Human Genetic Research Articles

A brief clinical genetics review: stepwise diagnostic processes of a monogenic disorder-hypertriglyceridemia.

The completion of the Human Genome Project and tremendous advances in automated high-throughput genetic analysis technologies have enabled explosive progress in the field of genetics, which resulted in countless discoveries of novel genes and pathways. Many phenotype- or disease-associated single nucleotide polymorphisms (SNPs) with a high statistical significance have been identified through numerous genome-wide association studies (GWAS), and various polygenic risk scoring (PRS) schemes have been proposed to identify individuals with a high risk for a certain trait or disorder. Meanwhile, medical education in genetics has lagged far behind, leaving many physicians and healthcare providers unprepared in the genomic era. Thus, there is an urgent need to educate physicians and healthcare providers with basic knowledge and skills in genetics. To facilitate this, some basic terminologies and concepts are discussed in this review. In addition, some important considerations in delineating and incorporating clinical genetic testing in the diagnosis and management of a monogenic disorder are illustrated in a stepwise fashion. Furthermore, the effects of disease-associated SNPs represented by a PRS scheme clearly demonstrated that even the phenotypes of a monogenic disorder due to the same pathogenic variant in family members are modulated by the polygenic background. In human genetics, despite these explosive advancements, we are still far from clearly deciphering the interplay of gene variants to effect unique characteristics in an individual. In addition, sophisticated genome or gene directed therapies are being investigated for numerous disorders. Therefore, evolution in the field of genetics is likely to continue into the foreseeable future. In the meantime, much emphasis should be placed on educating physicians and healthcare professionals to be well-versed and skillful in the clinical use of genetics so that they can fully embrace the new era of precision medicine.

Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags.

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n= 227,469UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3, APOA5, and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies.

Unlocking the genetic influence on milk variation and its potential implication for infant health

Human milk has long been recognized for its critical role in infant and maternal health. In this issue of Cell Genomics, Johnson etal.1 apply a human genetics and genomics approach to shed light on the complex relationship between maternal genetics, milk variation, and the infant gut microbiome.

The GENESIS database and tools: A decade of discovery in Mendelian genomics

In the past decade, human genetics research saw an acceleration of disease gene discovery and further dissection of the genetic architectures of many disorders. Much of this progress was enabled via data aggregation projects, collaborative data sharing among researchers, and the adoption of sophisticated and standardized bioinformatics analyses pipelines. In 2012, we launched the GENESIS platform, formerly known as GEM.app, with the aims to 1) empower clinical and basic researchers without bioinformatics expertise to analyze and explore genome level data and 2) facilitate the detection of novel pathogenic variation and novel disease genes by leveraging data aggregation and genetic matchmaking. The GENESIS database has grown to over 20,000 datasets from rare disease patients, which were provided by multiple academic research consortia and many individual investigators. Some of the largest global collections of genome-level data are available for Charcot-Marie-Tooth disease, hereditary spastic paraplegia, and cerebellar ataxia. A number of rare disease consortia and networks are archiving their data in this database. Over the past decade, more than 1500 scientists have registered and used this resource and published over 200 papers on gene and variant identifications, which garnered >6000 citations. GENESIS has supported >100 gene discoveries and contributed to approximately half of all gene identifications in the fields of inherited peripheral neuropathies and spastic paraplegia in this time frame. Many diagnostic odysseys of rare disease patients have been resolved. The concept of genomes-to-therapy has borne out for a number of such discoveries that let to rapid clinical trials and expedited natural history studies. This marks GENESIS as one of the most impactful data aggregation initiatives in rare monogenic diseases.

Valid inference for machine learning-assisted genome-wide association studies.

Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS.

An Analysis of the Effects of Space Conditions on Human Physiology, Psychology, and Genetics, and their Implications for Medicine on Earth

As of November 2023, 676 people have journeyed into space. As this number continues to rise every year, with plans for further, long-term space exploration extending even into the 2030's, it becomes increasingly important to form a comprehensive analysis of the challenges posed by harsh space conditions on human physiology, genetics, and general systems. Understanding these factors can in turn help reinvent medical techniques on Earth, such as the use of pressure adjustable suits to combat extreme acceleration and change in gravitational force for people suffering from postpartum haemorrhages. This paper delves into two main themes: firstly, the impacts of conditions like micro-gravity, galactic cosmic rays, and isolation on systems such as telomere length, neuro-ocular, and cardiology, and secondly, how these difficulties can be used to advance treatment for illnesses on Earth. This is essential to lay out a framework and area of reference for future space missions and possible developments in medicine - both in space and on Earth.

