The impact of rare and low-frequency genetic variants in common disease

Population-specific putative causal variants shape quantitative traits

Human genetic variants are associated with many traits through largely unknown mechanisms. Here, combining approximately 260,000 Japanese study participants, a Japanese-specific genotype reference panel and statistical fine-mapping, we identified 4,423 significant loci across 63 quantitative traits, among which 601 were new, and 9,406 putatively causal variants. New associations included Japanese-specific coding, splicing and noncoding variants, exemplified by a damaging missense variant rs730881101 in TNNT2 associated with lower heart function and increased risk for heart failure (P = 1.4 × 10-15 and odds ratio = 4.5, 95% confidence interval = 3.1-6.5). Putative causal noncoding variants were supported by state-of-art in silico functional assays and had comparable effect sizes to coding variants. A plausible example of new mechanisms of causal variants is an enrichment of causal variants in 3' untranslated regions (UTRs), including the Japanese-specific rs13306436 in IL6 associated with pro-inflammatory traits and protection against tuberculosis. We experimentally showed that transcripts with rs13306436 are resistant to mRNA degradation by regnase-1, an RNA-binding protein. Our study provides a list of fine-mapped causal variants to be tested for functionality and underscores the importance of sequencing, genotyping and association efforts in diverse populations.

Lack of a genetic risk continuum between pubertal timing in the general population and idiopathic hypogonadotropic hypogonadism

Pubertal timing is a highly heritable trait in the general population. Recently, a large-scale exome-wide association study has implicated rare variants in six genes (KDM4C, MC3R, MKRN3, PDE10A, TACR3, and ZNF483) as genetic determinants of pubertal timing within the general population. Two of the genes (TACR3, MKRN3) are already implicated in extreme disorders of pubertal timing. This observation suggests that there may be a pervasive "genetic risk continuum" wherein genes that govern pubertal timing in the general population, by extension, may also be causal for rare Mendelian disorders of pubertal timing. Hence, we hypothesized that the four novel genes linked to pubertal timing in the population will also contribute to idiopathic hypogonadotropic hypogonadism (IHH), a genetic disorder characterized by absent puberty. Exome sequencing data from 1322 unrelated IHH probands were reviewed for rare sequence variants (RSVs) (minor allele frequency bins: <1%; <0.1%; <0.01%) in the six genes linked to puberty in the general population. A gene-based rare variant association testing (RVAT) was performed between the IHH cohort and a reference public genomic sequences repository-the Genome Aggregation Database (gnomAD). As expected, RVAT analysis showed that RSVs in TACR3, a known IHH gene, were significantly enriched in the IHH cohort compared to gnomAD cohort across all three MAF bins. However, RVAT analysis of the remaining five genes failed to show any RSV enrichment in the IHH cohort across all MAF bins. Our findings argue strongly against a pervasive genetic risk continuum between pubertal timing in the general population and extreme pubertal phenotypes. The biologic basis of such distinct genetic architectures' merits further evaluation.

Integration of variant annotations using deep set networks boosts rare variant association testing

Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.

An atlas of genetic effects on the monocyte methylome across European and African populations

Elucidating the genetic architecture of DNA methylation (DNAm) is crucial for decoding the etiology of complex diseases. However, current epigenomic studies often suffer from incomplete coverage of methylation sites and the use of tissues containing heterogeneous cell populations. To address these challenges, we present a comprehensive human methylome atlas based on deep whole-genome bisulfite sequencing (WGBS) and whole-genome sequencing (WGS) of purified monocytes from 298 European Americans (EA) and 160 African Americans (AA) in the Louisiana Osteoporosis Study. Our atlas enables the analysis of over 25 million DNAm sites. We identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis -regions for EA and AA populations, respectively, with 880,108 sites shared between ancestries. While cis -meQTLs exhibited population-specific patterns, primarily due to differences in minor allele frequencies, shared cis -meQTLs showed high concordance across ancestries. Notably, cis -heritability estimates revealed significantly higher mean values in the AA population (0.09) compared to the EA population (0.04). Furthermore, we developed population-specific DNAm imputation models using Elastic Net, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. The performance of our MWAS models was validated through a systematic multi-ancestry analysis of 41 complex traits from the Million Veteran Program. Our findings bridge the gap between genomics and the monocyte methylome, uncovering novel methylation-phenotype associations and their transferability across diverse ancestries. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Rare heterozygous variants in paediatric steroid resistant nephrotic syndrome - a population-based analysis of their significance

Genetic testing in nephrotic syndrome may identify heterozygous predicted-pathogenic variants (HPPVs) in autosomal recessive (AR) genes that are known to cause disease in the homozygous or compound heterozygous state. In such cases, it can be difficult to define the variant's true significance and questions remain about whether a second pathogenic variant has been missed during analysis or whether the variant is an incidental finding. There are now known to be over 70 genes associated with nephrotic syndrome, the majority inherited as an AR trait. Knowledge of whether such HPPVs occur with equal frequency in patients compared to the general population would assist interpretation of their significance. Exome sequencing was performed on 187 Steroid-Resistant Nephrotic Syndrome (SRNS) paediatric patients recruited to a UK rare disease registry plus originating from clinics at Evelina, London. 59 AR podocytopathy linked genes were analysed in each patient and a list of HPPVs created. We compared the frequency of detected HPPVs with a 'control' population from the gnomAD database containing exome data from approximately 50,000 individuals. A bespoke filtering process was used for both patients and controls to predict 'likely pathogenicity' of variants. In total 130 Caucasian SRNS patients were screened across 59 AR genes and 201 rare heterozygous variants were identified. 17/201 (8.5%) were assigned as 'likely pathogenic' (HPPV) using our bespoke filtering method. Comparing each gene in turn, for SRNS patients with a confirmed genetic diagnosis, in 57 of the 59 genes we found no statistically significant difference in the frequency of these HPPVs between patients and controls (In genes ARHGDIA and TP53RK, we identified a significantly higher number of HPPVs in the control population compared with the patients when filtering was performed with 'high stringency' settings only). In the SRNS patients without a genetics diagnosis confirmed, there was no statistically significant difference identified in any gene between patient and control. In children with SRNS, we propose that identification of HPPV in AR podocytopathy linked genes is not necessarily representative of pathogenicity, given that the frequency is similar to that seen in controls for the majority. Whilst this may not exclude the presence of genetic kidney disease, this type of heterozygous variant is unlikely to be causal and each result must be interpreted in its clinical context.

Rare variants at KCNJ2 are associated with LDL-cholesterol levels in a cross-population study

Leveraging whole genome sequencing data of 1751 individuals from the UK and 2587 Qatari subjects, we suggest here an association of rare variants mapping to the sour taste-associated gene KCNJ2 with reduced low-density lipoprotein cholesterol (LDL-C, P = 2.10 × 10-12) and with a 22% decreased dietary trans-fat intake. This study identifies a novel candidate rare locus for LDL-C, adding insights into the genetic architecture of a complex trait implicated in cardiovascular disease.

Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease

Human plasminogen (PLG), the zymogen of the fibrinolytic protease, plasmin, is a polymorphic protein with two widely distributed codominant alleles, PLG/Asp453 and PLG/Asn453. About 15 other missense or non-synonymous single nucleotide polymorphisms (nsSNPs) of PLG show major, yet different, relative abundances in world populations. Although the existence of these relatively abundant allelic variants is generally acknowledged, they are often overlooked or assumed to be non-pathogenic. In fact, at least half of those major variants are classified as having conflicting pathogenicity, and it is unclear if they contribute to different molecular phenotypes. From those, PLG/K19E and PLG/A601T are examples of two relatively abundant PLG variants that have been associated with PLG deficiencies (PD), but their pathogenic mechanisms are unclear. On the other hand, approximately 50 rare and ultra-rare PLG missense variants have been reported to cause PD as homozygous or compound heterozygous variants, often leading to a debilitating disease known as ligneous conjunctivitis. The true abundance of PD-associated nsSNPs is unknown since they can remain undetected in heterozygous carriers. However, PD variants may also contribute to other diseases. Recently, the ultra-rare autosomal dominant PLG/K311E has been found to be causative of hereditary angioedema (HAE) with normal C1 inhibitor. Two other rare pathogenic PLG missense variants, PLG/R153G and PLG/V709E, appear to affect platelet function and lead to HAE, respectively. Herein, PLG missense variants that are abundant and/or clinically relevant due to association with disease are examined along with their world distribution. Proposed molecular mechanisms are discussed when known or can be reasonably assumed.

SwissGenVar: A Platform for Clinical-Grade Interpretation of Genetic Variants to Foster Personalized Healthcare in Switzerland

Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows. This includes extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUSs) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of the standards used to describe genetic variants and the associated phenotypes when searching for relevant information to support clinical decision making. To address this, all five Swiss academic institutions for Medical Genetics joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its aim is to provide a protected environment for expert evidence sharing about individual variants to harmonize and upscale their significance interpretation at the clinical grade according to international standards. To corroborate the clinical assessment, the variant-related data will be combined with consented high-quality clinical information. Broader visibility will be achieved by interfacing with international databases, thus supporting global initiatives in personalized healthcare.

Rosacea in Older Adults and Pharmacologic Treatments

Rosacea is a chronic inflammatory skin condition that is often more severe in older patients. The main clinical features are erythema, telangiectasia, and inflammatory lesions of the face. The pathogenesis of this condition is not fully understood but certainly multifaceted. Immune and inflammatory dysregulation, genetics, neurogenic dysregulation, microbiome dysbiosis, and systemic disease have all been implicated in rosacea pathogenesis. As we better understand the various pathways that lead to rosacea, we acknowledge that the different symptoms may have unique underlying triggers and mechanisms. Aging also impacts rosacea diagnosis and treatment. Older adults have more severe rosacea symptoms while also having more sensitive and fragile skin than younger patients; therefore, rosacea treatments for older patients require a balance between delivering adequate potency while also minimizing skin irritation and other adverse effects. Until recently, rosacea diagnoses were based on concrete subtypes that did not necessarily capture each patient's manifestation of rosacea. There is now an emphasis on more personalized phenotype-based diagnoses and treatments, which allows for more emphasis on treating individual symptoms and accounting for the unique characteristics of older patients. Centrofacial erythema is best treated with brimonidine and oxymetazoline, while phymatous change and telangiectasia are best treated with surgery and laser ablation. Treatment for rosacea papules and pustules ranges from topicals, such as azelaic acid, ivermectin, metronidazole, minocycline, and encapsulated benzoyl peroxide, to systemics, such as doxycycline and isotretinoin. It is important to understand these treatments in relation to adverse effects and drug interactions that may specifically arise in older populations to provide optimal care. As we advance in understanding rosacea's pathogenesis and adopt personalized phenotype-based approaches, optimizing care for older patients becomes crucial. Continued research into novel treatments is essential to address their unique needs.

Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes

Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare gnomAD variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated an interrogation of 11,929 somatic mutations, uncovering 3928 (33%) functional mutations in 155 cancer driver genes. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our study elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.

Structured testing of genetic association with mixed clinical outcomes

Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one. Moreover, the clinical outcomes are complex, including patients' survival time and other binary or continuous outcomes such as recurrences and lymph node count, and how to effectively analyze genetic association with these outcomes remains unclear. In this article, we proposed a structured test statistic for testing genetic association with mixed types of survival, binary, and continuous outcomes. The structured testing incorporates known biological information of variants while allowing for their heterogeneous effects and is a powerful strategy for analyzing infrequent genetic factors. Simulation studies show that the proposed test statistic has correct type I error and is highly effective in detecting significant genetic variants. We applied our approach to a uterine corpus endometrial carcinoma study and identified several genetic pathways associated with the clinical outcomes.

Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes

Introduction: After the era of genome-wide association studies (GWAS), thousands of genetic variants have been identified to exhibit main effects on human phenotypes. The next critical issue would be to explore the interplay between genes, the so-called "gene-gene interactions" (GxG) or epistasis. An exhaustive search for all single-nucleotide polymorphism (SNP) pairs is not recommended because this will induce a harsh penalty of multiple testing. Limiting the search of epistasis on SNPs reported by previous GWAS may miss essential interactions between SNPs without significant marginal effects. Moreover, most methods are computationally intensive and can be challenging to implement genome-wide. Methods: I here searched for GxG through variance quantitative trait loci (vQTLs) of 29 continuous Taiwan Biobank (TWB) phenotypes. A discovery cohort of 86,536 and a replication cohort of 25,460 TWB individuals were analyzed, respectively. Results: A total of 18 nearly independent vQTLs with linkage disequilibrium measure r 2 < 0.01 were identified and replicated from nine phenotypes. 15 significant GxG were found with p-values <1.1E-5 (in the discovery cohort) and false discovery rates <2% (in the replication cohort). Among these 15 GxG, 11 were detected for blood traits including red blood cells, hemoglobin, and hematocrit; 2 for total bilirubin; 1 for fasting glucose; and 1 for total cholesterol (TCHO). All GxG were observed for gene pairs on the same chromosome, except for the APOA5 (chromosome 11)-TOMM40 (chromosome 19) interaction for TCHO. Discussion: This study provided a computationally feasible way to search for GxG genome-wide and applied this approach to 29 phenotypes.

Identification of novel rare variants for anxiety: an exome-wide association study in the UK Biobank

BACKGROUND

Rare variants are believed to play a substantial role in the genetic architecture of mental disorders, particularly in coding regions. However, limited evidence supports the impact of rare variants on anxiety.

