Heterozygous loss of WBP11 function causes multiple congenital defects in humans and mice.

The genetic causes of multiple congenital anomalies are incompletely understood. Here, we report novel heterozygous predicted loss-of-function (LoF) and predicted damaging missense variants in the WW domain binding protein 11 (WBP11) gene in seven unrelated families with a variety of overlapping congenital malformations, including cardiac, vertebral, tracheo-esophageal, renal and limb defects. WBP11 encodes a component of the spliceosome with the ability to activate pre-messenger RNA splicing. We generated a Wbp11 null allele in mouse using CRISPR-Cas9 targeting. Wbp11 homozygous null embryos die prior to E8.5, indicating that Wbp11 is essential for development. Fewer Wbp11 heterozygous null mice are found than expected due to embryonic and postnatal death. Importantly, Wbp11 heterozygous null mice are small and exhibit defects in axial skeleton, kidneys and esophagus, similar to the affected individuals, supporting the role of WBP11 haploinsufficiency in the development of congenital malformations in humans. LoF WBP11 variants should be considered as a possible cause of VACTERL association as well as isolated Klippel-Feil syndrome, renal agenesis or esophageal atresia.

Insights into the genetic architecture underlying complex, critical congenital heart disease.

Congenital heart disease (CHD) has a multifactorial aetiology, raising the possibility of an underlying genetic burden, predisposing to disease but also variable expression, including variation in disease severity, and incomplete penetrance. Using whole genome sequencing (WGS), the findings of this study, indicate that complex, critical CHD is distinct from other types of disease due to increased genetic burden in common variation, specifically among established CHD genes. Additionally, these findings highlight associations with regulatory genes and environmental "stressors" in the final presentation of disease.

Whole genome sequencing in transposition of the great arteries and associations with clinically relevant heart, brain and laterality genes.

BACKGROUND: The most common cyanotic congenital heart disease (CHD) requiring management as a neonate is transposition of great arteries (TGA). Clinically, up to 50% of TGA patients develop some form of neurodevelopmental disability (NDD), thought to have a significant genetic component. A "ciliopathy" and links with laterality disorders have been proposed. This first report of whole genome sequencing in TGA, sought to identify clinically relevant variants contributing to heart, brain and laterality defects. METHODS: Initial whole genome sequencing analyses on 100 TGA patients focussed on established disease genes related to CHD (n = 107), NDD (n = 659) and heterotaxy (n = 74). Single variant as well as copy number variant analyses were conducted. Variant pathogenicity was assessed using the American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines. RESULTS: Fifty-five putatively damaging variants were identified in established disease genes associated with CHD, NDD and heterotaxy; however, no clinically relevant variants could be attributed to disease. Notably, case-control analyses identified significantly more predicted-damaging, silent and total variants in TGA cases than healthy controls in established CHD genes (P < .001), NDD genes (P < .001) as well as across the three gene panels (P < .001). CONCLUSION: We present compelling evidence that the majority of TGA is not caused by monogenic rare variants and is most likely oligogenic and/or polygenic in nature, highlighting the complex genetic architecture and multifactorial influences on this CHD sub-type and its long-term sequelae. Assessment of variant burden in key heart, brain and/or laterality genes may be required to unravel the genetic contributions to TGA and related disabilities.

dv-trio: a family-based variant calling pipeline using DeepVariant.

MOTIVATION: In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery. RESULTS: To further improve DeepVariant's variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant. AVAILABILITY AND IMPLEMENTATION: dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/). CONTACT: e.giannoulatou@victorchang.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NAD Deficiency, Congenital Malformations, and Niacin Supplementation.