Identification and characterization of human GDF15 knockouts.

Growth differentiation factor 15 (GDF15) is a secreted protein that regulates food intake, body weight and stress responses in pre-clinical models1. The physiological function of GDF15 in humans remains unclear. Pharmacologically, GDF15 agonism in humans causes nausea without accompanying weight loss2, and GDF15 antagonism is being tested in clinical trials to treat cachexia and anorexia. Human genetics point to a role for GDF15 in hyperemesis gravidarum, but the safety or impact of complete GDF15 loss, particularly during pregnancy, is unknown3-7. Here we show the absence of an overt phenotype in human GDF15 loss-of-function carriers, including stop gains, frameshifts and the fully inactivating missense variant C211G3. These individuals were identified from 75,018 whole-exome/genome-sequenced participants in the Pakistan Genomic Resource8,9 and recall-by-genotype studies with family-based recruitment of variant carrier probands. We describe 8 homozygous ('knockouts') and 227 heterozygous carriers of loss-of-function alleles, including C211G. GDF15 knockouts range in age from 31 to 75 years, are fertile, have multiple children and show no consistent overt phenotypes, including metabolic dysfunction. Our data support the hypothesis that GDF15 is not required for fertility, healthy pregnancy, foetal development or survival into adulthood. These observations support the safety of therapeutics that block GDF15.

Variance quantitative trait loci reveal gene-gene interactions which alter blood traits.

Gene-gene (GxG) interactions play an important role in human genetics, potentially explaining part of the "missing heritability" of polygenic traits and the variable expressivity of monogenic traits. Many GxG interactions have been identified in model organisms through experimental breeding studies, but they have been difficult to identify in human populations. To address this challenge, we applied two complementary variance QTL (vQTL)-based approaches to identify GxG interactions that contribute to human blood traits and blood-related disease risk. First, we used the previously validated genome-wide scale test for each trait in ∼450,000 people in the UK Biobank and identified 4 vQTLs. Genome-wide GxG interaction testing of these vQTLs enabled discovery of novel interactions between (1) CCL24 and CCL26 for eosinophil count and plasma CCL24 and CCL26 protein levels and (2) HLA-DQA1 and HLA-DQB1 for lymphocyte count and risk of celiac disease, both of which replicated in ∼140,000 NIH All of Us and ∼70,000 Vanderbilt BioVU participants. Second, we used a biologically informed approach to search for vQTL in disease-relevant genes. This approach identified (1) a known interaction for hemoglobin between two pathogenic variants in HFE which cause hereditary hemochromatosis and alters risk of cirrhosis and (2) a novel interaction between the JAK2 46/1 haplotype and a variant on chromosome 14 which modifies platelet count, JAK2 V617F clonal hematopoiesis, and risk of polycythemia vera. This work identifies novel disease-relevant GxG interactions and demonstrates the utility of vQTL-based approaches in identifying GxG interactions relevant to human health at scale.

AltAFplotter: a web app for reliable UPD detection in NGS diagnostics

BackgroundThe detection of uniparental disomies (the inheritance of both chromosome homologues from a single parent, UPDs) is not part of most standard or commercial NGS-pipelines in human genetics and thus a common gap in NGS diagnostics. To address this we developed a tool for UPD-detection based on panel or exome data which is easy to use and publicly available.ResultsThe app is freely available at https://altafplotter.uni-leipzig.de/ and implemented in Python, using the Streamlit framework for data science web apps. It utilizes bcftools and tabix for processing vcf files. The source code is available at https://github.com/HUGLeipzig/altafplotter and can be used to host your own instance of the tool.ConclusionWe believe the app to be a great benefit for research and diagnostic labs, which struggle identifying and interpreting UPDs in their NGS diagnostic setup. The information provided allows a quick interpretation of the results and thus is suitable for usage in a high throughput manner by clinicians and biologists.

The first review on prenatal drug exposure and ocular malformation occurrence.

Even though a non-negligible portion of congenital eye anomalies has a clear genetic origin, an etiology is not found for most patients. Prenatal medication exposure is recognized to be involved in fetal malformations and several medications are specifically known to alter eye morphogenesis during embryonic development leading to congenital eye defects. We explored and reviewed the role of medications described in the genesis of ocular malformations, a role that has been little evaluated and probably still underestimated especially since several studies have shown the wide exposure of pregnant women to medication. We present our results in two sections; the first describes medications reported to be associated with ocular malformations in humans; the second details medications responsible for ocular malformations in animal models. We have summarized these results in tables, providing a relevant tool for clinicians. As most of the associations between medication exposure and congenital eye defects are either old or single case reports, this study highlights the needs for high epidemiological vigilance, accurate clinical description as well as a combination of studies on human genetics and experimental studies. Since medication exposures are potentially modifiable risk factors for congenital anomalies, this represents an important opportunity to implement preventive measures.

Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques.

The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of functional impacts for effective disease diagnosis and personalized treatment strategies. Traditional methods, often compromised by suboptimal feature selection and limited generalizability, are outpaced by the enhanced classification model, MissenseNet (Missense Classification Network). This model, advancing beyond standard predictive features, incorporates structural insights from AlphaFold2 protein predictions, thus optimizing structural data utilization. MissenseNet, built on the ShuffleNet architecture, incorporates an encoder-decoder framework and a Squeeze-and-Excitation (SE) module designed to adaptively adjust channel weights and enhance feature fusion and interaction. The model's efficacy in classifying pathogenicity has been validated through superior accuracy compared to conventional methods and by achieving the highest areas under the Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves (Area Under the Curve and Area Under the Precision-Recall Curve) in an independent test set, thus underscoring its superiority.

Evaluation of genotype imputation using Glimpse tools on low coverage ancient DNA.

Ancient DNA provides a unique frame for directly studying human population genetics in time and space. Still, since most of the ancient genomic data is low coverage, analysis is confronted with a low number of SNPs, genotype uncertainties, and reference-bias. Here, we for the first time benchmark the two distinct versions of Glimpse tools on 120 ancient human genomes from Eurasia including those largely from previously under-evaluated regions and compare the performance of genotype imputation with de facto analysis approaches for low coverage genomic data analysis. We further investigate the impact of two distinct reference panels on imputation accuracy for low coverage genomic data. We compute accuracy statistics and perform PCA and f4-statistics to explore the behaviour of genotype imputation on low coverage data regarding (i)two versions of Glimpse, (ii)two reference panels, (iii)four post-imputation filters and coverages, as well as (iv)data type and geographical origin of the samples on the analyses. Our results reveal that even for 0.1X coverage ancient human genomes, genotype imputation using Glimpse-v2 is suitable. Additionally, using the 1000 Genomes merged with Human Genome Diversity Panel improves the accuracy of imputation for the rare variants with low MAF, which might be important not only for ancient genomics but also for modern human genomic studies based on low coverage data and for haplotype-based analysis. Most importantly, we reveal that genotype imputation of low coverage ancient human genomes reduces the genetic affinity of the samples towards human reference genome. Through solving one of the most challenging biases in data analysis, so-called reference bias, genotype imputation using Glimpse v2 is promising for low coverage ancient human genomic data analysis and for rare-variant-based and haplotype-based analysis.

Challenging Misconceptions about Race in Undergraduate Genetics.

Racial biases, which harm marginalized and excluded communities, may be combatted by clarifying misconceptions about race during biology lessons. We developed a human genetics laboratory activity that challenges the misconception that race is biological (biological essentialism). We assessed the relationship between this activity and student outcomes using a survey of students' attitudes about biological essentialism and color-evasive ideology and a concept inventory about phylogeny and human diversity. Students in the human genetics laboratory activity showed a significant decrease in their acceptance of biological essentialism compared with a control group, but did not show changes in color-evasive ideology. Students in both groups exhibited increased knowledge in both areas of the concept inventory, but the gains were larger in the human genetics laboratory. In the second iteration of this activity, we found that only white students' decreases in biological essentialist beliefs were significant and the activity failed to decrease color-evasive ideologies for all students. Concept inventory gains were similar and significant for both white and non-white students in this iteration. Our findings underscore the effectiveness of addressing misconceptions about the biological origins of race and encourage more research on ways to effectively change damaging student attitudes about race in undergraduate genetics education.

Exploration of the Human Microbiome’s Role in Health and Disease through the Lens of Genetics

The human microbiome is a complex ecosystem of bacteria residing in the body. It plays a crucial role in safeguarding the overall well-being of individuals while also making them more vulnerable to various diseases. The human microbiota, genetics, and health have a complex connection, which is significant for maintaining health and preventing infections. The microbiome has a role in several gastrointestinal, skin, dental, and systemic diseases, such as obesity, diabetes, and autoimmune disorders. Genetic variation and lifestyle and dietary choices modify the microbiome composition, thereby influencing the risk of developing severe infections. The microbiome impacts host gene expression and acts as a biomarker for several diseases. The gut microbiome and genes are linked in the pathogenesis of obesity and inflammatory bowel disease. Therefore, this review focuses on the relationship between the microbiome and genetics and elucidation of the complexity of this connection. Future research-based microbiome interventions to prevent diseases could lead to strategies for personalized medicine that enhance treatment efficacy and health outcomes.

