Variant association tools for quality control and analysis of large-scale sequence and genotyping array data

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Exome sequencing implicates ancestry-related Mendelian variation at SYNE1 in childhood-onset essential hypertension

Childhood-onset essential hypertension (COEH) is an uncommon form of hypertension that manifests in childhood or adolescence and, in the United States, disproportionately affects children of African ancestry. The etiology of COEH is unknown, but its childhood onset, low prevalence, high heritability, and skewed ancestral demography suggest the potential to identify rare genetic variation segregating in a Mendelian manner among affected individuals and thereby implicate genes important to disease pathogenesis. However, no COEH genes have been reported to date. Here, we identify recessive segregation of rare and putatively damaging missense variation in the spectrin domain of spectrin repeat containing nuclear envelope protein 1 (SYNE1), a cardiovascular candidate gene, in 3 of 16 families with early-onset COEH without an antecedent family history. By leveraging exome sequence data from an additional 48 COEH families, 1,700 in-house trios, and publicly available data sets, we demonstrate that compound heterozygous SYNE1 variation in these COEH individuals occurred more often than expected by chance and that this class of biallelic rare variation was significantly enriched among individuals of African genetic ancestry. Using in vitro shRNA knockdown of SYNE1, we show that reduced SYNE1 expression resulted in a substantial decrease in the elasticity of smooth muscle vascular cells that could be rescued by pharmacological inhibition of the downstream RhoA/Rho-associated protein kinase pathway. These results provide insights into the molecular genetics and underlying pathophysiology of COEH and suggest a role for precision therapeutics in the future.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Changes in antioxidant status and DNA repair capacity are corroborated with molecular alterations in malignant thyroid tissue of patients with papillary thyroid cancer

Introduction: Papillary thyroid cancer (PTC) accounts for approximately 80% of all thyroid cancer cases. The mechanism of PTC tumourigenesis is not fully understood, but oxidative imbalance is thought to play a role. To gain further insight, this study evaluated antioxidant status, DNA repair capacity and genetic alterations in individuals diagnosed with benign thyroid lesion in one lobe (BTG) and PTC lesion in another. Methods: Individuals with coexisting BTG and PTC lesions in their thyroid lobes were included in this study. Reactive oxygen species (ROS) level, ABTS radical scavenging activity, ferric reducing antioxidant capacity, glutathione peroxidase and superoxide dismutase activities were measured in the thyroid tissue lysate. The expression of selected genes and proteins associated with oxidative stress defence and DNA repair were analysed through quantitative real-time PCR and Western blotting. Molecular alterations in genomic DNA were analysed through whole-exome sequencing and the potentially pathogenic driver genes filtered through Cancer-Related Analysis of Variants Toolkit (CRAVAT) analysis were subjected to pathway enrichment analysis using Metascape. Results: Significantly higher ROS level was detected in the PTC compared to the BTG lesions. The PTC lesions had significantly higher expression of GPX1, SOD2 and OGG1 but significantly lower expression of CAT and PRDX1 genes than the BTG lesions. Pathway enrichment analysis identified "regulation of MAPK cascade," "positive regulation of ERK1 and ERK2 cascade" and "negative regulation of reactive oxygen species metabolic process" to be significantly enriched in the PTC lesions only. Four pathogenic genetic variants were identified in the PTC lesions; BRAF V600E, MAP2K7-rs2145142862, BCR-rs372013175 and CD24 NM_001291737.1:p.Gln23fs while MAP3K9 and G6PD were among 11 genes that were mutated in both BTG and PTC lesions. Conclusion: Our findings provided further insight into the connection between oxidative stress, DNA damage, and genetic changes associated with BTG-to-PTC transformation. The increased oxidative DNA damage due to the heightened ROS levels could have heralded the BTG-to-PTC transformation, potentially through mutations in the genes involved in the MAPK signalling pathway and stress-activated MAPK/JNK cascade. Further in-vitro functional analyses and studies involving a larger sample size would need to be carried out to validate the findings from this pilot study.

In-silico identification and comparison of transcription factor binding sites cluster in anterior-posterior patterning genes in Drosophila melanogaster and Tribolium castaneum

The cis-regulatory data that help in transcriptional regulation is arranged into modular pieces of a few hundred base pairs called CRMs (cis-regulatory modules) and numerous binding sites for multiple transcription factors are prominent characteristics of these cis-regulatory modules. The present study was designed to localize transcription factor binding site (TFBS) clusters on twelve Anterior-posterior (A-P) genes in Tribolium castaneum and compare them to their orthologous gene enhancers in Drosophila melanogaster. Out of the twelve A-P patterning genes, six were gap genes (Kruppel, Knirps, Tailless, Hunchback, Giant, and Caudal) and six were pair rule genes (Hairy, Runt, Even-skipped, Fushi-tarazu, Paired, and Odd-skipped). The genes along with 20 kb upstream and downstream regions were scanned for TFBS clusters using the Motif Cluster Alignment Search Tool (MCAST), a bioinformatics tool that looks for set of nucleotide sequences for statistically significant clusters of non-overlapping occurrence of a given set of motifs. The motifs used in the current study were Hunchback, Caudal, Giant, Kruppel, Knirps, and Even-skipped. The results of the MCAST analysis revealed the maximum number of TFBS for Hunchback, Knirps, Caudal, and Kruppel in both D. melanogaster and T. castaneum, while Bicoid TFBS clusters were found only in D. melanogaster. The size of all the predicted TFBS clusters was less than 1kb in both insect species. These sequences revealed more transversional sites (Tv) than transitional sites (Ti) and the average Ti/Tv ratio was 0.75.