BACKGROUND: Congenital malformations can be manifested as combinations of phenotypes that co-occur more often than expected by chance. In many such cases, it has proved difficult to identify a genetic cause. We sought the genetic cause of cardiac, vertebral, and renal defects, among others, in unrelated patients. METHODS: We used genomic sequencing to identify potentially pathogenic gene variants in families in which a person had multiple congenital malformations. We tested the function of the variant by using assays of in vitro enzyme activity and by quantifying metabolites in patient plasma. We engineered mouse models with similar variants using the CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 system. RESULTS: Variants were identified in two genes that encode enzymes of the kynurenine pathway, 3-hydroxyanthranilic acid 3,4-dioxygenase (HAAO) and kynureninase (KYNU). Three patients carried homozygous variants predicting loss-of-function changes in the HAAO or KYNU proteins (HAAO p.D162*, HAAO p.W186*, or KYNU p.V57Efs*21). Another patient carried heterozygous KYNU variants (p.Y156* and p.F349Kfs*4). The mutant enzymes had greatly reduced activity in vitro. Nicotinamide adenine dinucleotide (NAD) is synthesized de novo from tryptophan through the kynurenine pathway. The patients had reduced levels of circulating NAD. Defects similar to those in the patients developed in the embryos of Haao-null or Kynu-null mice owing to NAD deficiency. In null mice, the prevention of NAD deficiency during gestation averted defects. CONCLUSIONS: Disruption of NAD synthesis caused a deficiency of NAD and congenital malformations in humans and mice. Niacin supplementation during gestation prevented the malformations in mice. (Funded by the National Health and Medical Research Council of Australia and others.).

Spliceogen: an integrative, scalable tool for the discovery of splice-altering variants.

MOTIVATION: In silico prediction tools are essential for identifying variants which create or disrupt cis-splicing motifs. However, there are limited options for genome-scale discovery of splice-altering variants. RESULTS: We have developed Spliceogen, a highly scalable pipeline integrating predictions from some of the individually best performing models for splice motif prediction: MaxEntScan, GeneSplicer, ESRseq and Branchpointer. AVAILABILITY AND IMPLEMENTATION: Spliceogen is available as a command line tool which accepts VCF/BED inputs and handles both single nucleotide variants (SNVs) and indels (https://github.com/VCCRI/Spliceogen). SNV databases with prediction scores are also available, covering all possible SNVs at all genomic positions within all Gencode-annotated multi-exon transcripts. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of clinically actionable variants from genome sequencing of families with congenital heart disease.

PURPOSE: Congenital heart disease (CHD) affects up to 1% of live births. However, a genetic diagnosis is not made in most cases. The purpose of this study was to assess the outcomes of genome sequencing (GS) of a heterogeneous cohort of CHD patients. METHODS: Ninety-seven families with probands born with CHD requiring surgical correction were recruited for genome sequencing. At minimum, a proband-parents trio was sequenced per family. GS data were analyzed via a two-tiered method: application of a high-confidence gene screen (hcCHD), and comprehensive analysis. Identified variants were assessed for pathogenicity using the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines. RESULTS: Clinically relevant genetic variants in known and emerging CHD genes were identified. The hcCHD screen identified a clinically actionable variant in 22% of families. Subsequent comprehensive analysis identified a clinically actionable variant in an additional 9% of families in genes with recent disease associations. Overall, this two-tiered approach provided a clinically relevant variant for 31% of families. CONCLUSIONS: Interrogating GS data using our two-tiered method allowed identification of variants with high clinical utility in a third of our heterogeneous cohort. However, association of emerging genes with CHD etiology, and development of novel technologies for variant assessment and interpretation, will increase diagnostic yield during future reassessment of our GS data.

Genetic burden and associations with adverse neurodevelopment in neonates with congenital heart disease.

BACKGROUND: Up to 20% of children with congenital heart disease (CHD) undergoing cardiac surgery develop neurodevelopmental disabilities (NDD), with some studies reporting persistent impairment. Recent large-scale studies have demonstrated shared genetic mechanisms contributing to CHD and NDD. In this study, a targeted approach was applied to assess direct clinical applicability of this information. METHODS: A gene panel comprising 148 known CHD and/or NDD genes was used to sequence 15 patients with CHD + NDD, 15 patients with CHD, and 15 healthy controls. The number and types of variants between the 3 groups were compared using Poisson log-linear regression, and the SNP-set (Sequence) Kernel Association Test-Optimized was used to conduct single-gene and gene-pathway burden analyses. RESULTS: A significant increase in rare (minor allele frequency < 0.01) and novel variants was identified between the CHD + NDD cohort and controls, P < .001 and P = .001, respectively. There was also a significant increase in rare variants in the CHD cohort compared with controls (P = .04). Rare variant burden analyses implicated pathways associated with "neurotransmitters," "axon guidance," and those incorporating "RASopathy" genes in the development of NDD in CHD patients. CONCLUSIONS: These findings suggest that an increase in novel and rare variants in known CHD and/or NDD genes is associated with the development of NDD in patients with CHD. Furthermore, burden analyses point toward rare variant burden specifically in pathways related to brain development and function as contributors to NDD. Although promising variants and pathways were identified, further research, utilizing whole-genome approaches, is required prior to demonstrating clinical utility in this patient group.