METHODS

Using whole-exome sequencing data from 200,643 participants in the UK Biobank, we investigated the contribution of rare variants to anxiety. Firstly, we computed genetic risk score (GRS) of anxiety utilizing genotype data and summary data from a genome-wide association study (GWAS) on anxiety disorder. Subsequently, we identified individuals within the lowest 50% GRS, a subgroup more likely to carry pathogenic rare variants. Within this subgroup, we classified individuals with the highest 10% 7-item Generalized Anxiety Disorder scale (GAD-7) score as cases (N = 1869), and those with the lowest 10% GAD-7 score were designated as controls (N = 1869). Finally, we conducted gene-based burden tests and single-variant association analyses to assess the relationship between rare variants and anxiety.

RESULTS

Totally, 47,800 variants with MAF ≤0.01 were annotated as non-benign coding variants, consisting of 42,698 nonsynonymous SNVs, 489 nonframeshift substitution, 236 frameshift substitution, 617 stop-gain and 40 stop-loss variants. After variation aggregation, 5066 genes were included in gene-based association analysis. Totally, 11 candidate genes were detected in burden test, such as RNF123 (PBonferroni adjusted = 3.40 × 10-6), MOAP1(PBonferroni adjusted = 4.35 × 10-4), CCDC110 (PBonferroni adjusted = 5.83 × 10-4). Single-variant test detected 9 rare variants, such as rs35726701(RNF123)(PBonferroni adjusted = 3.16 × 10-10) and rs16942615(CAMTA2) (PBonferroni adjusted = 4.04 × 10-4). Notably, RNF123, CCDC110, DNAH2, and CSKMT gene were identified in both tests.

CONCLUSIONS

Our study identified novel candidate genes for anxiety in protein-coding regions, revealing the contribution of rare variants to anxiety.

[Genetic factors associated with long COVID]

INTRODUCTION

The variability in expression and evolution of COVID is not completely explained by clinical factors. In fact, genetic factors play an important role. Moreover, it is unknown whether the genetic factor that contribute to susceptibility and severity are also involved in the onset and evolution of long-COVID. The objective of this review is to gather information from literature to understand which genetic factors are involved in the onset of persistent COVID.

MATERIAL AND METHODS

Systematic review in PubMed and bioRxiv and medRxiv repositories based on MeSH-descriptors and MeSH-terms related to COVID and genetic factors. Using these terms 2715 articles were pooled. An initial screening performed by authors independently, selected 205 articles of interest. A final deeper screening a total of 85 articles were chosen for complete reading and summarized in this review.

RESULTS

Although ACE2 and TMPSS6 are involved in COVID susceptibility, their involvement in long-COVID has not been found. On the other hand, the severity of the disease and the onset of long-COVID has been associated with different genes involved in the inflammatory and immune response. Particularly interesting has been the association found with the FOXP4 locus.

CONCLUSIONS

Although studies on long-COVID are insufficient to fully comprehend the cause, it is clear that the current identified genetic factors do not fully explain the progression and onset of long-COVID. Other factors such as polygenic action, pleiotropic genes, the microbiota and epigenetic changes must be considered and studied.

A novel CTBP1 variant in a Chinese pediatric patient with a phenotype distinct from hypotonia, ataxia, developmental delay, and tooth enamel defect syndrome

Hypotonia, Ataxia, Developmental Delay, and Tooth Enamel Defect Syndrome (HADDTS) is an exceptionally rare disorder resulting from a heterozygous variant in the C-terminal binding protein 1 (CTBP1) gene. To date, a mere two variants (14 patients) have been documented on a global scale. The aim of this study was to identify a causative CTBP1 variant in a Chinese patient, and to determine the potential pathogenicity of the identified variant. Here, Whole-exome sequencing (WES) was conducted on the proband to pinpoint the candidate variant. Following this, Sanger sequencing was employed to validate the identified candidate variant and examine its co-segregation within the available family members. Employing both in silico prediction and three-dimensional protein modeling, we conducted an analysis to assess the potential functional implications of the variant on the encoded protein. Our investigation led to the identification of a novel heterozygous variant in the CTBP1 gene, namely, c.371 C>T (p.Ser124Phe), in a Chinese patient. This case represents the first confirmed instance of such a variant in a Chinese patient. When comparing the patient's clinical symptoms with those reported in the literature, notable distinctions were observed between her primary symptoms and those associated with HADDTS. She showed other signs such as microcephaly, coarse facial features, single transverse palmar crease, visible beard, myopia, coarse toenail and skeletal anomalies. This study enriching the spectrum of genetic variants observed in different ethnic populations and expanding the phenotypic profile associated with this gene. These findings are expected to contribute to the enhancement of future variant-based screening and genetic diagnosis, while also providing further insights into the pathogenic mechanisms underlying CTBP1-related conditions.

Exploring the frequency of a TP53 polyadenylation signal variant in tumor DNA from patients diagnosed with lung adenocarcinomas, sarcomas and uterine leiomyomas

The TP53 3'UTR variant rs78378222 A>C has been detected in different tumor types as a somatic alteration that reduces p53 expression through modification of polyadenylation and miRNA regulation. Its prevalence is not yet known in all tumors. Herein, we examine tumor tissue prevalence of rs7837822 in Brazilian cohorts of patients from south and southeast regions diagnosed with lung adenocarcinoma (LUAD, n=586), sarcoma (SARC, n=188) and uterine leiomyoma (ULM, n=41). The minor allele (C) was identified in heterozygosity in 6/586 LUAD tumors (prevalence = 1.02 %) and none of the SARC and ULM samples. Additionally, next generation sequencing analysis revealed that all variant-positive tumors (n=4) with sample availability had additional pathogenic or likely pathogenic somatic variants in the TP53 coding regions. Among them, 3/4 (75 %) had the same pathogenic or likely pathogenic sequence variant (allele frequency <0.05 in tumor DNA) namely c.751A>C (p.Ile251Leu). Our results indicate a low somatic prevalence of rs78378222 in LUAD, ULM and SARC tumors from Brazilian patients, which suggests that no further analysis of this variant in the specific studied regions of Brazil is warranted. However, these findings should not exclude tumor molecular testing of this TP53 3'UTR functional variant for different populations.

PASTRY: achieving balanced power for detecting risk and protective minor alleles in meta-analysis of association studies with overlapping subjects

BACKGROUND

Meta-analysis is a statistical method that combines the results of multiple studies to increase statistical power. When multiple studies participating in a meta-analysis utilize the same public dataset as controls, the summary statistics from these studies become correlated. To solve this challenge, Lin and Sullivan proposed a method to provide an optimal test statistic adjusted for the correlation. This method quickly became the standard practice. However, we identified an unexpected power asymmetry phenomenon in this standard framework. This can lead to unbalanced power for detecting protective minor alleles and risk minor alleles.