Buffering of genetic dominance by allele-specific protein complex assembly

Protein complex assembly often occurs while subunits are being translated, resulting in complexes whose subunits were translated from the same mRNA in an allele-specific manner. It has thus been hypothesized that such cotranslational assembly may counter the assembly-mediated dominant-negative effect, whereby co-assembly of mutant and wild-type subunits "poisons" complex activity. Here, we show that cotranslationally assembling subunits are much less likely to be associated with autosomal dominant relative to recessive disorders, and that subunits with dominant-negative disease mutations are significantly depleted in cotranslational assembly compared to those associated with loss-of-function mutations. We also find that complexes with known dominant-negative effects tend to expose their interfaces late during translation, lessening the likelihood of cotranslational assembly. Finally, by combining complex properties with other features, we trained a computational model for predicting proteins likely to be associated with non-loss-of-function disease mechanisms, which we believe will be of considerable utility for protein variant interpretation.

RAVAQ: An integrative pipeline from quality control to region-based rare variant association analysis

Next-generation sequencing technologies have opened up the possibility to sequence large samples of cases and controls to test for association with rare variants. To limit cost and increase sample sizes, data from controls could be used in multiple studies and might thus be generated on different sequencing platforms. This could pose some problems of comparability between cases and controls due to batch effects that could be confounding factors, leading to false-positive association signals. To limit batch effects and ensure comparability of datasets, stringent quality controls are required. We propose an integrative five-steps pipeline, RAVAQ, that (a) performs a specific three-step quality control taking into account the case-control status to ensure data comparability, (b) selects qualifying variants as defined by the user, and (c) performs rare variant association tests per genomic region. The RAVAQ pipeline is wrapped in an R package. It is user-friendly and flexible in its arguments to adapt to the specificity of each research project. We provide examples showing how RAVAQ improves rare variant association tests. The default RAVAQ quality control outperformed the widely used Variant Quality Score Recalibration method, removing inflation due to spurious signals. RAVAQ is open source and freely available at https://gitlab.com/gmarenne/ravaq.

Whole genome sequences of 234 indigenous African chickens from Ethiopia

Indigenous chickens predominate poultry production in Africa. Although preferred for backyard farming because of their adaptability to harsh tropical environments, these populations suffer from relatively low productivity compared to commercial lines. Genome analyses can unravel the genetic potential of improvement of these birds for both production and resilience traits for the benefit of African poultry farming systems. Here we report whole-genome sequences of 234 indigenous chickens from 24 Ethiopian populations distributed under diverse agro-climatic conditions. The data represents over eight terabytes of paired-end sequences from the Ilumina HiSeqX platform with an average coverage of about 57X. Almost 99% of the sequence reads could be mapped against the chicken reference genome (GRCg6a), confirming the high quality of the data. Variant calling detected around 15 million SNPs, of which about 86% are known variants (i.e., present in public databases), providing further confidence on the data quality. The dataset provides an excellent resource for investigating genetic diversity and local environmental adaptations with important implications for breed improvement and conservation purposes.

Measurement(s)	genome
Technology Type(s)	DNA sequencing
Factor Type(s)	animal population
Sample Characteristic - Organism	Gallus gallus
Sample Characteristic - Location	Ethiopia

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16999891

Cutevariant: a standalone GUI-based desktop application to explore genetic variations from an annotated VCF file

Summary

Cutevariant is a graphical user interface (GUI)-based desktop application designed to filter variations from annotated VCF file. The application imports data into a local SQLite database where complex filter queries can be built either from GUI controllers or using a domain-specific language called Variant Query Language. Cutevariant provides more features than existing applications and is fully customizable thanks to a complete plugins architecture.

Availability and implementation

Cutevariant is distributed as a multiplatform client-side software under an open source license and is available at https://github.com/labsquare/cutevariant.

A case of retinitis pigmentosa homozygous for a rare CNGA1 causal variant

Retinitis pigmentosa (RP) is a heterogenous hereditary disorder leading to blindness. Despite using next-generation sequencing technologies, causal variants in about 60% of RP cases remain unknown. The heterogeneous genetic inheritance pattern makes it difficult to pinpoint causal variants. Besides, rare penetrating variants are hardly observed in general case–control studies. Thus, a family-based analysis, specifically in a consanguineous family, is a clinically and genetically valuable approach for RP. We analyzed a Japanese consanguineous family with a member suffering from RP with a typical autosomal recessive pattern. We sequenced five direct descendants and spouse using Whole-exome sequencing (WES) and Whole-genome sequencing (WGS). We identified a homozygous pathogenic missense variant in CNGA1 (NM_000087.3, c.839G > A, p.Arg280His) in the proband, while we found no homozygous genotypes in the other family members. CNGA1 was previously reported to be associated with RP. We confirmed the genotypes by the Sanger sequencing. Additionally, we assessed the homozygous genotype in the proband for the possibility of a founder mutation using homozygosity analysis. Our results suggested the two copies of the variant derived from a founder mutation. In conclusion, we found the homozygotes for c.839G > A in CNGA1 as causal for RP.

Spatio-Temporal Mutational Profile Appearances of Swedish SARS-CoV-2 during the Early Pandemic

BACKGROUND

During the COVID-19 pandemic, the virus evolved, and we therefore aimed to provide an insight into which genetic variants were enriched, and how they spread in Sweden.

METHODS

We analyzed 348 Swedish SARS-CoV-2 sequences freely available from GISAID obtained from 7 February 2020 until 14 May 2020.

RESULTS

We identified 14 variant sites ≥5% frequency in the population. Among those sites, the D936Y substitution in the viral Spike protein was under positive selection. The variant sites can distinguish 11 mutational profiles in Sweden. Nine of the profiles appeared in Stockholm in March 2020. Mutational profiles 3 (B.1.1) and 6 (B.1), which contain the D936Y mutation, became the predominant profiles over time, spreading from Stockholm to other Swedish regions during April and the beginning of May. Furthermore, Bayesian phylogenetic analysis indicated that SARS-CoV-2 could have emerged in Sweden on 27 December 2019, and community transmission started on February 1st with an evolutionary rate of 1.5425 × 10-3 substitutions per year.

