The Missing Diversity in Human Genetic Studies.

The majority of studies of genetic association with disease have been performed in Europeans. This European bias has important implications for risk prediction of diseases across global populations. In this commentary, we justify the need to study more diverse populations using both empirical examples and theoretical reasoning.

Transcriptional linkage analysis with in vivo AAV-Perturb-seq.

The ever-growing compendium of genetic variants associated with human pathologies demands new methods to study genotype-phenotype relationships in complex tissues in a high-throughput manner1,2. Here we introduce adeno-associated virus (AAV)-mediated direct in vivo single-cell CRISPR screening, termed AAV-Perturb-seq, a tuneable and broadly applicable method for transcriptional linkage analysis as well as high-throughput and high-resolution phenotyping of genetic perturbations in vivo. We applied AAV-Perturb-seq using gene editing and transcriptional inhibition to systematically dissect the phenotypic landscape underlying 22q11.2 deletion syndrome3,4 genes in the adult mouse brain prefrontal cortex. We identified three 22q11.2-linked genes involved in known and previously undescribed pathways orchestrating neuronal functions in vivo that explain approximately 40% of the transcriptional changes observed in a 22q11.2-deletion mouse model. Our findings suggest that the 22q11.2-deletion syndrome transcriptional phenotype found in mature neurons may in part be due to the broad dysregulation of a class of genes associated with disease susceptibility that are important for dysfunctional RNA processing and synaptic function. Our study establishes a flexible and scalable direct in vivo method to facilitate causal understanding of biological and disease mechanisms with potential applications to identify genetic interventions and therapeutic targets for treating disease.

A new era in functional genomics screens.

The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. Despite this, the function of many genes and gene regulatory elements remains poorly characterized, which limits our ability to apply these insights to human disease. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.

A novel method for multiple phenotype association studies based on genotype and phenotype network.

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

A framework for an evidence-based gene list relevant to autism spectrum disorder.

Autism spectrum disorder (ASD) is often grouped with other brain-related phenotypes into a broader category of neurodevelopmental disorders (NDDs). In clinical practice, providers need to decide which genes to test in individuals with ASD phenotypes, which requires an understanding of the level of evidence for individual NDD genes that supports an association with ASD. Consensus is currently lacking about which NDD genes have sufficient evidence to support a relationship to ASD. Estimates of the number of genes relevant to ASD differ greatly among research groups and clinical sequencing panels, varying from a few to several hundred. This Roadmap discusses important considerations necessary to provide an evidence-based framework for the curation of NDD genes based on the level of information supporting a clinically relevant relationship between a given gene and ASD.

Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort.

The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.

Bayesian mixed model inference for genetic association under related samples with brain network phenotype.

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.

Curation of causal interactions mediated by genes associated with autism accelerates the understanding of gene-phenotype relationships underlying neurodevelopmental disorders.

Autism spectrum disorder (ASD) comprises a large group of neurodevelopmental conditions featuring, over a wide range of severity and combinations, a core set of manifestations (restricted sociality, stereotyped behavior and language impairment) alongside various comorbidities. Common and rare variants in several hundreds of genes and regulatory regions have been implicated in the molecular pathogenesis of ASD along a range of causation evidence strength. Despite significant progress in elucidating the impact of few paradigmatic individual loci, such sheer complexity in the genetic architecture underlying ASD as a whole has hampered the identification of convergent actionable hubs hypothesized to relay between the vastness of risk alleles and the core phenotypes. In turn this has limited the development of strategies that can revert or ameliorate this condition, calling for a systems-level approach to probe the cross-talk of cooperating genes in terms of causal interaction networks in order to make convergences experimentally tractable and reveal their clinical actionability. As a first step in this direction, we have captured from the scientific literature information on the causal links between the genes whose variants have been associated with ASD and the whole human proteome. This information has been annotated in a computer readable format in the SIGNOR database and is made freely available in the resource website. To link this information to cell functions and phenotypes, we have developed graph algorithms that estimate the functional distance of any protein in the SIGNOR causal interactome to phenotypes and pathways. The main novelty of our approach resides in the possibility to explore the mechanistic links connecting the suggested gene-phenotype relations.

Benefits and limitations of genome-wide association studies.

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Pathogenic SATB2 missense variants affecting p.Gly392 have variable functional implications and result in diverse clinical phenotypes.