Genetic structure analysis of yak breeds and their response to adaptive evolution

Yaks are crucial genetic resources in the Tibetan Plateau and surrounding regions. Throughout the long process of domestication, natural and artificial selection pressures have enabled yaks to demonstrate adaptive characteristics to the environment in terms of physiological structure and genetic molecules, but no systematic cell analysis has been carried out on this phenomenon of yaks. Here, the population structure and genetic diversity of yak were studied by WGRS, and the genes related to yak adaptability were excavated. Combined with scRNA-seq method, the transcription map of yak lung tissue and skin tissue was constructed, which provided a new comprehensive insight into yak adaptability. The analysis of yak population structure showed that there was obvious genetic differentiation between TZ _ yak and other seven yak populations, while there was significant genetic exchange between PL _ yak and SB _ yak at high altitude. WGRS and scRNA-seq analysis revealed that the gene HIF1A related to high altitude adaptation was expressed in various cell types, while EPAS1 was predominantly expressed in epithelial and endothelial cells of yak lung tissue. Endothelial cells play a critical role in hypoxia-adapted VEGF signaling, which correlates closely with the high expression of KDR and VEGFA genes in endothelial cells and monocytes. Furthermore, in the selection signal of High _ yak vs Low _ yak, 19.8 % of the genes overlapped with the genes screened by skin scRNA-seq, including genes related to coat color such as RORA, BNC2, and KIT. Notably, BNC2 is a gene associated with melanin deposition and shows high expression levels in HS cells. Additionally, GRN in melanocytes and SORT1 in IRS play an important role in cell communication between melanocytes and IRS. These findings offer new insights into the natural polymorphism of yaks and provide a valuable reference for future research on high-altitude mammals, and potentially even human genetics.

Biosynthesis and Metabolism of ApoB-Containing Lipoproteins.

Recent advances in human genetics, together with a substantial body of epidemiological, preclinical and clinical trial evidence, strongly support a causal relationship between triglyceride-rich lipoproteins (TRLs) and atherosclerotic cardiovascular disease. Consequently, the secretion and metabolism of TRLs have a significant impact on cardiovascular health. This knowledge underscores the importance of understanding the molecular mechanisms and regulation of very-low-density lipoprotein (VLDL) and chylomicron biogenesis. Fortunately, there has been a resurgence of interest in the intracellular assembly, trafficking, degradation, and secretion of VLDL, leading to many ground-breaking molecular insights. Furthermore, the identification of molecular control mechanisms related to triglyceride metabolism has greatly advanced our understanding of the complex metabolism of TRLs. In this review, we explore recent advances in the assembly, secretion, and metabolism of TRLs. We also discuss available treatment strategies for hypertriglyceridemia.

Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization

Identifying causal genes underlying genome-wide association studies (GWAS) is a fundamental problem in human genetics. Although colocalisation with gene expression quantitative trait loci (eQTLs) is often used to prioritise GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analysed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalogue datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalisation methods (coloc.susie, coloc.abf and CLPP) and five Mendelian randomisation (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalisation methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalisation method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalising QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritising novel targets requires triangulation of evidence from multiple sources.

Benefit-Sharing by Design: A Call to Action for Human Genomics Research.

The ethical standards for the responsible conduct of human research have come a long way; however, concerns surrounding equity remain in human genetics and genomics research. Addressing these concerns will help society realize the full potential of human genomics research. One outstanding concern is the fair and equitable sharing of benefits from research on human participants. Several international bodies have recognized that benefit-sharing can be an effective tool for ethical research conduct, but international laws, including the Convention on Biological Diversity and its Nagoya Protocol on Access and Benefit-Sharing, explicitly exclude human genetic and genomic resources. These agreements face significant challenges that must be considered and anticipated if similar principles are applied in human genomics research. We propose that benefit-sharing from human genomics research can be a bottom-up effort and embedded into the existing research process. We propose the development of a "benefit-sharing by design" framework to address concerns of fairness and equity in the use of human genomic resources and samples and to learn from the aspirations and decade of implementation of the Nagoya Protocol.

Deep Learning Sequence Models for Transcriptional Regulation.

Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research.

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.