CONCLUSIONS

Our study provides novel knowledge on the spatio-temporal dynamics of Swedish SARS-CoV-2 variants during the early pandemic. Characterization of these viral variants can provide precious insights on viral pathogenesis and can be valuable for diagnostic and drug development approaches.

Whole Genome Sequencing of Four Representatives From the Admixed Population of the United Arab Emirates

Whole genome sequences (WGS) of four nationals of the United Arab Emirates (UAE) at an average coverage of 33X have been completed and described. The selection of suitable subpopulation representatives was informed by a preceding comprehensive population structure analysis. Representatives were chosen based on their central location within the subpopulation on a principal component analysis (PCA) and the degree to which they were admixed. Novel genomic variations among the different subgroups of the UAE population are reported here. Specifically, the WGS analysis identified 4,161,067-4,798,806 variants in the four individual samples, where approximately 80% were single nucleotide polymorphisms (SNPs) and 20% were insertions or deletions (indels). An average of 2.75% was found to be novel variants according to dbSNP (build 151). This is the first report of structural variants (SV) from WGS data from UAE nationals. There were 15,677-20,339 called SVs, of which around 13.5% were novel. The four samples shared 1,399,178 variants, each with distinct variants as follows: 1,085,524 (for the individual denoted as UAE S011), 1,228,559 (UAE S012), 791,072 (UAE S013), and 906,818 (UAE S014). These results show a previously unappreciated population diversity in the region. The synergy of WGS and genotype array data was demonstrated through variant annotation of the former using 2.3 million allele frequencies for the local population derived from the latter technology platform. This novel approach of combining breadth and depth of array and WGS technologies has guided the choice of population genetic representatives and provides complementary, regionalized allele frequency annotation to new genomes comprising millions of loci.

Genetic Diversity and Low Stratification of the Population of the United Arab Emirates

With high consanguinity rates on the Arabian Peninsula, it would not have been unexpected if the population of the United Arab Emirates (UAE) was shown to be relatively homogenous. However, this study of 1000 UAE nationals provided a contrasting perspective, one of a relatively heterogeneous population. Located at the apex of Europe, Asia, and Africa, the observed diversity could be explained by a plethora of migration patterns since the first Out-of-Africa movement. A strategy to explore the extent of genetic variation of the population of the UAE is presented. The first step involved a comprehensive population stratification study that was instructive for subsequent whole genome sequencing (WGS) of suitable representatives (which is described elsewhere). When these UAE data were compared to previous smaller studies from the region, the findings were consistent with a population that is a diverse and admixed group of people. However, rather than sharp and distinctive clusters, cluster analysis reveals low levels of stratification throughout the population. UAE emirates exhibit high within-Emirate-distance/among-Emirate distance ratios. Supervised admixture analysis showed a continuous gradient of ancestral populations, suggesting that admixture on the south eastern tip of the Arabian Peninsula occurred gradually. When visualized using a unique technique that combined admixture ratios and principal component analysis (PCA), unappreciated diversity was revealed while mitigating projection bias of conventional PCA. We observe low population stratification in the UAE in terms of homozygosity versus separation cluster coefficients. This holds for the UAE in a global context as well as for isolated cluster analysis of the Emirati birthplaces. However, the subtle clustering observed in the Emirates reflects geographic proximity and historic migration events. The analytical strategy used here highlights the complementary nature of data from genotype array and WGS for anthropological studies. Specifically, genotype array data were instructive to select representative subjects for WGS. Furthermore, from the 2.3 million allele frequencies obtained from genotype arrays, we identified 46,481 loci with allele frequencies that were significantly different with respect to other world populations. This comparison of allele frequencies facilitates variant prioritization in common diseases. In addition, these loci bear great potential as biomarkers in anthropological and forensic studies.

Rare variant association testing in the non-coding genome

The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.

Mitochondrial DNA heteroplasmy rises in substantial nigra of aged PINK1 KO mice

Mutations in PINK1 and Parkin result in early-onset autosomal recessive Parkinson's disease (PD). PINK1/Parkin pathway maintain mitochondrial function by mediating the clearance of damaged mitochondria. However, the role of PINK1/Parkin in maintaining the balance of mtDNA heteroplasmy is still unknown. Here, we isolated mitochondrial DNA (mtDNA) from cortex, striatum and substantia nigra of wildtype (WT), PINK1 knockout (PINK1 KO) and Parkin knockout (Parkin KO) mice to analyze mtDNA heteroplasmy induced by PINK1/Parkin deficiency or aging. Our results showed that the Single Nucleotide Variants (SNVs) of late-onset somatic variants mainly increased with aging. Conversely, the early-onset somatic variants exhibited significant increase in the cortex and substantia nigra of PINK1 KO mice than WT mice of the same age. Increased average variant allele frequency was observed in aged PINK1 KO mice and in substantial nigra of aged Parkin KO mice than in WT mice. Cumulative variant allele frequency in the substantia nigra of PINK1 KO mice was significantly higher than that in WT mice, further supporting the pivotal role of PINK1 in mtDNA maintenance. This study presented a new evidence for PINK1 and Parkin in participating in mitochondrial quality control and provided clues for further revealing the role of PINK1 and Parkin in the pathogenesis of PD.

Optimal sequencing depth design for whole genome re-sequencing in pigs

BACKGROUND

As whole-genome sequencing is becoming a routine technique, it is important to identify a cost-effective depth of sequencing for such studies. However, the relationship between sequencing depth and biological results from the aspects of whole-genome coverage, variant discovery power and the quality of variants is unclear, especially in pigs. We sequenced the genomes of three Yorkshire boars at an approximately 20X depth on the Illumina HiSeq X Ten platform and downloaded whole-genome sequencing data for three Duroc and three Landrace pigs with an approximately 20X depth for each individual. Then, we downsampled the deep genome data by extracting twelve different proportions of 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 paired reads from the original bam files to mimic the sequence data of the same individuals at sequencing depths of 1.09X, 2.18X, 3.26X, 4.35X, 6.53X, 8.70X, 10.88X, 13.05X, 15.22X, 17.40X, 19.57X and 21.75X to evaluate the influence of genome coverage, the variant discovery rate and genotyping accuracy as a function of sequencing depth. In addition, SNP chip data for Yorkshire pigs were used as a validation for the comparison of single-sample calling and multisample calling algorithms.

