Exploring Paleogene Tibet's warm temperate environments through target enrichment and phylogenetic niche modelling of Himalayan spiny frogs (Paini, Dicroglossidae).

The Cenozoic topographic development of the Himalaya-Tibet orogen (HTO) substantially affected the paleoenvironment and biodiversity patterns of High Asia. However, concepts on the evolution and paleoenvironmental history of the HTO differ massively in timing, elevational increase and sequence of surface uplift of the different elements of the orogen. Using target enrichment of a large set of transcriptome-derived markers, ancestral range estimation and paleoclimatic niche modelling, we assess a recently proposed concept of a warm temperate paleo-Tibet in Asian spiny frogs of the tribe Paini and reconstruct their historical biogeography. That concept was previously developed in invertebrates. Because of their early evolutionary origin, low dispersal capacity, high degree of local endemism, and strict dependence on temperature and humidity, the cladogenesis of spiny frogs may echo the evolution of the HTO paleoenvironment. We show that diversification of main lineages occurred during the early to Mid-Miocene, while the evolution of alpine taxa started during the late Miocene/early Pliocene. Our distribution and niche modelling results indicate range shifts and niche stability that may explain the modern disjunct distributions of spiny frogs. They probably maintained their (sub)tropical or (warm)temperate preferences and moved out of the ancestral paleo-Tibetan area into the Himalaya as the climate shifted, as opposed to adapting in situ. Based on ancestral range estimation, we assume the existence of low-elevation, climatically suitable corridors across paleo-Tibet during the Miocene along the Kunlun, Qiangtang and/or Gangdese Shan. Our results contribute to a deeper understanding of the mechanisms and processes of faunal evolution in the HTO.

A genome-wide association meta-analysis implicates Hedgehog and Notch signaling in Dupuytren's disease.

Dupuytren's disease (DD) is a highly heritable fibrotic disorder of the hand with incompletely understood etiology. A number of genetic loci, including Wnt signaling members, have been previously identified. Our overall aim was to identify novel genetic loci, to prioritize genes within the loci for functional studies, and to assess genetic correlation with associated disorders. We performed a meta-analysis of six DD genome-wide association studies from three European countries and extensive bioinformatic follow-up analyses. Leveraging 11,320 cases and 47,023 controls, we identified 85 genome-wide significant single nucleotide polymorphisms in 56 loci, of which 11 were novel, explaining 13.3-38.1% of disease variance. Gene prioritization implicated the Hedgehog and Notch signaling pathways. We also identified a significant genetic correlation with frozen shoulder. The pathways identified highlight the potential for new therapeutic targets and provide a basis for additional mechanistic studies for a common disorder that can severely impact hand function.

SMapper: visualizing spatial prevalence data of all types, including sparse and incomplete datasets.

Motivation: We introduce SMapper, a novel web and software tool for visualizing spatial prevalence data of all types including those suffering from incomplete geographic coverage and insufficient sample sizes. We demonstrate the benefits of our tool in overcoming interpretational issues with existing tools caused by such data limitations. We exemplify the use of SMapper by applications to human genotype and phenotype data relevant in an epidemiological, anthropological and forensic context. Availability and implementation: A web implementation is available at https://rhodos.ccg.uni-koeln.de/smapper/. A stand-alone version, released under the GNU General Public License version 3 as published by the Free Software Foundation, is available from https://rhodos.ccg.uni-koeln.de/smapper/software-download.php as a Singularity container (https://docs.sylabs.io/guides/latest/user-guide/index.html) and a native Linux Python installation.

CNV-ClinViewer: enhancing the clinical interpretation of large copy-number variants online.