SATB2-associated syndrome (SAS) is caused by pathogenic variants in SATB2, which encodes an evolutionarily conserved transcription factor. Despite the broad range of phenotypic manifestations and variable severity related to this syndrome, haploinsufficiency has been assumed to be the primary molecular explanation.In this study, we describe eight individuals with SATB2 variants that affect p.Gly392 (four women, age range 2-16 years; p.Gly392Arg, p.Gly392Glu and p.Gly392Val). Of these, individuals with p.Gly392Arg substitutions were found to have more severe neurodevelopmental phenotypes based on an established rubric scoring system when compared with individuals with p.Gly392Glu, p.Gly392Val and other previously reported causative SATB2 missense variants. Consistent with the observations at the phenotypic level, using human cell-based and model organism functional data, we documented that while all three described p.Gly392 variants affect the same residue and seem to all have a partial loss-of-function effect, some effects on SATB2 protein function appear to be variant-specific. Our results indicate that genotype-phenotype correlations in SAS are more complex than originally thought, and variant-specific genotype-phenotype correlations are needed.

Extending the clinical spectrum of X-linked Tonne-Kalscheuer syndrome (TOKAS): new insights from the fetal perspective.

INTRODUCTION: Tonne-Kalscheuer syndrome (TOKAS) is a recessive X-linked multiple congenital anomaly disorder caused by RLIM variations. Of the 41 patients reported, only 7 antenatal cases were described. METHOD: After the antenatal diagnosis of TOKAS by exome analysis in a family followed for over 35 years because of multiple congenital anomalies in five male fetuses, a call for collaboration was made, resulting in a cohort of 11 previously unpublished cases. RESULTS: We present a TOKAS antenatal cohort, describing 11 new cases in 6 French families. We report a high frequency of diaphragmatic hernia (9 of 11), differences in sex development (10 of 11) and various visceral malformations. We report some recurrent dysmorphic features, but also pontocerebellar hypoplasia, pre-auricular skin tags and olfactory bulb abnormalities previously unreported in the literature. Although no clear genotype-phenotype correlation has yet emerged, we show that a recurrent p.(Arg611Cys) variant accounts for 66% of fetal TOKAS cases. We also report two new likely pathogenic variants in RLIM, outside of the two previously known mutational hotspots. CONCLUSION: Overall, we present the first fetal cohort of TOKAS, describe the clinical features that made it a recognisable syndrome at fetopathological examination, and extend the phenotypical spectrum and the known genotype of this rare disorder.

Cerebral visual impairment: genetic diagnoses and phenotypic associations.

BACKGROUND: Cerebral visual impairment (CVI) is the most common form of paediatric visual impairment in developed countries. CVI can arise from a host of genetic or acquired causes, but there has been limited research to date on CVI in the context of genetic disorders. METHODS: We carried out a retrospective analysis of genotypic and phenotypic data for participants with CVI within the DECIPHER database and 100 000 Genomes Project (100KGP). RESULTS: 158 individuals with CVI were identified across both cohorts. Within this group, pathogenic or likely pathogenic sequence variants in 173 genes were identified. 25 of these genes already have known associations with CVI, while the remaining 148 are candidate genes for this phenotype. Gene ontology analysis of the CVI gene sets from both DECIPHER and 100KGP suggests that CVI has a similar degree of genetic heterogeneity to other neurodevelopmental phenotypes, and a strong association with genetic variants converging on ion channels and receptor functions. Individuals with a monogenic disorder and CVI have a higher frequency of epilepsies and severe neurodisability than individuals with a monogenic disorder but not CVI. CONCLUSION: This study supports the availability of genetic testing for individuals with CVI alongside other neurodevelopmental difficulties. It also supports the availability of ophthalmological screening for individuals with genetic diagnoses linked to CVI. Further studies could elaborate on the links between specific genetic disorders, visual maturation and broader neurodevelopmental characteristics.