RESULTS

Our results indicated that 10X is an ideal practical depth for achieving plateau coverage and discovering accurate variants, which achieved greater than 99% genome coverage. The number of false-positive variants was increased dramatically at a depth of less than 4X, which covered 95% of the whole genome. In addition, the comparison of multi- and single-sample calling showed that multisample calling was more sensitive than single-sample calling, especially at lower depths. The number of variants discovered under multisample calling was 13-fold and 2-fold higher than that under single-sample calling at 1X and 22X, respectively. A large difference was observed when the depth was less than 4.38X. However, more false-positive variants were detected under multisample calling.

CONCLUSIONS

Our research will inform important study design decisions regarding whole-genome sequencing depth. Our results will be helpful for choosing the appropriate depth to achieve the same power for studies performed under limited budgets.

Behçet's disease and genetic interactions between HLA-B*51 and variants in genes of autoinflammatory syndromes

Behçet’s disease (BD) is an immune-mediated systemic disorder with a well-established genetic base. In a previous study, using a next generation sequencing approach, we found many rare variants and some functional polymorphisms in genes related to autoinflammatory syndromes (AID): CECR1, MEFV, MVK, NLRP3, NOD2, PSTPIP1 and TNFRSF1A in our BD cohort. Our strategy did not allow us to establish either number of patients with variants, proportion of individuals accumulating them or relationship with other genetic factors. With the goal to answer these questions, the individual samples were sequenced. Additionally, three functional polymorphisms: NLRP3 p.Gln703Lys, NOD2 p.Arg702Trp and p.Val955Ile were genotyped using TaqMan assays. A total of 98 patients (27.6%) carried at least one rare variant and 13 of them (3.7%) accumulated two or three. Functional regression model analysis suggests epistatic interaction between B51 and MEFV (P = 0.003). A suggestive protective association of the minor allele of NOD2 p.Arg702Trp (P = 0.01) was found in both, B51 positive and negative individuals. Therefore, a high percentage of patients with BD have rare variants in AID genes. Our results suggest that the association of MEFV with BD could be modulated by the HLA molecules; whereas the protective effect of NOD2 p.Arg702Trp would be independent of HLA.

Risk gene-set and pathways in 22q11.2 deletion-related schizophrenia: a genealogical molecular approach

The 22q11.2 deletion is a strong, but insufficient, "first hit" genetic risk factor for schizophrenia (SZ). We attempted to identify "second hits" from the entire genome in a unique multiplex 22q11.2 deletion syndrome (DS) family. Bioinformatic analysis of whole-exome sequencing and comparative-genomic hybridization array identified de novo and inherited, rare and damaging variants, including copy number variations, outside the 22q11.2 region. A specific 22q11.2-haplotype was associated with psychosis. The interaction of the identified "second hits" with the 22q11.2 haploinsufficiency may affect neurodevelopmental processes, including neuron projection, cytoskeleton activity, and histone modification in 22q11.2DS-ralated psychosis. A larger load of variants, involved in neurodevelopment, in combination with additional molecular events that affect sensory perception, olfactory transduction and G-protein-coupled receptor signaling may account for the development of 22q11.2DS-related SZ. Comprehensive analysis of multiplex families is a promising approach to the elucidation of the molecular pathophysiology of 22q11.2DS-related SZ with potential relevance to treatment.

Association of modifiers and other genetic factors explain Marfan syndrome clinical variability

Marfan syndrome (MFS) is a rare autosomal dominant connective tissue disorder related to variants in the FBN1 gene. Prognosis is related to aortic risk of dissection following aneurysm. MFS clinical variability is notable, for age of onset as well as severity and number of clinical manifestations. To identify genetic modifiers, we combined genome-wide approaches in 1070 clinically well-characterized FBN1 disease-causing variant carriers: (1) an FBN1 eQTL analysis in 80 fibroblasts of FBN1 stop variant carriers, (2) a linkage analysis, (3) a kinship matrix association study in 14 clinically concordant and discordant sib-pairs, (4) a genome-wide association study and (5) a whole exome sequencing in 98 extreme phenotype samples.Three genetic mechanisms of variability were found. A new genotype/phenotype correlation with an excess of loss-of-cysteine variants (P = 0.004) in severely affected subjects. A second pathogenic event in another thoracic aortic aneurysm gene or the COL4A1 gene (known to be involved in cerebral aneurysm) was found in nine individuals. A polygenic model involving at least nine modifier loci (named gMod-M1-9) was observed through cross-mapping of results. Notably, gMod-M2 which co-localizes with PRKG1, in which activating variants have already been described in thoracic aortic aneurysm, and gMod-M3 co-localized with a metalloprotease (proteins of extra-cellular matrix regulation) cluster. Our results represent a major advance in understanding the complex genetic architecture of MFS and provide the first steps toward prediction of clinical evolution.

