The complications observed in the study were classified into intra and post operative complications. Post operative complications such as pain (16%), swelling (6%), reduced mouth opening (22%) and neurological complications were the most commonly observed ones. Rare complications such as ipsilateral palatal swelling (6%), parapharyngeal swelling (4%), and post operative malocclusion (2%) were also observed. CONCLUSION: Although the complications of ALL are entirely unavoidable, their incidence can be reduced by strict adherence to standard techniques. Three-dimensional awareness and orientation of the dangerous angles and depth around the TMJ region is mandatory to reduce the complications.

Machine Learning Models to Predict 24 Hour Urinary Abnormalities for Kidney Stone Disease.

OBJECTIVE: To help guide empiric therapy for kidney stone disease, we sought to demonstrate the feasibility of predicting 24-hour urine abnormalities using machine learning methods. METHODS: We trained a machine learning model (XGBoost [XG]) to predict 24-hour urine abnormalities from electronic health record-derived data (n = 1314). The machine learning model was compared to a logistic regression model [LR]. Additionally, an ensemble (EN) model combining both XG and LR models was evaluated as well. Models predicted binary 24-hour urine values for volume, sodium, oxalate, calcium, uric acid, and citrate; as well as a multiclass prediction of pH. We evaluated performance using area under the receiver operating curve (AUC-ROC) and identified predictors for each model. RESULTS: The XG model was able to discriminate 24-hour urine abnormalities with fair performance, comparable to LR. The XG model most accurately predicted abnormalities of urine volume (accuracy = 98%, AUC-ROC = 0.59), uric acid (69%, 0.73) and elevated urine sodium (71%, 0.79). The LR model outperformed the XG model alone in prediction of abnormalities of urinary pH (AUC-ROC of 0.66 vs 0.57) and citrate (0.69 vs 0.64). The EN model most accurately predicted abnormalities of oxalate (accuracy = 65%, ROC-AUC = 0.70) and citrate (65%, 0.69) with overall similar predictive performance to either XG or LR alone. Body mass index, age, and gender were the three most important features for training the models for all outcomes. CONCLUSION: Urine chemistry prediction for kidney stone disease appears to be feasible with machine learning methods. Further optimization of the performance could facilitate dietary or pharmacologic prevention.

Machine Learning Prediction of Kidney Stone Composition Using Electronic Health Record-Derived Features.

Objectives: To assess the accuracy of machine learning models in predicting kidney stone composition using variables extracted from the electronic health record (EHR). Materials and Methods: We identified kidney stone patients (n = 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 vs 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. Results: For discriminating binary stone composition, XG outperformed LR with higher accuracy (91% vs 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 vs 0.56) and ROC-AUC (0.79 vs 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. Conclusion: 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.

Vascular alterations impede fragile tolerance to pregnancy in type 1 diabetes.

OBJECTIVE: To determine the impact of autoimmunity in the absence of glycemic alterations on pregnancy in type 1 diabetes (T1D). DESIGN: Because nonobese diabetic (NOD) mice experience autoimmunity before the onset of hyperglycemia, we studied pregnancy outcomes in prediabetic NOD mice using flow cytometry and enzyme-linked immunosorbent assays. Once we determined that adverse events in pregnancy occurred in euglycemic mice, we performed an exploratory study using electronic health records to better understand pregnancy complications in humans with T1D and normal hemoglobin A1c levels. SETTING: University Medical Center. PATIENT(S)/ANIMAL(S): Nonobese diabetic mice and electronic health records from Vanderbilt University Medical Center. INTERVENTION(S): Nonobese diabetic mice were administered 200 µg of an anti-interleukin 6 (IL-6) antibody every other day starting on day 5 of gestation. MAIN OUTCOME MEASURE(S): Changes in the number of abnormal and reabsorbed pups in NOD mice and odds of vascular complications in pregnancy in T1D in relation to A1c. RESULT(S): Prediabetic NOD mice had increased adverse pregnancy outcomes compared with nonautoimmune mice; blockade of IL-6, which was secreted by endothelial cells, decreased the number of reabsorbed and abnormal fetuses. Similarly, vascular complications were increased in pregnant patients with T1D across all A1c values. CONCLUSION(S): The vascular secretion of IL-6 drives adverse pregnancy outcomes in prediabetic NOD mice. Pregnant patients with T1D have increased vascular complications even with normal hemoglobin A1cs, indicating a potential effect of autoimmunity on the placental vasculature.

Intrauterine devices as an exposure risk for urinary tract infections: A scoping review.

BACKGROUND: The intrauterine device (IUD) as a potential source of uro-gynecologic infection has raised concern for decades. While a causal link between IUD and pelvic inflammatory disease has been refuted, the relationship between IUDs and urinary tract infections (UTIs) remains incompletely understood. METHODS: We used a PubMed, CINAHL, and Cochrane Library search strategy to identify studies evaluating UTI occurrence and microbial signatures among women exposed to IUD. We evaluated the question, "what is currently known about the IUD as an exposure risk for UTIs?" RESULTS: Nine studies met inclusion criteria and were summarized in this structured, scoping review. Studies to date have not reported a significant association between IUD exposue and UTI occurence. While all nine studies acknowledged the breadth of contraceptive methods, none evaluated the impact of different IUD types (i.e., copper vs. hormone-eluting) on UTI incidence. CONCLUSION: Small sample sizes and inconsistent UTI definitions limit the current literature. Future studies should rigorously define the UTI phenotype and evaluate the association of UTI with IUD exposure accounting for known covariates.

Evaluating human autosomal loci for sexually antagonistic viability selection in two large biobanks.

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.

Integrating structural and evolutionary data to interpret variation and pathogenicity in adapter protein complex 4.

Genetic variation in the membrane trafficking adapter protein complex 4 (AP-4) can result in pathogenic neurological phenotypes including microencephaly, spastic paraplegias, epilepsy, and other developmental defects. We lack molecular mechanisms responsible for impaired AP-4 function arising from genetic variation, because AP-4 remains poorly understood structurally. Here, we analyze patterns of AP-4 genetic evolution and conservation to identify regions that are likely important for function and thus more susceptible to pathogenic variation. We map known variants onto an AP-4 homology model and predict the likelihood of pathogenic variation at a given location on the structure of AP-4. We find significant clustering of likely pathogenic variants located at the interface between the ß4 and N-µ4 subunits, as well as throughout the C-µ4 subunit. Our work offers an integrated perspective on how genetic and evolutionary forces affect AP-4 structure and function. As more individuals with uncharacterized AP-4 variants are identified, our work provides a foundation upon which their functional effects and disease relevance can be interpreted.

Accounting for diverse evolutionary forces reveals mosaic patterns of selection on human preterm birth loci.

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.