Cultural basis for diabetes-related beliefs among low- and high-education African American, American Indian, and white older adults.

OBJECTIVES: Racial and ethnic disparities in diabetes and subsequent complications are often attributed to culture; however, previous diabetes disparities research is restricted to in-depth ethnic-specific samples or to comparative study designs with limited belief assessment. The goal of our study was to improve understanding of the cultural basis for variation in diabetes beliefs. DESIGN: Cross-sectional. SETTING: Rural North Carolina. PARTICIPANTS: Older adults (aged 60+) with diabetes, equally divided by ethnicity (White, African American, American Indian) and sex (N=593). INTERVENTIONS: Guided by Explanatory Models of Illness and Cultural Consensus research traditions, trained interviewers collected data using 38 items in four diabetes belief domains: causes, symptoms, consequences, and medical management. Items were obtained from the Common Sense Model of Diabetes Inventory (CSMDI). MAIN OUTCOME: Beliefs about diabetes. Response options for each diabetes belief item were "agree," "disagree" and "don't know." Collected data were analyzed using Anthropac (version 4.98) and Latent Gold (version 4.5) programs. RESULTS: There is substantial similarity in diabetes beliefs among African Americans, American Indians and Whites. Diabetes beliefs were most similar in the symptoms and consequences domains compared to beliefs pertaining to causes and medical management. Although some discrete beliefs differed by ethnicity, systematic differences by ethnicity were observed for specific educational groups. CONCLUSIONS: Socioeconomic conditions influence diabetes beliefs rather than ethnicity per se.

Obesity educational interventions in U.S. medical schools: a systematic review and identified gaps.

BACKGROUND: Obesity is the second leading cause of preventable death in the United States. However, physicians feel poorly trained to address the obesity epidemic. This article examines effective training methods for overweight and obesity intervention in undergraduate medical education. Using indexing terms related to overweight, obesity, and medical student education, we conducted a literature searched PubMed PsycINFO, Cochrane, and ERIC for relevant articles in English. References from articles identified were also reviewed to located additional articles. SUMMARY: We included all studies that incorporated process or outcome evaluations of obesity educational interventions for U.S. medical students. Of an initial 168 citations, 40 abstracts were retrieved; 11 studies were found to be pertinent to medical student obesity education, but only 5 included intervention and evaluation elements. Quality criteria for inclusion consisted of explicit evaluation of the educational methods used. Data extraction identified participants (e.g., year of medical students), interventions, evaluations, and results. These 5 studies successfully used a variety of teaching methods including hands on training, didactic lectures, role-playing, and standardized patient interaction to increase medical students' knowledge, attitudes, and skills regarding overweight and obesity intervention. Two studies addressed medical student bias toward overweight and obese patients. No studies addressed health disparities in the epidemiology and bias of obesity. CONCLUSIONS: Despite the commonly cited "obesity epidemic," there are very few published studies that report the effectiveness of medical school obesity educational programs. Gaps still exist within undergraduate medical education including specific training that addresses obesity and long-term studies showing that such training is retained.

Benchmarking the Effectiveness and Accuracy of Multiple Mitochondrial DNA Variant Callers: Practical Implications for Clinical Application.