MOTIVATION: Pathogenic copy-number variants (CNVs) can cause a heterogeneous spectrum of rare and severe disorders. However, most CNVs are benign and are part of natural variation in human genomes. CNV pathogenicity classification, genotype-phenotype analyses, and therapeutic target identification are challenging and time-consuming tasks that require the integration and analysis of information from multiple scattered sources by experts. RESULTS: Here, we introduce the CNV-ClinViewer, an open-source web application for clinical evaluation and visual exploration of CNVs. The application enables real-time interactive exploration of large CNV datasets in a user-friendly designed interface and facilitates semi-automated clinical CNV interpretation following the ACMG guidelines by integrating the ClassifCNV tool. In combination with clinical judgment, the application enables clinicians and researchers to formulate novel hypotheses and guide their decision-making process. Subsequently, the CNV-ClinViewer enhances for clinical investigators' patient care and for basic scientists' translational genomic research. AVAILABILITY AND IMPLEMENTATION: The web application is freely available at https://cnv-ClinViewer.broadinstitute.org and the open-source code can be found at https://github.com/LalResearchGroup/CNV-clinviewer.

Identification of de novo variants in nonsyndromic cleft lip with/without cleft palate patients with low polygenic risk scores.

BACKGROUND: Nonsyndromic cleft lip with/without cleft palate (nsCL/P) is a congenital malformation of multifactorial etiology. Research has identified >40 genome-wide significant risk loci, which explain less than 40% of nsCL/P heritability. Studies show that some of the hidden heritability is explained by rare penetrant variants. METHODS: To identify new candidate genes, we searched for highly penetrant de novo variants (DNVs) in 50 nsCL/P patient/parent-trios with a low polygenic risk for the phenotype (discovery). We prioritized DNV-carrying candidate genes from the discovery for resequencing in independent cohorts of 1010 nsCL/P patients of diverse ethnicities and 1574 population-matched controls (replication). Segregation analyses and rare variant association in the replication cohort, in combination with additional data (genome-wide association data, expression, protein-protein-interactions), were used for final prioritization. CONCLUSION: In the discovery step, 60 DNVs were identified in 60 genes, including a variant in the established nsCL/P risk gene CDH1. Re-sequencing of 32 prioritized genes led to the identification of 373 rare, likely pathogenic variants. Finally, MDN1 and PAXIP1 were prioritized as top candidates. Our findings demonstrate that DNV detection, including polygenic risk score analysis, is a powerful tool for identifying nsCL/P candidate genes, which can also be applied to other multifactorial congenital malformations.

Conserved patterns across ion channels correlate with variant pathogenicity and clinical phenotypes.

Clinically identified genetic variants in ion channels can be benign or cause disease by increasing or decreasing the protein function. As a consequence, therapeutic decision-making is challenging without molecular testing of each variant. Our biophysical knowledge of ion-channel structures and function is just emerging, and it is currently not well understood which amino acid residues cause disease when mutated. We sought to systematically identify biological properties associated with variant pathogenicity across all major voltage and ligand-gated ion-channel families. We collected and curated 3049 pathogenic variants from hundreds of neurodevelopmental and other disorders and 12 546 population variants for 30 ion channel or channel subunits for which a high-quality protein structure was available. Using a wide range of bioinformatics approaches, we computed 163 structural features and tested them for pathogenic variant enrichment. We developed a novel 3D spatial distance scoring approach that enables comparisons of pathogenic and population variant distribution across protein structures. We discovered and independently replicated that several pore residue properties and proximity to the pore axis were most significantly enriched for pathogenic variants compared to population variants. Using our 3D scoring approach, we showed that the strongest pathogenic variant enrichment was observed for pore-lining residues and alpha-helix residues within 5Å distance from the pore axis centre and not involved in gating. Within the subset of residues located at the pore, the hydrophobicity of the pore was the feature most strongly associated with variant pathogenicity. We also found an association between the identified properties and both clinical phenotypes and functional in vitro assays for voltage-gated sodium channels (SCN1A, SCN2A, SCN8A) and N-methyl-D-aspartate receptor (GRIN1, GRIN2A, GRIN2B) encoding genes. In an independent expert-curated dataset of 1422 neurodevelopmental disorder pathogenic patient variants and 679 electrophysiological experiments, we show that pore axis distance is associated with seizure age of onset and cognitive performance as well as differential gain versus loss-of-channel function. In summary, we identified biological properties associated with ion-channel malfunction and show that these are correlated with in vitro functional readouts and clinical phenotypes in patients with neurodevelopmental disorders. Our results suggest that clinical decision support algorithms that predict variant pathogenicity and function are feasible in the future.