RESULTS

We found that the power asymmetry of the current framework is mainly due to the errors in approximating the correlation term. We then developed a meta-analysis method based on an accurate correlation estimator, called PASTRY (A method to avoid Power ASymmeTRY). PASTRY outperformed the standard method on both simulated and real datasets in terms of the power symmetry.

CONCLUSIONS

Our findings suggest that PASTRY can help to alleviate the power asymmetry problem. PASTRY is available at https://github.com/hanlab-SNU/PASTRY .

Single nucleotide polymorphism within chromosome 8q24 is associated with prostate cancer development in Saudi Arabia

Objective

Genome-wide association studies have demonstrated that single nucleotide polymorphisms (SNPs) are important risk factors for the development of prostate cancer (PCa). Preliminary studies have suggested that the incidence of PCa in Saudi males is low but is probably familial or genetically related.

Methods

To identify any possible association of SNP with PCa development in Saudi patients, we investigated a group of SNPs in Saudi PCa patients (n=85) and compared the outcomes to healthy normal controls (n=115) and nodular hyperplasia patients (n=120). DNA was extracted from paraffin-embedded formalin fixed tissue or whole blood from both patients' groups and healthy control group. A total of thirteen SNPs were genotyped using TaqMan® minor groove binder polymerase chain reaction assay.

Results

The rs16901979A, s629242T and rs1447295A alleles were found at significantly higher frequency in PCa patients than controls (p<0.05). The rs16901979 CA genotype was found at significantly greater frequency in PCa patients than in healthy controls (43% vs. 14%, odds ratio=4.6, p=0.0001) and benign hyperplasia group (43% vs. 25%, odds ratio=2.2, p=0.009).

Conclusion

Our study has highlighted the association of rs16901979 SNP with PCa in Saudi males. Such findings have important implications in the PCa diagnosis and in screening unaffected family members of Saudi patients.

Type 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis

BACKGROUND

Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D.

METHODS

We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program. We included first-order T2D interaction terms in each model to determine whether CAD loci were modified by T2D. The genetic main and interaction effects were assessed using a joint test to determine whether a CAD variant, or gene-based rare variant set, was associated with the respective subclinical atherosclerosis measures and then further determined whether these loci had a significant interaction test.

RESULTS

Using a Bonferroni-corrected significance threshold of P<1.6×10-4, we identified 3 genes (ATP1B1, ARVCF, and LIPG) associated with CAC and 2 genes (ABCG8 and EIF2B2) associated with carotid intima-media thickness and carotid plaque, respectively, through gene-based rare variant set analysis. Both ATP1B1 and ARVCF also had significantly different associations for CAC in T2D cases versus controls. No significant interaction tests were identified through the candidate single-variant analysis.

CONCLUSIONS

These results highlight T2D as an important modifier of rare variant associations in CAD loci with CAC.

A human mitofusin 2 mutation can cause mitophagic cardiomyopathy

Cardiac muscle has the highest mitochondrial density of any human tissue, but mitochondrial dysfunction is not a recognized cause of isolated cardiomyopathy. Here, we determined that the rare mitofusin (MFN) 2 R400Q mutation is 15-20× over-represented in clinical cardiomyopathy, whereas this specific mutation is not reported as a cause of MFN2 mutant-induced peripheral neuropathy, Charcot-Marie-Tooth disease type 2A (CMT2A). Accordingly, we interrogated the enzymatic, biophysical, and functional characteristics of MFN2 Q400 versus wild-type and CMT2A-causing MFN2 mutants. All MFN2 mutants had impaired mitochondrial fusion, the canonical MFN2 function. Compared to MFN2 T105M that lacked catalytic GTPase activity and exhibited normal activation-induced changes in conformation, MFN2 R400Q and M376A had normal GTPase activity with impaired conformational shifting. MFN2 R400Q did not suppress mitochondrial motility, provoke mitochondrial depolarization, or dominantly suppress mitochondrial respiration like MFN2 T105M. By contrast to MFN2 T105M and M376A, MFN2 R400Q was uniquely defective in recruiting Parkin to mitochondria. CRISPR editing of the R400Q mutation into the mouse Mfn2 gene induced perinatal cardiomyopathy with no other organ involvement; knock-in of Mfn2 T105M or M376V did not affect the heart. RNA sequencing and metabolomics of cardiomyopathic Mfn2 Q/Q400 hearts revealed signature abnormalities recapitulating experimental mitophagic cardiomyopathy. Indeed, cultured cardiomyoblasts and in vivo cardiomyocytes expressing MFN2 Q400 had mitophagy defects with increased sensitivity to doxorubicin. MFN2 R400Q is the first known natural mitophagy-defective MFN2 mutant. Its unique profile of dysfunction evokes mitophagic cardiomyopathy, suggesting a mechanism for enrichment in clinical cardiomyopathy.

Rare Variants of Obesity-Associated Genes in Young Adults with Abdominal Obesity

The increase in the prevalence of overweight, obesity and associated diseases is a serious problem. The aim of the study was to identify rare variants in obesity-associated genes in young adults with abdominal obesity in our population and to analyze information about these variants in other populations. Targeted high-throughput sequencing of obesity-associated genes was performed (203 young adults with an abdominal obesity phenotype). In our study, all of the 203 young adults with abdominal obesity had some rare variant in the genes associated with obesity. The widest range of rare and common variants was presented in ADIPOQ, FTO, GLP1R, GHRL, and INS genes. The use of targeted sequencing and clinical criteria makes it possible to identify carriers of rare clinically significant variants in a wide range of obesity-associated genes and to investigate their influence on phenotypic manifestations of abdominal obesity.

Accurate proteome-wide missense variant effect prediction with AlphaMissense

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.

Polygenic scores, and the genome-wide association studies they derive from, will have difficulty identifying genes that predispose one to develop a social behavioral trait

Polygenic scores (PGSs) have several limitations. They are confounded with environmental effects on behavior and cannot be used to study how mutations affect brain function and behavior. For this, mutations with large effects, which often arise in only one geographical population are needed. Genome-wide association studies (GWASs), commonly used for identifying mutations, have difficulty detecting these mutations. A strategy that overcomes this challenge is discussed.

Gene-based burden scores identify rare variant associations for 28 blood biomarkers

BACKGROUND

A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling.

METHODS

We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined).

RESULTS

Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models.

CONCLUSION

This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level.