Whole-Exome Sequencing Reveals Uncaptured Variation and Distinct Ancestry in the Southern African Population of Botswana

Large-scale, population-based genomic studies have provided a context for modern medical genetics. Among such studies, however, African populations have remained relatively underrepresented. The breadth of genetic diversity across the African continent argues for an exploration of local genomic context to facilitate burgeoning disease mapping studies in Africa. We sought to characterize genetic variation and to assess population substructure within a cohort of HIV-positive children from Botswana-a Southern African country that is regionally underrepresented in genomic databases. Using whole-exome sequencing data from 164 Batswana and comparisons with 150 similarly sequenced HIV-positive Ugandan children, we found that 13%-25% of variation observed among Batswana was not captured by public databases. Uncaptured variants were significantly enriched (p = 2.2 × 10^-16) for coding variants with minor allele frequencies between 1% and 5% and included predicted-damaging non-synonymous variants. Among variants found in public databases, corresponding allele frequencies varied widely, with Botswana having significantly higher allele frequencies among rare (<1%) pathogenic and damaging variants. Batswana clustered with other Southern African populations, but distinctly from 1000 Genomes African populations, and had limited evidence for admixture with extra-continental ancestries. We also observed a surprising lack of genetic substructure in Botswana, despite multiple tribal ethnicities and language groups, alongside a higher degree of relatedness than purported founder populations from the 1000 Genomes project. Our observations reveal a complex, but distinct, ancestral history and genomic architecture among Batswana and suggest that disease mapping within similar Southern African populations will require a deeper repository of genetic variation and allelic dependencies than presently exists.

Exome array analysis of adverse reactions to fluoropyrimidine-based therapy for gastrointestinal cancer

Fluoropyrimidines, including 5-fluororacil (5FU) and its pro-drug Capecitabine, are the common treatment for colorectal, breast, neck and head cancers—either as monotherapy or in combination therapy. Adverse reactions (ADRs) to the treatment are common and often result in treatment discontinuation or dose reduction. Factors contributing to ADRs, including genetic variation, are poorly characterized. We performed exome array analysis to identify genetic variants that contribute to adverse reactions. Our final dataset consisted of 504 European ancestry individuals undergoing fluoropyrimidine-based therapy for gastrointestinal cancer. A subset of 254 of these were treated with Capecitabine. All individuals were genotyped on the Illumina HumanExome Array. Firstly, we performed SNP and gene-level analyses of protein-altering variants on the array to identify novel associations the following ADRs, which were grouped into four phenotypes based on symptoms of diarrhea, mucositis, and neutropenia and hand-and-foot syndrome. Secondly, we performed detailed analyses of the HLA region on the same phenotypes after imputing the HLA alleles and amino acids. No protein-altering variants, or sets of protein-altering variants collapsed into genes, were associated with the main outcomes after Bonferroni correction. We found evidence that the HLA region was enriched for associations with Hand-and-Foot syndrome (p = 0.023), but no specific SNPs or HLA alleles were significant after Bonferroni correction. Larger studies will be required to characterize the genetic contribution to ADRs to 5FU. Future studies that focus on the HLA region are likely to be fruitful.

Loss-of-function of neuroplasticity-related genes confers risk for human neurodevelopmental disorders

High and increasing prevalence of neurodevelopmental disorders place enormous personal and economic burdens on society. Given the growing realization that the roots of neurodevelopmental disorders often lie in early childhood, there is an urgent need to identify childhood risk factors. Neurodevelopment is marked by periods of heightened experience-dependent neuroplasticity wherein neural circuitry is optimized by the environment. If these critical periods are disrupted, development of normal brain function can be permanently altered, leading to neurodevelopmental disorders. Here, we aim to systematically identify human variants in neuroplasticity-related genes that confer risk for neurodevelopmental disorders. Historically, this knowledge has been limited by a lack of techniques to identify genes related to neurodevelopmental plasticity in a high-throughput manner and a lack of methods to systematically identify mutations in these genes that confer risk for neurodevelopmental disorders. Using an integrative genomics approach, we determined loss-of-function (LOF) variants in putative plasticity genes, identified from transcriptional profiles of brain from mice with elevated plasticity, that were associated with neurodevelopmental disorders. From five shared differentially expressed genes found in two mouse models of juvenile-like elevated plasticity (juvenile wild-type or adult Lynx1-/- relative to adult wild-type) that were also genotyped in the Mount Sinai BioMe Biobank we identified multiple associations between LOF genes and increased risk for neurodevelopmental disorders across 10,510 patients linked to the Mount Sinai Electronic Medical Records (EMR), including epilepsy and schizophrenia. This work demonstrates a novel approach to identify neurodevelopmental risk genes and points toward a promising avenue to discover new drug targets to address the unmet therapeutic needs of neurodevelopmental disease.

Mutations in GREB1L Cause Bilateral Kidney Agenesis in Humans and Mice

Congenital anomalies of the kidney and urinary tract (CAKUT) constitute a major cause of chronic kidney disease in children and 20% of prenatally detected anomalies. CAKUT encompass a spectrum of developmental kidney defects, including renal agenesis, hypoplasia, and cystic and non-cystic dysplasia. More than 50 genes have been reported as mutated in CAKUT-affected case subjects. However, the pathophysiological mechanisms leading to bilateral kidney agenesis (BKA) remain largely elusive. Whole-exome or targeted exome sequencing of 183 unrelated familial and/or severe CAKUT-affected case subjects, including 54 fetuses with BKA, led to the identification of 16 heterozygous variants in GREB1L (growth regulation by estrogen in breast cancer 1-like), a gene reported as a target of retinoic acid signaling. Four loss-of-function and 12 damaging missense variants, 14 being absent from GnomAD, were identified. Twelve of them were present in familial or simplex BKA-affected case subjects. Female BKA-affected fetuses also displayed uterus agenesis. We demonstrated a significant association between GREB1L variants and BKA. By in situ hybridization, we showed expression of Greb1l in the nephrogenic zone in developing mouse kidney. We generated a Greb1l knock-out mouse model by CRISPR-Cas9. Analysis at E13.5 revealed lack of kidneys and genital tract anomalies in male and female Greb1l^-/- embryos and a slight decrease in ureteric bud branching in Greb1l^+/- embryos. We showed that Greb1l invalidation in mIMCD3 cells affected tubulomorphogenesis in 3D-collagen culture, a phenotype rescued by expression of the wild-type human protein. This demonstrates that GREB1L plays a major role in early metanephros and genital development in mice and humans.