Benchmarking of univariate pleiotropy detection methods applied to epilepsy.

Pleiotropy is a widespread phenomenon that may increase insight into the etiology of biological and disease traits. Since genome-wide association studies frequently provide information on a single trait only, only univariate pleiotropy detection methods are applicable, with yet unknown comparative performance. Here, we compared five such methods with respect to their ability to detect pleiotropy, including meta-analysis, ASSET, conditional false discovery rate (cFDR), cross-phenotype Bayes (CPBayes), and pleiotropic analysis under the composite null hypothesis (PLACO), by performing extended computer simulations that varied the underlying etiological model for pleiotropy for a pair of traits, including the number of causal variants, degree of traits' overlap, effect sizes as well as trait prevalence, and varying sample sizes. Our results indicate that ASSET provides the best trade-off between power and protection against false positives. We then applied ASSET to a previously published International League Against Epilepsy (ILAE) consortium data set on complex epilepsies, comprising genetic generalized epilepsy and focal epilepsy cases and corresponding controls. We identified a novel candidate locus at 17q21.32 and confirmed locus 2q24.3, previously identified to act pleiotropically on both epilepsy subtypes by a mega-analysis. Functional annotation, tissue-specific expression, and regulatory function analysis as well as Bayesian colocalization analysis corroborated this result, rendering 17q21.32 a worthwhile candidate for follow-up studies on pleiotropy in epilepsies.

Distinct gene-set burden patterns underlie common generalized and focal epilepsies.

BACKGROUND: Analyses of few gene-sets in epilepsy showed a potential to unravel key disease associations. We set out to investigate the burden of ultra-rare variants (URVs) in a comprehensive range of biologically informed gene-sets presumed to be implicated in epileptogenesis. METHODS: The burden of 12 URV types in 92 gene-sets was compared between cases and controls using whole exome sequencing data from individuals of European descent with developmental and epileptic encephalopathies (DEE, n = 1,003), genetic generalized epilepsy (GGE, n = 3,064), or non-acquired focal epilepsy (NAFE, n = 3,522), collected by the Epi25 Collaborative, compared to 3,962 ancestry-matched controls. FINDINGS: Missense URVs in highly constrained regions were enriched in neuron-specific and developmental genes, whereas genes not expressed in brain were not affected. GGE featured a higher burden in gene-sets derived from inhibitory vs. excitatory neurons or associated receptors, whereas the opposite was found for NAFE, and DEE featured a burden in both. Top-ranked susceptibility genes from recent genome-wide association studies (GWAS) and gene-sets derived from generalized vs. focal epilepsies revealed specific enrichment patterns of URVs in GGE vs. NAFE. INTERPRETATION: Missense URVs affecting highly constrained sites differentially impact genes expressed in inhibitory vs. excitatory pathways in generalized vs. focal epilepsies. The excess of URVs in top-ranked GWAS risk-genes suggests a convergence of rare deleterious and common risk-variants in the pathogenesis of generalized and focal epilepsies. FUNDING: DFG Research Unit FOR-2715 (Germany), FNR (Luxembourg), NHGRI (US), NHLBI (US), DAAD (Germany).

Development and Evaluation of the Ancestry Informative Marker Panel of the VISAGE Basic Tool.