Biallelic Mutations in P4HTM Cause Syndromic Obesity

We previously demonstrated that 50% of children with obesity from consanguineous families from Pakistan carry pathogenic variants in known monogenic obesity genes. Here, we have discovered a novel monogenetic recessive form of severe childhood obesity using an in-house computational staged approach. The analysis included whole-exome sequencing data of 366 children with severe obesity, 1,000 individuals of the Pakistan Risk of Myocardial Infarction Study (PROMIS) study, and 200,000 participants of the UK Biobank to prioritize genes harboring rare homozygous variants with putative effect on human obesity. We identified five rare or novel homozygous missense mutations predicted deleterious in five consanguineous families in P4HTM encoding prolyl 4-hydroxylase transmembrane (P4H-TM). We further found two additional homozygous missense mutations in children with severe obesity of Indian and Moroccan origin. Molecular dynamics simulation suggested that these mutations destabilized the active conformation of the substrate binding domain. Most carriers also presented with hypotonia, cognitive impairment, and/or developmental delay. Three of the five probands died of pneumonia during the first 2 years of the follow-up. P4HTM deficiency is a novel form of syndromic obesity, affecting 1.5% of our children with obesity associated with high mortality. P4H-TM is a hypoxia-inducible factor that is necessary for survival and adaptation under oxygen deprivation, but the role of this pathway in energy homeostasis and obesity pathophysiology remains to be elucidated.

Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies

Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research in virology, especially for virus discovery. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS has greatly facilitated the sequencing of new genomes of detected viruses and their comparison. However, bioinformatics analyses allowing reconstruction of genome sequences and detection of single nucleotide polymorphisms (SNPs) can potentially create bias and has not been widely addressed so far. Therefore, more knowledge is required on the limitations of predicting SNPs based on HTS-generated sequence samples. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) in three samples through large-scale performance testing (PT) using three artificially designed datasets. To evaluate the impact of bioinformatics analyses, they were divided into three key steps: reads pre-processing, virus-isolate identification, and variant calling. Each step was evaluated independently through an original, PT design including discussion and validation between participants at each step. Overall, this work underlines key parameters influencing SNPs detection and proposes recommendations for reliable variant calling for plant viruses. The identification of the closest reference, mapping parameters and manual validation of the detection were recognized as the most impactful analysis steps for the success of the SNPs detections. Strategies to improve the prediction of SNPs are also discussed.

Polygenic risk scores and risk stratification in deep vein thrombosis

INTRODUCTION

Deep vein thrombosis (DVT) is a complex disease, where 60 % of risk is due to genetic factors, such as the Factor V Leiden (FVL) variant. DVT is either asymptomatic or manifests with unspecific symptoms and, if left untreated, DVT leads to severe complications. The impact is dramatic and currently, there is still a research gap in DVT prevention. We characterized the genetic contribution and stratified individuals based on genetic makeup to evaluate if it favorably impacts risk prediction.

METHODS

In the UK Biobank (UKB), we performed gene-based association tests using exome sequencing data, as well as a genome-wide association study. We also constructed polygenic risk scores (PRS) in a subset of the cohort (Number of cases = 8231; Number of controls = 276,360) and calculated the impact on the prediction capacity of the PRS in a non-overlapping part of the cohort (Number of cases = 4342; Number of controls = 142,822). We generated additional PRSs that excluded the known causative variants.

RESULTS

We discovered and replicated a novel common variant (rs11604583) near the region where are located the TRIM51 and LRRC55 genes and identified a novel rare variant (rs187725533) located near the CREB3L1 gene, associated with 2.5-fold higher risk of DVT. In one of the PRS models constructed, the top decile of risk is associated with 3.4-fold increased risk, an effect that is 2.3-fold when excluding FVL carriers. In the top PRS decile, the cumulative risk of DVT at the age of 80 years is 10 % for FVL carriers, contraposed to 5 % for non-carriers. The population attributable fractions of having a high polygenic risk on the rate of DVT was estimated to be around 20 % in our cohort.

CONCLUSION

Individuals with a high polygenic risk of DVT, and not only carriers of well-studied variants such as FVL, may benefit from prevention strategies.

Cytochrome P450 Oxidoreductase (POR) Associated with Severe Paclitaxel-Induced Peripheral Neuropathy in Patients of European Ancestry from ECOG-ACRIN E5103

PURPOSE

Paclitaxel is a widely used anticancer therapeutic. Peripheral neuropathy is the dose-limiting toxicity and negatively impacts quality of life. Rare germline gene markers were evaluated for predicting severe taxane-induced peripheral neuropathy (TIPN) in the patients of European ancestry. In addition, the impact of Cytochrome P450 (CYP) 2C8, CYP3A4, and CYP3A5 metabolizer status on likelihood of severe TIPN was also assessed.

EXPERIMENTAL DESIGN

Whole-exome sequencing analyses were performed in 340 patients of European ancestry who received a standard dose and schedule of paclitaxel in the adjuvant, randomized phase III breast cancer trial, E5103. Patients who experienced grade 3-4 (n = 168) TIPN were compared to controls (n = 172) who did not experience TIPN. For the analyses, rare variants with a minor allele frequency ≤ 3% and predicted to be deleterious by protein prediction programs were retained. A gene-based, case-control analysis using SKAT was performed to identify genes that harbored an imbalance of deleterious variants associated with increased risk of severe TIPN. CYP star alleles for CYP2C8, CYP3A4, and CYP3A5 were called. An additive logistic regression model was performed to test the association of CYP2C8, CYP3A4, and CYP3A5 metabolizer status with severe TIPN.

RESULTS

Cytochrome P450 oxidoreductase (POR) was significantly associated with severe TIPN (P value = 1.8 ×10-6). Six variants were predicted to be deleterious in POR. There were no associations between CYP2C8, CYP3A4, or CYP3A5 metabolizer status with severe TIPN.

CONCLUSIONS

Rare variants in POR predict an increased risk of severe TIPN in patients of European ancestry who receive paclitaxel.

Whole genome sequencing identifies genetic variants associated with neurogenic inflammation in rosacea

Rosacea is a chronic inflammatory skin disorder with high incidence rate. Although genetic predisposition to rosacea is suggested by existing evidence, the genetic basis remains largely unknown. Here we present the integrated results of whole genome sequencing (WGS) in 3 large rosacea families and whole exome sequencing (WES) in 49 additional validation families. We identify single rare deleterious variants of LRRC4, SH3PXD2A and SLC26A8 in large families, respectively. The relevance of SH3PXD2A, SLC26A8 and LRR family genes in rosacea predisposition is underscored by presence of additional variants in independent families. Gene ontology analysis suggests that these genes encode proteins taking part in neural synaptic processes and cell adhesion. In vitro functional analysis shows that mutations in LRRC4, SH3PXD2A and SLC26A8 induce the production of vasoactive neuropeptides in human neural cells. In a mouse model recapitulating a recurrent Lrrc4 mutation from human patients, we find rosacea-like skin inflammation, underpinned by excessive vasoactive intestinal peptide (VIP) release by peripheral neurons. These findings strongly support familial inheritance and neurogenic inflammation in rosacea development and provide mechanistic insight into the etiopathogenesis of the condition.