Settling the score: variant prioritization and Mendelian disease

When investigating Mendelian disease using exome or genome sequencing, distinguishing disease-causing genetic variants from the multitude of candidate variants is a complex, multidimensional task. Many prioritization tools and online interpretation resources exist, and professional organizations have offered clinical guidelines for review and return of prioritization results. In this Review, we describe the strengths and weaknesses of widely used computational approaches, explain their roles in the diagnostic and discovery process and discuss how they can inform (and misinform) expert reviewers. We place variant prioritization in the wider context of gene prioritization, burden testing and genotype-phenotype association, and we discuss opportunities and challenges introduced by whole-genome sequencing.

Mutational profile of rare variants in inflammasome-related genes in Behçet disease: A Next Generation Sequencing approach

Behçet's disease (BD) is an immune-mediated systemic disorder with a well-established association with HLA class I and other genes. BD has clinical overlap with many autoinflammatory diseases (AIDs). The aim of this study was to investigate the role of rare variants in seven genes involved in AIDs: CECR1, MEFV, MVK, NLRP3, NOD2, PSTPIP1 and TNFRSF1A using a next generation sequencing (NGS) approach in 355 BD patients. To check global association of each gene, 4 tests: SKAT, CollapseBt, C(α) and weighted KBAC were used. Databases: 1000 Genomes Project Phase 3, Infevers, HGMD and ClinVar and algorithms: PolyPhen2 and SIFT were consulted to collect information of the 62 variants found. All the genes resulted associated using SKAT but only 3 (MVK, NOD2 and PSTPIP1) with C(α) and weighted KBAC. When all the genes are considered, 40 variants were associated to AIDs in clinical databases and 25 were predicted as pathogenic at least by one of the algorithms. Including only MVK, NOD2 and PSTPIP1, the associated to AIDs variants found in BD were 20 and the predicted as pathogenic, 12. The maxima contribution corresponds to NOD2. This study supports influence of rare variants in genes involved in AIDs in the pathogenesis of BD.

SEQSpark: A Complete Analysis Tool for Large-Scale Rare Variant Association Studies Using Whole-Genome and Exome Sequence Data

Massively parallel sequencing technologies provide great opportunities for discovering rare susceptibility variants involved in complex disease etiology via large-scale imputation and exome and whole-genome sequence-based association studies. Due to modest effect sizes, large sample sizes of tens to hundreds of thousands of individuals are required for adequately powered studies. Current analytical tools are obsolete when it comes to handling these large datasets. To facilitate the analysis of large-scale sequence-based studies, we developed SEQSpark which implements parallel processing based on Spark to increase the speed and efficiency of performing data quality control, annotation, and association analysis. To demonstrate the versatility and speed of SEQSpark, we analyzed whole-genome sequence data from the UK10K, testing for associations with waist-to-hip ratios. The analysis, which was completed in 1.5 hr, included loading data, annotation, principal component analysis, and single variant and rare variant aggregate association analysis of >9 million variants. For rare variant aggregate analysis, an exome-wide significant association (p < 2.5 × 10^-6) was observed with CCDC62 (SKAT-O [p = 6.89 × 10^-7], combined multivariate collapsing [p = 1.48 × 10^-6], and burden of rare variants [p = 1.48 × 10^-6]). SEQSpark was also used to analyze 50,000 simulated exomes and it required 1.75 hr for the analysis of a quantitative trait using several rare variant aggregate association methods. Additionally, the performance of SEQSpark was compared to Variant Association Tools and PLINK/SEQ. SEQSpark was always faster and in some situations computation was reduced to a hundredth of the time. SEQSpark will empower large sequence-based epidemiological studies to quickly elucidate genetic variation involved in the etiology of complex traits.

A burden of rare variants in BMPR2 and KCNK3 contributes to a risk of familial pulmonary arterial hypertension

Background

Pulmonary arterial hypertension (PAH) is a severe lung disease with only few effective treatments available. Familial cases of PAH are usually recognized as an autosomal dominant disease, but incomplete penetrance of the disease makes it difficult to identify pathogenic variants in accordance with a Mendelian pattern of inheritance.

Methods

To elucidate the complex genetic basis of PAH, we obtained whole exome- or genome-sequencing data of 17 subjects from 9 families with heritable PAH and applied gene-based association analysis with 9 index patients and 300 PAH-free controls.

Results

A burden of rare variants in BMPR2 significantly contributed to the risk of the disease (p = 6.0 × 10⁻⁸). Eight of nine families carried four previously reported single nucleotide variants and four novel insertion/deletion variants in the gene. One of the novel variants was a large 6.5 kilobase-deletion. In the remaining one family, the patient carried a pathogenic variant in a member of potassium channels, KCNK3, which was the first replicative finding of channelopathy in an Asian population.

Conclusions

The variety of rare pathogenic variants suggests that gene-based association analysis using genome-wide sequencing data from increased number of samples is essential to tracing the genetic heterogeneity and developing an appropriate panel for genetic testing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12890-017-0400-z) contains supplementary material, which is available to authorized users.

The Rare-Variant Generalized Disequilibrium Test for Association Analysis of Nuclear and Extended Pedigrees with Application to Alzheimer Disease WGS Data

Whole-genome and exome sequence data can be cost-effectively generated for the detection of rare-variant (RV) associations in families. Causal variants that aggregate in families usually have larger effect sizes than those found in sporadic cases, so family-based designs can be a more powerful approach than population-based designs. Moreover, some family-based designs are robust to confounding due to population admixture or substructure. We developed a RV extension of the generalized disequilibrium test (GDT) to analyze sequence data obtained from nuclear and extended families. The GDT utilizes genotype differences of all discordant relative pairs to assess associations within a family, and the RV extension combines the single-variant GDT statistic over a genomic region of interest. The RV-GDT has increased power by efficiently incorporating information beyond first-degree relatives and allows for the inclusion of covariates. Using simulated genetic data, we demonstrated that the RV-GDT method has well-controlled type I error rates, even when applied to admixed populations and populations with substructure. It is more powerful than existing family-based RV association methods, particularly for the analysis of extended pedigrees and pedigrees with missing data. We analyzed whole-genome sequence data from families affected by Alzheimer disease to illustrate the application of the RV-GDT. Given the capability of the RV-GDT to adequately control for population admixture or substructure and analyze pedigrees with missing genotype data and its superior power over other family-based methods, it is an effective tool for elucidating the involvement of RVs in the etiology of complex traits.