We detail the development of the ancestry informative single nucleotide polymorphisms (SNPs) panel forming part of the VISAGE Basic Tool (BT), which combines 41 appearance predictive SNPs and 112 ancestry predictive SNPs (three SNPs shared between sets) in one massively parallel sequencing (MPS) multiplex, whereas blood-based age analysis using methylation markers is run in a parallel MPS analysis pipeline. The selection of SNPs for the BT ancestry panel focused on established forensic markers that already have a proven track record of good sequencing performance in MPS, and the overall SNP multiplex scale closely matched that of existing forensic MPS assays. SNPs were chosen to differentiate individuals from the five main continental population groups of Africa, Europe, East Asia, America, and Oceania, extended to include differentiation of individuals from South Asia. From analysis of 1000 Genomes and HGDP-CEPH samples from these six population groups, the BT ancestry panel was shown to have no classification error using the Bayes likelihood calculators of the Snipper online analysis portal. The differentiation power of the component ancestry SNPs of BT was balanced as far as possible to avoid bias in the estimation of co-ancestry proportions in individuals with admixed backgrounds. The balancing process led to very similar cumulative population-specific divergence values for Africa, Europe, America, and Oceania, with East Asia being slightly below average, and South Asia an outlier from the other groups. Comparisons were made of the African, European, and Native American estimated co-ancestry proportions in the six admixed 1000 Genomes populations, using the BT ancestry panel SNPs and 572,000 Affymetrix Human Origins array SNPs. Very similar co-ancestry proportions were observed down to a minimum value of 10%, below which, low-level co-ancestry was not always reliably detected by BT SNPs. The Snipper analysis portal provides a comprehensive population dataset for the BT ancestry panel SNPs, comprising a 520-sample standardised reference dataset; 3445 additional samples from 1000 Genomes, HGDP-CEPH, Simons Foundation and Estonian Biocentre genome diversity projects; and 167 samples of six populations from in-house genotyping of individuals from Middle East, North and East African regions complementing those of the sampling regimes of the other diversity projects.

Analysis of single nucleotide polymorphisms in chronic beryllium disease.

Sarcoidosis and chronic beryllium disease (CBD) are phenocopies, however the latter one has a clear trigger factor that is beryllium exposure. This study analyses single nucleotide polymorphisms (SNPs) in a large cohort for beryllium-exposed persons. SNPs were chosen for their relevance in sarcoidosis. Even though one of largest cohorts of beryllium-exposed persons was analysed, no statistically relevant association between any SNP and CBD could be verified. Notably, some SNPs exhibit inverse OR for beryllium sensitization and CBD with nominally statistical significance, which allows hypothesizing about pathophysiological role of genes for the disease triggering and development.

Evaluation of supervised machine-learning methods for predicting appearance traits from DNA.

The prediction of human externally visible characteristics (EVCs) based solely on DNA information has become an established approach in forensic and anthropological genetics in recent years. While for a large set of EVCs, predictive models have already been established using multinomial logistic regression (MLR), the prediction performances of other possible classification methods have not been thoroughly investigated thus far. Motivated by the question to identify a potential classifier that outperforms these specific trait models, we conducted a systematic comparison between the widely used MLR and three popular machine learning (ML) classifiers, namely support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), that have shown good performance outside EVC prediction. As examples, we used eye, hair and skin color categories as phenotypes and genotypes based on the previously established IrisPlex, HIrisPlex, and HIrisPlex-S DNA markers. We compared and assessed the performances of each of the four methods, complemented by detailed hyperparameter tuning that was applied to some of the methods in order to maximize their performance. Overall, we observed that all four classification methods showed rather similar performance, with no method being substantially superior to the others for any of the traits, although performances varied slightly across the different traits and more so across the trait categories. Hence, based on our findings, none of the ML methods applied here provide any advantage on appearance prediction, at least when it comes to the categorical pigmentation traits and the selected DNA markers used here.

Exome sequencing utility in defining the genetic landscape of hearing loss and novel-gene discovery in Iran.