The Bovine Pangenome Consortium: democratizing production and accessibility of genome assemblies for global cattle breeds and other bovine species

The Bovine Pangenome Consortium (BPC) is an international collaboration dedicated to the assembly of cattle genomes to develop a more complete representation of cattle genomic diversity. The goal of the BPC is to provide genome assemblies and a community-agreed pangenome representation to replace breed-specific reference assemblies for cattle genomics. The BPC invites partners sharing our vision to participate in the production of these assemblies and the development of a common, community-approved, pangenome reference as a public resource for the research community ( https://bovinepangenome.github.io/ ). This community-driven resource will provide the context for comparison between studies and the future foundation for cattle genomic selection.

Multi-ethnic imputation system (MI-System): a genotype imputation server for high-dimensional data

OBJECTIVE

Genotype imputation is a commonly used technique that infers un-typed variants into a study's genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there's a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance.

METHODS

We aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to, genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain, towards rapidly and efficiently conducting accurate genotype imputation.

RESULTS

We present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds.

CONCLUSION

Users can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research.

STATEMENT OF SIGNIFICANCE

Multi-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

Advances and Challenges for GWAS Analysis in Cardiac Diseases: A Focus on Coronary Artery Disease (CAD)

The achievement of genome-wide association studies (GWAS) has rapidly progressed our understanding of the etiology of coronary artery disease (CAD). It unlocks new strategies to strengthen the stalling of CAD drug development. In this review, we highlighted the recent drawbacks, mainly pointing out those involved in identifying causal genes and interpreting the connections between disease pathology and risk variants. We also benchmark the novel insights into the biological mechanism behind the disease primarily based on outcomes of GWAS. Furthermore, we also shed light on the successful discovery of novel treatment targets by introducing various layers of "omics" data and applying systems genetics strategies. Lastly, we discuss in-depth the significance of precision medicine that is helpful to improve through GWAS analysis in cardiovascular research.

Rare genetic variants underlie outlying levels of DNA methylation and gene-expression

Testing the effect of rare variants on phenotypic variation is difficult due to the need for extremely large cohorts to identify associated variants given expected effect sizes. An alternative approach is to investigate the effect of rare genetic variants on DNA methylation (DNAm) as effect sizes are expected to be larger for molecular traits compared with complex traits. Here, we investigate DNAm in healthy ageing populations-the Lothian Birth Cohorts of 1921 and 1936-and identify both transient and stable outlying DNAm levels across the genome. We find an enrichment of rare genetic single nucleotide polymorphisms (SNPs) within 1 kb of DNAm sites in individuals with stable outlying DNAm, implying genetic control of this extreme variation. Using a family-based cohort, the Brisbane Systems Genetics Study, we observed increased sharing of DNAm outliers among more closely related individuals, consistent with these outliers being driven by rare genetic variation. We demonstrated that outlying DNAm levels have a functional consequence on gene expression levels, with extreme levels of DNAm being associated with gene expression levels toward the tails of the population distribution. This study demonstrates the role of rare SNPs in the phenotypic variation of DNAm and the effect of extreme levels of DNAm on gene expression.

The contribution of copy number variants to psychiatric symptoms and cognitive ability

Copy number variants (CNVs) are deletions and duplications of DNA sequence. The most frequently studied CNVs, which are described in this review, are recurrent CNVs that occur in the same locations on the genome. These CNVs have been strongly implicated in neurodevelopmental disorders, namely autism spectrum disorder (ASD), intellectual disability (ID), and developmental delay (DD), but also in schizophrenia. More recent work has also shown that CNVs increase risk for other psychiatric disorders, namely, depression, bipolar disorder, and post-traumatic stress disorder. Many of the same CNVs are implicated across all of these disorders, and these neuropsychiatric CNVs are also associated with cognitive ability in the general population, as well as with structural and functional brain alterations. Neuropsychiatric CNVs also show incomplete penetrance, such that carriers do not always develop any psychiatric disorder, and may show only mild symptoms, if any. Variable expressivity, whereby the same CNVs are associated with many different phenotypes of varied severity, also points to highly complex mechanisms underlying disease risk in CNV carriers. Comprehensive and longitudinal phenotyping studies of individual CNVs have provided initial insights into these mechanisms. However, more work is needed to estimate and predict the effect of non-recurrent, ultra-rare CNVs, which also contribute to psychiatric and cognitive outcomes. Moreover, delineating the broader phenotypic landscape of neuropsychiatric CNVs in both clinical and general population cohorts may also offer important mechanistic insights.

Nutrigenomics in the context of evolution

Nutrigenomics describes the interaction between nutrients and our genome. Since the origin of our species most of these nutrient-gene communication pathways have not changed. However, our genome experienced over the past 50,000 years a number of evolutionary pressures, which are based on the migration to new environments concerning geography and climate, the transition from hunter-gatherers to farmers including the zoonotic transfer of many pathogenic microbes and the rather recent change of societies to a preferentially sedentary lifestyle and the dominance of Western diet. Human populations responded to these challenges not only by specific anthropometric adaptations, such as skin color and body stature, but also through diversity in dietary intake and different resistance to complex diseases like the metabolic syndrome, cancer and immune disorders. The genetic basis of this adaptation process has been investigated by whole genome genotyping and sequencing including that of DNA extracted from ancient bones. In addition to genomic changes, also the programming of epigenomes in pre- and postnatal phases of life has an important contribution to the response to environmental changes. Thus, insight into the variation of our (epi)genome in the context of our individual's risk for developing complex diseases, helps to understand the evolutionary basis how and why we become ill. This review will discuss the relation of diet, modern environment and our (epi)genome including aspects of redox biology. This has numerous implications for the interpretation of the risks for disease and their prevention.

Missense Variants of von Willebrand Factor in the Background of COVID-19 Associated Coagulopathy

COVID-19 associated coagulopathy (CAC), characterized by endothelial dysfunction and hypercoagulability, evokes pulmonary immunothrombosis in advanced COVID-19 cases. Elevated von Willebrand factor (vWF) levels and reduced activities of the ADAMTS13 protease are common in CAC. Here, we aimed to determine whether common genetic variants of these proteins might be associated with COVID-19 severity and hemostatic parameters. A set of single nucleotide polymorphisms (SNPs) in the vWF (rs216311, rs216321, rs1063856, rs1800378, rs1800383) and ADAMTS13 genes (rs2301612, rs28729234, rs34024143) were genotyped in 72 COVID-19 patients. Cross-sectional cohort analysis revealed no association of any polymorphism with disease severity. On the other hand, analysis of variance (ANOVA) uncovered associations with the following clinical parameters: (1) the rs216311 T allele with enhanced INR (international normalized ratio); (2) the rs1800383 C allele with elevated fibrinogen levels; and (3) the rs1063856 C allele with increased red blood cell count, hemoglobin, and creatinine levels. No association could be observed between the phenotypic data and the polymorphisms in the ADAMTS13 gene. Importantly, in silico protein conformational analysis predicted that these missense variants would display global conformational alterations, which might affect the stability and plasma levels of vWF. Our results imply that missense vWF variants might modulate the thrombotic risk in COVID-19.