Amyotrophic lateral sclerosis patients with various gene mutations show diverse motor phenotypes and survival in China.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder characterised by progressive degeneration of motor neurons. Genetic factors have a substantial impact on ALS. Therefore, this study aimed to explore the correlation between genotype (SOD1, TARDBP, FUS, C9orf72) and phenotype in ALS. METHODS: Genetic analysis was performed on 2038 patients with ALS, among which 1696 patients with sporadic ALS (SALS) as controls for genotype-phenotype analysis, and 1602 SALS as controls for survival analysis. Logistic regression and Cox proportional hazards models were used for statistical analysis. RESULTS: A total of 172 patients with ALS with the gene mutations were included in the statistical analysis (SOD1, n=65; FUS, n=43; TARDBP, n=27; C9orf72, n=37). SOD1 mutations were more frequent in flail leg phenotype (OR 7.317, p=0.001) and less in bulbar phenotype (OR 0.222, p=0.038). C9orf72 expansions exhibited higher frequency in bulbar phenotype (OR 2.770, p=0.008). SOD1 and FUS mutations were significantly associated with earlier age of onset (HR 2.039, p<0.001; HR 1.762, p=0.001). The patients with SOD1 mutations, C9orf72 expansions and those carrying pathogenic FUS mutations had significantly increased death risk (HR 2.217, p<0.001; HR 1.694, p=0.008; HR 1.652, p=0.036). The increased risk of death in ALS with C9orf72 expansions was significant in females (HR 2.419, p=0.014) but not in males (HR 1.442, p=0.128). CONCLUSION: Our study revealed distinct motor phenotypic tendencies in patients with ALS with different genotypes, indicating variations in the vulnerability of motor neurons during the disease's progression. Furthermore, we made novel discoveries regarding survival of different gene mutations, warranting further investigation.

Novel variants and genotype-phenotype correlation in a multicentre cohort of GNE myopathy in China.

BACKGROUND: GlcNAc2-epimerase (GNE) myopathy is a rare autosomal recessive disorder caused by pathogenic variants in the GNE gene, which is essential for the sialic acid biosynthesis pathway. OBJECTIVE: This multi-centre study aimed to delineate the clinical phenotype and GNE variant spectrum in Chinese patients, enhancing our understanding of the genetic diversity and clinical manifestation across different populations. METHODS: We retrospectively analysed GNE variants from 113 patients, integrating these data with external GNE variants from online databases for a global perspective, examining their consequences, distribution, ethnicity and severity. RESULTS: This study revealed 97 distinct GNE variants, including 35 (36.08%) novel variants. Two more patients with deep intronic variant c.862+870C>T were identified, while whole genome sequencing (WGS) uncovered another two novel intronic variants: c.52-8924G>T and c.1505-12G>A. Nanopore long reads sequencing (LRS) and further PCR analysis verified a 639 bp insertion at chr9:36249241. Missense variants predominantly located in the epimerase/kinase domain coding region, indicating the impairment of catalytic function as a key pathogenic consequence. Comparative studies with Japanese, Korean and Jewish, our cohorts showed later onset ages by 2 years. The high allele frequency of the non-catalytic GNE variant, c.620A>T, might underlie the milder phenotype of Chinese patients. CONCLUSIONS: Comprehensive techniques such as WGS and Nanopore LRS warrants the identifying of GNE variants. Patients with the non-catalytic GNE variant, c.620A>T, had a milder disease progression and later wheelchair use.

A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation.

BACKGROUND: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. RESULTS: Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships.

Investigative power of genomic informational field theory relative to genome-wide association studies for genotype-phenotype mapping.

Identifying associations between phenotype and genotype is the fundamental basis of genetic analyses. Inspired by frequentist probability and the work of R. A. Fisher, genome-wide association studies (GWAS) extract information using averages and variances from genotype-phenotype datasets. Averages and variances are legitimated upon creating distribution density functions obtained through the grouping of data into categories. However, as data from within a given category cannot be differentiated, the investigative power of such methodologies is limited. Genomic informational field theory (GIFT) is a method specifically designed to circumvent this issue. The way GIFT proceeds is opposite to that of GWAS. Although GWAS determines the extent to which genes are involved in phenotype formation (bottom-up approach), GIFT determines the degree to which the phenotype can select microstates (genes) for its subsistence (top-down approach). Doing so requires dealing with new genetic concepts, a.k.a. genetic paths, upon which significance levels for genotype-phenotype associations can be determined. By using different datasets obtained in Ovis aries related to bone growth (dataset 1) and to a series of linked metabolic and epigenetic pathways (dataset 2), we demonstrate that removing the informational barrier linked to categories enhances the investigative and discriminative powers of GIFT, namely that GIFT extracts more information than GWAS. We conclude by suggesting that GIFT is an adequate tool to study how phenotypic plasticity and genetic assimilation are linked.NEW & NOTEWORTHY The genetic basis of complex traits remains challenging to investigate using classic genome-wide association studies (GWASs). Given the success of gene editing technologies, this point needs to be addressed urgently since there can only be useful editing technologies whether precise genotype-phenotype mapping information is available initially. Genomic informational field theory (GIFT) is a new mapping method designed to increase the investigative power of biological/medical datasets suggesting, in turn, the need to rethink the conceptual bases of quantitative genetics.