Hearing loss (HL) is one of the most common sensory defects affecting more than 466 million individuals worldwide. It is clinically and genetically heterogeneous with over 120 genes causing non-syndromic HL identified to date. Here, we performed exome sequencing (ES) on a cohort of Iranian families with no disease-causing variants in known deafness-associated genes after screening with a targeted gene panel. We identified likely causal variants in 20 out of 71 families screened. Fifteen families segregated variants in known deafness-associated genes. Eight families segregated variants in novel candidate genes for HL: DBH, TOP3A, COX18, USP31, TCF19, SCP2, TENM1, and CARMIL1. In the three of these families, intrafamilial locus heterogeneity was observed with variants in both known and novel candidate genes. In aggregate, we were able to identify the underlying genetic cause of HL in nearly 30% of our study cohort using ES. This study corroborates the observation that high-throughput DNA sequencing in populations with high rates of consanguineous marriages represents a more appropriate strategy to elucidate the genetic etiology of heterogeneous conditions such as HL.

Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits.

The prediction of appearance traits by use of solely genetic information has become an established approach and a number of statistical prediction models have already been developed for this purpose. However, given limited knowledge on appearance genetics, currently available models are incomplete and do not include all causal genetic variants as predictors. Therefore such prediction models may benefit from the inclusion of additional information that acts as a proxy for this unknown genetic background. Use of priors, possibly informed by trait category prevalence values in biogeographic ancestry groups, in a Bayesian framework may thus improve the prediction accuracy of previously predicted externally visible characteristics, but has not been investigated as of yet. In this study, we assessed the impact of using trait prevalence-informed priors on the prediction performance in Bayesian models for eye, hair and skin color as well as hair structure and freckles in comparison to the respective prior-free models. Those prior-free models were either similarly defined either very close to the already established ones by using a reduced predictive marker set. However, these differences in the number of the predictive markers should not affect significantly our main outcomes. We observed that such priors often had a strong effect on the prediction performance, but to varying degrees between different traits and also different trait categories, with some categories barely showing an effect. While we found potential for improving the prediction accuracy of many of the appearance trait categories tested by using priors, our analyses also showed that misspecification of those prior values often severely diminished the accuracy compared to the respective prior-free approach. This emphasizes the importance of accurate specification of prevalence-informed priors in Bayesian prediction modeling of appearance traits. However, the existing literature knowledge on spatial prevalence is sparse for most appearance traits, including those investigated here. Due to the limitations in appearance trait prevalence knowledge, our results render the use of trait prevalence-informed priors in DNA-based appearance trait prediction currently infeasible.

A Y-chromosomal survey of Ecuador's multi-ethnic population reveals new insights into the tri-partite population structure and supports an early Holocene age of the rare Native American founder lineage C3-MPB373.

Ecuador is a multiethnic and pluricultural country with a complex history defined by migration and admixture processes. The present study aims to increase our knowledge on the Ecuadorian Native Amerindian groups and the unique South American Y-chromosome haplogroup C3-MPB373 through the analysis of up to 23 Y-chromosome STRs (Y-STRs) and several Y-SNPs in a sample of 527 Ecuadorians from 7 distinct populations and geographic areas, including Kichwa and non-Kichwa Native Amerindians, Mestizos and Afro-Ecuadorians. Our results reveal the presence of C3-MPB373 both in the Amazonian lowland Kichwa with frequencies up to 28 % and, for the first time, in notable proportions in Kichwa populations from the Ecuadorian highlands. The substantially higher frequencies of C3-MPB373 in the Amazonian lowlands found in Kichwa and Waorani individuals suggest a founder effect in that area. Notably, estimates for the time to the most recent common ancestor (TMRCA) in the range of 7.2-9.0 kya point to an ancient origin of the haplogroup and suggest an early Holocene expansion of C3-MPB373 into South America. Finally, the pairwise genetic distances (RST) separate the Kichwa Salasaka from all the other Native Amerindian and Ecuadorian groups, indicating a so far hidden diversity among the Kichwa-speaking populations and suggesting a more southern origin of this population. In sum, our study provides a more in-depth knowledge of the male genetic structure of the multiethnic Ecuadorian population, as well as a valuable reference dataset for forensic use.

The impact of correlations between pigmentation phenotypes and underlying genotypes on genetic prediction of pigmentation traits.