Mutation Analysis of Autosomal-Dominant Polycystic Kidney Disease Patients

Autosomal-dominant polycystic kidney disease (ADPKD) is characterized by bilateral kidney cysts that ultimately lead to end-stage kidney disease. While the major causative genes of ADPKD are PKD1 and PKD2, other genes are also thought to be involved. Fifty ADPKD patients were analyzed by exome sequencing or multiplex ligation-dependent probe amplification (MLPA), followed by long polymerase chain reaction and Sanger sequencing. Variants in PKD1 or PKD2 or GANAB were detected in 35 patients (70%). Exome sequencing identified 24, 7, and 1 variants in PKD1, PKD2, and GANAB, respectively, in 30 patients. MLPA analyses identified large deletions in PKD1 in three patients and PKD2 in two patients. We searched 90 cyst-associated genes in 15 patients who were negative by exome sequencing and MLPA analyses, and identified 17 rare variants. Four of them were considered "likely pathogenic" or "pathogenic" variants according to the American College of Medical Genetics and Genomics guidelines. Of the 11 patients without a family history, four, two, and four variants were found in PKD1, PKD2, and other genes, respectively, while no causative gene was identified in one patient. While the pathogenicity of each variant in these genes should be carefully assessed, a comprehensive genetic analysis may be useful in cases of atypical ADPKD.

Weighted kernels improve multi-environment genomic prediction

Crucial to variety improvement programs is the reliable and accurate prediction of genotype's performance across environments. However, due to the impactful presence of genotype by environment (G×E) interaction that dictates how changes in expression and function of genes influence target traits in different environments, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multi-environment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4-33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.

Development of molecular diagnostic platform for α0 -thalassemia 44.6 kb (Chiang Rai, --CR ) deletion in individuals with microcytic red blood cells across Thailand

INTRODUCTION

The α⁰ -thalassemia 44.6 kb or Chiang Rai (--^CR ) deletion has been reported in northern Thailand and is capable of causing hemoglobin (Hb) H disease and a lethal α-thalassemia genotype, Hb Bart's hydrops fetalis, in this region. However, there are no current data regarding the frequency of --^CR nationwide due to a lack of effective diagnostic assay. Therefore, this study aimed to develop a reliable platform for simultaneous genotyping of --^CR and two common α⁰ -thalassemias in Thailand (--^SEA and --^THAI ) and investigate the frequency of --^CR across Thailand.

METHODS

Multiplex gap-PCR assay and five renewable plasmid DNA controls for --^CR , --^SEA , --^THAI , α2-globin (HBA2), and β-actin (ACTB) were newly developed and validated with reference methods. The developed assay was further tested on 1046 unrelated individuals with a reduced mean corpuscular volume (MCV) of less than 75 fl for investigating genotypic and allelic spectrum of --^CR .

RESULTS

Our developed assay showed 100% concordance with reference methods. The results were valid and reproducible throughout hundreds of reactions. Comparison of the genotypic and allelic spectra revealed that heterozygous --^SEA (--^SEA /αα) and --^SEA alleles were dominant with the frequency of 22.85% (239/1046) and 13.34% (279/2092), respectively. Of these, --^THAI and --^CR were relatively rare in this population and comparable to each other with the allelic frequency of 0.14% (3/2092).

CONCLUSION

This study successfully established a reliable molecular diagnostic platform for genotyping of --^CR , --^SEA , and --^THAI in a single reaction. Additionally, we demonstrated the frequency of --^CR in Thailand for the first time and provided knowledge basis for the planning of severe α-thalassemia prevention and control programs in Thailand, where thalassemia is endemic.

Genome-wide analysis of copy-number variation in humans with cleft lip and/or cleft palate identifies COBLL1, RIC1, and ARHGEF38 as clefting genes

Cleft lip with or without cleft palate (CL/P) is a common birth defect with a complex, heterogeneous etiology. It is well established that common and rare sequence variants contribute to the formation of CL/P, but the contribution of copy-number variants (CNVs) to cleft formation remains relatively understudied. To fill this knowledge gap, we conducted a large-scale comparative analysis of genome-wide CNV profiles of 869 individuals from the Philippines and 233 individuals of European ancestry with CL/P with three primary goals: first, to evaluate whether differences in CNV number, amount of genomic content, or amount of coding genomic content existed within clefting subtypes; second, to assess whether CNVs in our cohort overlapped with known Mendelian clefting loci; and third, to identify unestablished Mendelian clefting genes. Significant differences in CNVs across cleft types or in individuals with non-syndromic versus syndromic clefts were not observed; however, several CNVs in our cohort overlapped with known syndromic and non-syndromic Mendelian clefting loci. Moreover, employing a filtering strategy relying on population genetics data that rare variants are on the whole more deleterious than common variants, we identify several CNV-associated gene losses likely driving non-syndromic clefting phenotypes. By prioritizing genes deleted at a rare frequency across multiple individuals with clefts yet enriched in our cohort of individuals with clefts compared to control subjects, we identify COBLL1, RIC1, and ARHGEF38 as clefting genes. CRISPR-Cas9 mutagenesis of these genes in Xenopus laevis and Danio rerio yielded craniofacial dysmorphologies, including clefts analogous to those seen in human clefting disorders.

Rare copy number variation in posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24-71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029, SE = 0.005, P = 6.3 × 10-8). The genome-wide neurodevelopmental CNV burden identified explains 0.034% of the variation in PTSD symptoms. The 15q11.2 BP1-BP2 microdeletion region was significantly associated with PTSD (beta = 0.0206, SE = 0.0056, P = 0.0002). No individual significant genes interrupted by CNV were identified. 22 gene pathways related to the function of the nervous system and brain were significant in pathway analysis (FDR q < 0.05), but these associations were not significant once NDD regions were removed. A larger sample size, better detection methods, and annotated resources of CNV are needed to explore this relationship further.

Mutations along human chromosomes: How randomly scattered are they?

The diversity of mutations in human chromosomes is nowadays very well documented. The mutations characterize populations in the world as well as genetic causes of diseases. In the approach that we follow, we study the patterns of gaps between mutations by means of the rescaled range analysis and the fractal dimension estimates. The results for chromosomes 1 to 22 and X indicate the existence of the so-called Hurst phenomenon in all of them. The interpretation of this outcome entails the presence of long-range correlations and we propose an explanation based on the genomic feature dubbed linkage disequilibrium, a nonrandom association of alleles at different loci. An unexpected outcome is the noteworthy uniform reduction in the Hurst phenomenon when considering the centimorgan metric instead of base position units. By contrast, such uniform reduction is not observed with the fractal dimension values.