Investigation of genotype-phenotype and familial features of Turkish dystrophinopathy patients.

Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are X-linked recessive allelic muscle diseases caused by dystrophin gene mutations. Eight hundred thirty-seven patients admitted between 1997 and 2022 were included in the study. Two hundred twenty patients were analyzed by multiplex PCR (mPCR) alone. Five hundred ninety-five patients were investigated by multiplex ligation-dependent probe amplification (MLPA), and 54 patients were examined by sequencing. Deletion was detected in 60% (132/220) of the cases in the mPCR group only and in 58.3% (347/595) of the cases with MLPA analysis. The rates of deletion and duplication were 87.7% and 12.3%, respectively, in the MLPA analysis. Single exon deletions were the most common mutation type. The introns 43-55 (81.8%) and exons 2-21 (13.1%) regions were detected as hot spots in deletions. It was determined that 89% of the mutations were suitable for exon skipping therapy. The reading frame rule did not hold in 7.6% of D/BMD cases (17/224). We detected twenty-five pathogenic/likely pathogenic variants in sequencing, five of which were novel variants. Nonsense mutation was the most common small mutation (44%). 21% of DMD patients were familial. We detected germline mosaicism in four families (4.3%) in the large rearrangement group and one gonosomal mosaicism in a family with a nonsense mutation. This is the largest study examining genotype and phenotype data in Turkish D/BMD families investigated by MLPA analysis. The reading frame hypothesis is not valid in all cases. Sharing the genotype and phenotype characteristics of these cases in the literature will shed light on the molecular structure of DMD and guide gene therapy research. In genetic counseling, carrier screening in the family and possible gonadal mosaicism should be emphasized.

Distribution of pathogenic variants in the CFTR gene in a representative cohort of people with cystic fibrosis in the Kingdom of Bahrain.

BACKGROUND: Cystic fibrosis (CF) is a rare multi-systemic recessive disorder. The spectrum and the frequencies of CFTR mutations causing CF vary amongst different populations in Europe and the Middle East. In this study, we characterised the distribution of CF-causing mutations (i.e. pathogenic variants in the  CFTR gene) in a representative CF cohort from the Kingdom of Bahrain based on a three-decade-long analysis at a single tertiary centre. We aim to improve CF genetic diagnostics, introduce of CF neonatal screening and provide CFTR modulator therapy (CFTRm). METHODS: CFTR genotyping  and associated clinical information were drawn from a longitudinal cohort. We sequenced 56 people with CF (pwCF) that had one or both CFTR mutations unidentified and carried out comprehensive bioinformatic- and family-based segregation analyses of detected variants, including genotype-phenotype correlations and disease incidence estimates. The study methodology could serve as a basis for other non-European CF populations with a high degree of consanguinity. RESULTS: Altogether 18 CF-causing mutations  were identified, 15 of which were not previously detected in Bahrain, accounting for close to 100% of all population-specific alleles. The most common alleles comprise c.1911delG [2043delG; 22.8%], c.2988+1G > A [3120+1G>A; 16.3%], c.2989-1G>A [3121-1G>A; 14.1%], c.3909C>G [N1303K; 13.0%], and c.1521_1523delCTT [p.PheF508del; 7.6%]. Although the proportion of 1st cousin marriages has decreased to 50%, the frequency of homozygosity in our pwCF is 67.4%, thereby indicating that CF still occurs in large, often related, families. pwCF in Bahrain present with faltering growth, pancreatic insufficiency and classical sino-pulmonary manifestations. Interestingly, two pwCF also suffer from sickle cell disease. The estimated incidence of CF in Bahrain based on data from the last three decades is 1 in 9,880 live births. CONCLUSION: The most commonCF-causing  mutations in Bahraini pwCF were identified, enabling more precise diagnosis, introduction of two-tier neonatal screening and fostering administration of CFTRm.