Predicting appearance phenotypes from genotypes is relevant for various areas of human genetic research and applications such as genetic epidemiology, human history, anthropology, and particularly in forensics. Many appearance phenotypes, and thus their underlying genotypes, are highly correlated, with pigmentation traits serving as primary examples. However, all available genetic prediction models, including those for pigmentation traits currently used in forensic DNA phenotyping, ignore phenotype correlations. Here, we investigated the impact of appearance phenotype correlations on genetic appearance prediction in the exemplary case of three pigmentation traits. We used data for categorical eye, hair and skin colour as well as 41 DNA markers utilized in the recently established HIrisPlex-S system from 762 individuals with complete phenotype and genotype information. Based on these data, we performed genetic prediction modelling of eye, hair and skin colour via three different strategies, namely the established approach of predicting phenotypes solely based on genotypes while not considering phenotype correlations, and two novel approaches that considered phenotype correlations, either incorporating truly observed correlated phenotypes or DNA-predicted correlated phenotypes in addition to the DNA predictors. We found that using truly observed correlated pigmentation phenotypes as additional predictors increased the DNA-based prediction accuracies for almost all eye, hair and skin colour categories, with the largest increase for intermediate eye colour, brown hair colour, dark to black skin colour, and particularly for dark skin colour. Outcomes of dedicated computer simulations suggest that this prediction accuracy increase is due to the additional genetic information that is implicitly provided by the truly observed correlated pigmentation phenotypes used, yet not covered by the DNA predictors applied. In contrast, considering DNA-predicted correlated pigmentation phenotypes as additional predictors did not improve the performance of the genetic prediction of eye, hair and skin colour, which was in line with the results from our computer simulations. Hence, in practical applications of DNA-based appearance prediction where no phenotype knowledge is available, such as in forensic DNA phenotyping, it is not advised to use DNA-predicted correlated phenotypes as predictors in addition to the DNA predictors. In the very least, this is not recommended for the pigmentation traits and the established pigmentation DNA predictors tested here.

Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits.

Cigarette smoking is the leading cause of preventable morbidity and mortality. Genetic variation contributes to initiation, regular smoking, nicotine dependence, and cessation. We present a Fagerström Test for Nicotine Dependence (FTND)-based genome-wide association study in 58,000 European or African ancestry smokers. We observe five genome-wide significant loci, including previously unreported loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416), and extend loci reported for other smoking traits to nicotine dependence. Using the heaviness of smoking index from UK Biobank (N = 33,791), rs2714700 is consistently associated; rs1862416 is not associated, likely reflecting nicotine dependence features not captured by the heaviness of smoking index. Both variants influence nearby gene expression (rs2714700/MAGI2-AS3 in hippocampus; rs1862416/TENM2 in lung), and expression of genes spanning nicotine dependence-associated variants is enriched in cerebellum. Nicotine dependence (SNP-based heritability = 8.6%) is genetically correlated with 18 other smoking traits (rg = 0.40-1.09) and co-morbidities. Our results highlight nicotine dependence-specific loci, emphasizing the FTND as a composite phenotype that expands genetic knowledge of smoking.

The exhaustive genomic scan approach, with an application to rare-variant association analysis.

Region-based genome-wide scans are usually performed by use of a priori chosen analysis regions. Such an approach will likely miss the region comprising the strongest signal and, thus, may result in increased type II error rates and decreased power. Here, we propose a genomic exhaustive scan approach that analyzes all possible subsequences and does not rely on a prior definition of the analysis regions. As a prime instance, we present a computationally ultraefficient implementation using the rare-variant collapsing test for phenotypic association, the genomic exhaustive collapsing scan (GECS). Our implementation allows for the identification of regions comprising the strongest signals in large, genome-wide rare-variant association studies while controlling the family-wise error rate via permutation. Application of GECS to two genomic data sets revealed several novel significantly associated regions for age-related macular degeneration and for schizophrenia. Our approach also offers a high potential to improve genome-wide scans for selection, methylation, and other analyses.