Uncommon variants in FLG2 and TCHHL1 are associated with remission of atopic dermatitis in a large longitudinal US cohort

Atopic dermatitis (AD) is a relapsing inflammatory skin disease; filaggrin (FLG) variation has been consistently associated with its pathogenesis. Filaggrin-2 (FLG2) and trichohyalin-like-1 (TCHHL1) are members of the same protein family (S100 fused-type proteins), are similar in structure to FLG, and may be involved in AD pathogenesis. We sought to evaluate the association between variation in FLG2, TCHHL1 and AD remission. We sequenced FLG2 and TCHHL1 in a longitudinal AD cohort using targeted capture-based massively parallel sequencing. Association between individual alleles and AD remission was evaluated with generalized estimating equations for binary outcomes. Association between groups of alleles and AD remission was evaluated using a genetic algorithm to group alleles. We identified two loss-of-function (LoF) mutations in FLG2 (Ser2377Ter, Arg2207Ter) and 2 LoF mutations in TCHHL1 (Gln656Ter, Gln294Ter), none of which were associated with AD remission. Common (MAF > 5%) alleles in FLG2 were similarly unassociated with AD. No common alleles in TCHHL1 were associated with AD remission after multiple testing correction. Among self-described whites, a group of 34 uncommon alleles in FLG2 were associated with increased AD remission (OR 7.64e17; 95% CI 4.41e17-1.32e18; adjusted p < 1.0e-16). Twelve uncommon alleles in TCHHL1 trended toward association with increased AD remission (OR 23.46; 95% CI 7.07-77.89; adjusted p = 0.064). Among self-described African Americans, 13 uncommon FLG2 alleles were associated with increased AD remission (OR 21.01; 95% CI 11.90-37.09; adjusted p < 1.0e-16). No TCHHL1 uncommon allele groups were associated with AD remission among African Americans. Our study supports the role of uncommon alleles in FLG2 and TCHHL1 in AD pathogenesis.

Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases

Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.

The contribution of common and rare genetic variants to variation in metabolic traits in 288,137 East Asians

Metabolic traits are heritable phenotypes widely-used in assessing the risk of various diseases. We conduct a genome-wide association analysis (GWAS) of nine metabolic traits (including glycemic, lipid, liver enzyme levels) in 125,872 Korean subjects genotyped with the Korea Biobank Array. Following meta-analysis with GWAS from Biobank Japan identify 144 novel signals (MAF ≥ 1%), of which 57.0% are replicated in UK Biobank. Additionally, we discover 66 rare (MAF < 1%) variants, 94.4% of them co-incident to common loci, adding to allelic series. Although rare variants have limited contribution to overall trait variance, these lead, in carriers, substantial loss of predictive accuracy from polygenic predictions of disease risk from common variant alone. We capture groups with up to 16-fold variation in type 2 diabetes (T2D) prevalence by integration of genetic risk scores of fasting plasma glucose and T2D and the I349F rare protective variant. This study highlights the need to consider the joint contribution of both common and rare variants on inherited risk of metabolic traits and related diseases.

CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants

Background

Availability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants.

Results

CLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case–control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT, installed through devtools::install_github("ShihChingYu/CLIN_SKAT", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats.

Conclusions

Statistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04987-2.

Nucleotide-based genetic networks: Methods and applications

Genomic variations have been acclaimed as among the key players in understanding the biological mechanisms behind migration, evolution, and adaptation to extreme conditions. Due to stochastic evolutionary forces, the frequency of polymorphisms is affected by changes in the frequency of nearby polymorphisms in the same DNA sample, making them connected in terms of evolution. This article presents all the ingredients to understand the cumulative effects and complex behaviors of genetic variations in the human mitochondrial genome by analyzing co-occurrence networks of nucleotides, and shows key results obtained from such analyses. The article emphasizes recent investigations of these co-occurrence networks, describing the role of interactions between nucleotides in fundamental processes of human migration and viral evolution. The corresponding co-mutation-based genetic networks revealed genetic signatures of human adaptation in extreme environments. This article provides the methods of constructing such networks in detail, along with their graph-theoretical properties, and applications of the genomic networks in understanding the role of nucleotide co-evolution in evolution of the whole genome.

Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine

Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.

Mutation Analysis of Thin Basement Membrane Nephropathy

Thin basement membrane nephropathy (TBMN) is characterized by the observation of microhematuria and a thin glomerular basement membrane on kidney biopsy specimens. Its main cause is heterozygous mutations of COL4A3 or COL4A4, which also cause late-onset focal segmental glomerulosclerosis (FSGS) or autosomal dominant Alport syndrome (ADAS). Thirteen TBMN cases were analyzed using Sanger sequencing, multiplex ligation-dependent probe amplification (MLPA), and exome sequencing. Ten heterozygous variants were detected in COL4A3 or COL4A4 in nine patients via Sanger sequencing, three of which were novel variants. The diagnostic rate of “likely pathogenic” or “pathogenic” under the American College of Medical Genetics and Genomics guidelines was 53.8% (7 out of 13 patients). There were eight single nucleotide variants, seven of which were glycine substitutions in the collagenous domain, one of which was a splice-site single nucleotide variant, and two of which were deletion variants. One patient had digenic variants in COL4A3 and COL4A4. While MLPA analyses showed negative results, exome sequencing identified three heterozygous variants in causative genes of FSGS in four patients with no apparent variants on Sanger sequencing. Since patients with heterozygous mutations of COL4A3 or COL4A4 showed a wide spectrum of disease from TBMN to ADAS, careful follow-up will be necessary for these patients.

A tree-based gene-environment interaction analysis with rare features

Gene-environment (G-E) interaction analysis plays a critical role in understanding and modeling complex diseases. Compared to main-effect-only analysis, it is more seriously challenged by higher dimensionality, weaker signals, and the unique "main effects, interactions" variable selection hierarchy. In joint G-E interaction analysis under which a large number of G factors are analysed in a single model, effort tailored to rare features (e.g., SNPs with low minor allele frequencies) has been limited. Existing investigations on rare features have been mostly focused on marginal analysis, where various data aggregation techniques have been developed, and hypothesis testings have been conducted to identify significant aggregated features. However, such techniques cannot be extended to joint G-E interaction analysis. In this study, building on a very recent tree-based data aggregation technique, which has been developed for main-effect-only analysis, we develop a new G-E interaction analysis approach tailored to rare features. The adopted data aggregation technique allows for more efficient information borrowing from neighboring rare features. Similar to some existing state-of-the-art ones, the proposed approach adopts penalization for variable selection, regularized estimation, and respect of the variable selection hierarchy. Simulation shows that it has more accurate identification of important interactions and main effects than several competing alternatives. In the analysis of NFBC1966 study, the proposed approach leads to findings different from the alternatives and with satisfactory prediction and stability performance.