Mitochondrial DNA (mtDNA) mutations contribute to human disease across a range of severity, from rare, highly penetrant mutations causal for monogenic disorders to mutations with milder contributions to phenotypes. mtDNA variation can exist in all copies of mtDNA or in a percentage of mtDNA copies and can be detected with levels as low as 1%. The large number of copies of mtDNA and the possibility of multiple alternative alleles at the same DNA nucleotide position make the task of identifying allelic variation in mtDNA very challenging. In recent years, specialized variant calling algorithms have been developed that are tailored to identify mtDNA variation from whole-genome sequencing (WGS) data. However, very few studies have systematically evaluated and compared these methods for the detection of both homoplasmy and heteroplasmy. A publicly available synthetic gold standard dataset was used to assess four mtDNA variant callers (Mutserve, mitoCaller, MitoSeek, and MToolBox), and the commonly used Genome Analysis Toolkit "best practices" pipeline, which is included in most current WGS pipelines. We also used WGS data from 126 trios and calculated the percentage of maternally inherited variants as a metric of calling accuracy, especially for homoplasmic variants. We additionally compared multiple pathogenicity prediction resources for mtDNA variants. Although the accuracy of homoplasmic variant detection was high for the majority of the callers with high concordance across callers, we found a very low concordance rate between mtDNA variant callers for heteroplasmic variants ranging from 2.8% to 3.6%, for heteroplasmy thresholds of 5% and 1%. Overall, Mutserve showed the best performance using the synthetic benchmark dataset. The analysis of mtDNA pathogenicity resources also showed low concordance in prediction results. We have shown that while homoplasmic variant calling is consistent between callers, there remains a significant discrepancy in heteroplasmic variant calling. We found that resources like population frequency databases and pathogenicity predictors are now available for variant annotation but still need refinement and improvement. With its peculiarities, the mitochondria require special considerations, and we advocate that caution needs to be taken when analyzing mtDNA data from WGS data.

VPOT: A Customizable Variant Prioritization Ordering Tool for Annotated Variants.

Next-generation sequencing (NGS) technologies generate thousands to millions of genetic variants per sample. Identification of potential disease-causal variants is labor intensive as it relies on filtering using various annotation metrics and consideration of multiple pathogenicity prediction scores. We have developed VPOT (variant prioritization ordering tool), a python-based command line tool that allows researchers to create a single fully customizable pathogenicity ranking score from any number of annotation values, each with a user-defined weighting. The use of VPOT can be informative when analyzing entire cohorts, as variants in a cohort can be prioritized. VPOT also provides additional functions to allow variant filtering based on a candidate gene list or by affected status in a family pedigree. VPOT outperforms similar tools in terms of efficacy, flexibility, scalability, and computational performance. VPOT is freely available for public use at GitHub (https://github.com/VCCRI/VPOT/). Documentation for installation along with a user tutorial, a default parameter file, and test data are provided.

A Screening Approach to Identify Clinically Actionable Variants Causing Congenital Heart Disease in Exome Data.

BACKGROUND: Congenital heart disease (CHD)-structural abnormalities of the heart that arise during embryonic development-is the most common inborn malformation, affecting ≤1% of the population. However, currently, only a minority of cases can be explained by genetic abnormalities. The goal of this study was to identify disease-causal genetic variants in 30 families affected by CHD. METHODS: Whole-exome sequencing was performed with the DNA of multiple family members. We utilized a 2-tiered whole-exome variant screening and interpretation procedure. First, we manually curated a high-confidence list of 90 genes known to cause CHD in humans, identified predicted damaging variants in genes on this list, and rated their pathogenicity using American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines. RESULTS: In 3 families (10%), we found pathogenic variants in known CHD genes TBX5, TFAP2B, and PTPN11, explaining the cardiac lesions. Second, exomes were comprehensively analyzed to identify additional predicted damaging variants that segregate with disease in CHD candidate genes. In 10 additional families (33%), likely disease-causal variants were uncovered in PBX1, CNOT1, ZFP36L2, TEK, USP34, UPF2, KDM5A, KMT2C, TIE1, TEAD2, and FLT4. CONCLUSIONS: The pathogenesis of CHD could be explained using our high-confidence CHD gene list for variant filtering in a subset of cases. Furthermore, our unbiased screening procedure of family exomes implicates additional genes and variants in the pathogenesis of CHD, which suggest themselves for functional validation. This 2-tiered approach provides a means of (1) identifying clinically actionable variants and (2) identifying additional disease-causal genes, both of which are essential for improving the molecular diagnosis of CHD.