RNF213 Is Associated with Intracranial Aneurysms in the French-Canadian Population

Intracranial aneurysms (IAs) are the result of focal weakness in the artery wall and have a complex genetic makeup. To date, genome-wide association and sequencing studies have had limited success in identifying IA risk factors. Distinct populations, such as the French-Canadian (FC) population, have increased IA prevalence. In our study, we used exome sequencing to prioritize risk variants in a discovery cohort of six FC families affected by IA, and the analysis revealed an increased variation burden for ring finger protein 213 (RNF213). We resequenced RNF213 in a larger FC validation cohort, and association tests on further identified variants supported our findings (SKAT-O, p = 0.006). RNF213 belongs to the AAA+ protein family, and two variants (p.Arg2438Cys and p.Ala2826Thr) unique to affected FC individuals were found to have increased ATPase activity, which could lead to increased risk of IA by elevating angiogenic activities. Common SNPs in RNF213 were also extracted from the NeuroX SNP-chip genotype data, comprising 257 FC IA-affected and 1,988 control individuals. We discovered that the non-ancestral allele of rs6565666 was significantly associated with the affected individuals (p = 0.03), and it appeared as though the frequency of the risk allele had changed through genetic drift. Although RNF213 is a risk factor for moyamoya disease in East Asians, we demonstrated that it might also be a risk factor for IA in the FC population. It therefore appears that the function of RNF213 can be differently altered to predispose distinct populations to dissimilar neurovascular conditions, highlighting the importance of a population's background in genetic studies of heterogeneous disease.

Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. In this paper, we annotate non-synonymous single-nucleotide variants according to protein binding sites (BSs) by using a more accurate BS prediction method. We then incorporate this information into association study through a statistical framework of likelihood ratio test (LRT) based on weighted burden score of single-nucleotide variants (SNVs). The strategy is applied to Genetic Analysis Workshop 19 exome-sequencing data for detecting novel genes associated to hypotension. The SNV-weighting LRT idea is empirically verified by the simulated phenotypes (336 cases and 1607 controls), and the weights based on BS annotation are applied to the real phenotypes (394 cases and 1457 controls). Such strategy of weighting the prior information on protein functional sites is shown to be superior to the unweighted LRT and serves as a good complement to the existing association tests. Several putative genes are reported; some of them are functionally related to hypertension according to the previous evidence in the literature.

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine

OBJECTIVE

Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare.

METHODS

In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling.

RESULTS

To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR.

CONCLUSION

Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine.

SIGNIFICANCE

Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

Guidelines for Large-Scale Sequence-Based Complex Trait Association Studies: Lessons Learned from the NHLBI Exome Sequencing Project

Massively parallel whole-genome sequencing (WGS) data have ushered in a new era in human genetics. These data are now being used to understand the role of rare variants in complex traits and to advance the goals of precision medicine. The technological and computing advances that have enabled us to generate WGS data on thousands of individuals have also outpaced our ability to perform analyses in scientifically and statistically rigorous and thoughtful ways. The past several years have witnessed the application of whole-exome sequencing (WES) to complex traits and diseases. From our analysis of NHLBI Exome Sequencing Project (ESP) data, not only have a number of important disease and complex trait association findings emerged, but our collective experience offers some valuable lessons for WGS initiatives. These include caveats associated with generating automated pipelines for quality control and analysis of rare variants; the importance of studying minority populations; sample size requirements and efficient study designs for identifying rare-variant associations; and the significance of incidental findings in population-based genetic research. With the ESP as an example, we offer guidance and a framework on how to conduct a large-scale association study in the era of WGS.

Rare genetic variation in UNC13A may modify survival in amyotrophic lateral sclerosis

Our objective was to identify whether rare genetic variation in amyotrophic lateral sclerosis (ALS) candidate survival genes modifies ALS survival. Candidate genes were selected based on evidence for modifying ALS survival. Each tail of the extreme 1.5% of survival was selected from the UK MND DNA Bank and all samples available underwent whole genome sequencing. A replication set from the Netherlands was used for validation. Sequences of candidate survival genes were extracted and variants passing quality control with a minor allele frequency ≤0.05 were selected for association testing. Analysis was by burden testing using SKAT. Candidate survival genes UNC13A, KIFAP3, and EPHA4 were tested for association in a UK sample comprising 25 short survivors and 25 long survivors. Results showed that only SNVs in UNC13A were associated with survival (p = 6.57 × 10-3). SNV rs10419420:G > A was found exclusively in long survivors (3/25) and rs4808092:G > A exclusively in short survivors (4/25). These findings were not replicated in a Dutch sample. In conclusion, population specific rare variants of UNC13A may modulate survival in ALS.

SORL1 rare variants: a major risk factor for familial early-onset Alzheimer's disease

The SORL1 protein plays a protective role against the secretion of the amyloid β peptide, a key event in the pathogeny of Alzheimer's disease. We assessed the impact of SORL1 rare variants in early-onset Alzheimer's disease (EOAD) in a case-control setting. We conducted a whole exome analysis among 484 French EOAD patients and 498 ethnically matched controls. After collapsing rare variants (minor allele frequency ≤1%), we detected an enrichment of disruptive and predicted damaging missense SORL1 variants in cases (odds radio (OR)=5.03, 95% confidence interval (CI)=(2.02-14.99), P=7.49.10(-5)). This enrichment was even stronger when restricting the analysis to the 205 cases with a positive family history (OR=8.86, 95% CI=(3.35-27.31), P=3.82.10(-7)). We conclude that predicted damaging rare SORL1 variants are a strong risk factor for EOAD and that the association signal is mainly driven by cases with positive family history.

RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data

MOTIVATION

Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data.

AVAILABILITY AND IMPLEMENTATION

RVTESTS is available on Linux, MacOS and Windows. Source code and executable files can be obtained at https://github.com/zhanxw/rvtests

CONTACT

zhanxw@gmail.com; goncalo@umich.edu; dajiang.liu@outlook.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evidence for Association Between Low Frequency Variants in CHRNA6/CHRNB3 and Antisocial Drug Dependence

Common SNPs in nicotinic acetylcholine receptor genes (CHRN genes) have been associated with drug behaviors and personality traits, but the influence of rare genetic variants is not well characterized. The goal of this project was to identify novel rare variants in CHRN genes in the Center for Antisocial Drug Dependence (CADD) and Genetics of Antisocial Drug Dependence (GADD) samples and to determine if low frequency variants are associated with antisocial drug dependence. Two samples of 114 and 200 individuals were selected using a case/control design including the tails of the phenotypic distribution of antisocial drug dependence. The capture, sequencing, and analysis of all variants in 16 CHRN genes (CHRNA1-7, 9, 10, CHRNB1-4, CHRND, CHRNG, CHRNE) were performed independently for each subject in each sample. Sequencing reads were aligned to the human reference sequence using BWA prior to variant calling with the Genome Analysis ToolKit (GATK). Low frequency variants (minor allele frequency < 0.05) were analyzed using SKAT-O and C-alpha to examine the distribution of rare variants among cases and controls. In our larger sample, the region containing the CHRNA6/CHRNB3 gene cluster was significantly associated with disease status using both SKAT-O and C-alpha (unadjusted p values <0.05). More low frequency variants in the CHRNA6/CHRNB3 gene region were observed in cases compared to controls. These data support a role for genetic variants in CHRN genes and antisocial drug behaviors.

Strategies for Imputing and Analyzing Rare Variants in Association Studies

Rare genetic variants may be responsible for a significant amount of the uncharacterized genetic risk underlying many diseases. An efficient approach to characterizing the disease burden of rare variants may be to impute them into existing large datasets. It is well known that the ability to impute a rare variant is dependent both on the array choice and number of individuals in the reference panel carrying that variant, although it is still unclear exactly how well imputation will work for rare variants. Here, we review the additional challenges that arise when imputing rare variants, looking at studies that have been able to impute rare variants, methods behind merging reference panels, approaches for imputing rare variants, and methods for analyzing rare variants.

ABCA7 rare variants and Alzheimer disease risk

OBJECTIVE

To study the association between ABCA7 rare coding variants and Alzheimer disease (AD) in a case-control setting.

METHODS

We conducted a whole exome analysis among 484 French patients with early-onset AD and 590 ethnically matched controls.

RESULTS

After collapsing rare variants (minor allele frequency ≤1%), we detected an enrichment of ABCA7 loss of function (LOF) and predicted damaging missense variants in cases (odds ratio [OR] 3.40, 95% confidence interval [CI] 1.68-7.35, p = 0.0002). Performing a meta-analysis with previously published data, we found that in a combined sample of 1,256 patients and 1,347 controls from France and Belgium, the OR was 2.81 (95% CI 1.89-4.20, p = 3.60 × 10(-7)).

CONCLUSIONS

These results confirm that ABCA7 LOF variants are enriched in patients with AD and extend this finding to predicted damaging missense variants.

Rare variant associations with waist-to-hip ratio in European-American and African-American women from the NHLBI-Exome Sequencing Project

Waist-to-hip ratio (WHR), a relative comparison of waist and hip circumferences, is an easily accessible measurement of body fat distribution, in particular central abdominal fat. A high WHR indicates more intra-abdominal fat deposition and is an established risk factor for cardiovascular disease and type 2 diabetes. Recent genome-wide association studies have identified numerous common genetic loci influencing WHR, but the contributions of rare variants have not been previously reported. We investigated rare variant associations with WHR in 1510 European-American and 1186 African-American women from the National Heart, Lung, and Blood Institute-Exome Sequencing Project. Association analysis was performed on the gene level using several rare variant association methods. The strongest association was observed for rare variants in IKBKB (P=4.0 × 10(-8)) in European-Americans, where rare variants in this gene are predicted to decrease WHRs. The activation of the IKBKB gene is involved in inflammatory processes and insulin resistance, which may affect normal food intake and body weight and shape. Meanwhile, aggregation of rare variants in COBLL1, previously found to harbor common variants associated with WHR and fasting insulin, were nominally associated (P=2.23 × 10(-4)) with higher WHR in European-Americans. However, these significant results are not shared between African-Americans and European-Americans that may be due to differences in the allelic architecture of the two populations and the small sample sizes. Our study indicates that the combined effect of rare variants contribute to the inter-individual variation in fat distribution through the regulation of insulin response.

The Genetic Basis of Mendelian Phenotypes: Discoveries, Challenges, and Opportunities

Discovering the genetic basis of a Mendelian phenotype establishes a causal link between genotype and phenotype, making possible carrier and population screening and direct diagnosis. Such discoveries also contribute to our knowledge of gene function, gene regulation, development, and biological mechanisms that can be used for developing new therapeutics. As of February 2015, 2,937 genes underlying 4,163 Mendelian phenotypes have been discovered, but the genes underlying ∼50% (i.e., 3,152) of all known Mendelian phenotypes are still unknown, and many more Mendelian conditions have yet to be recognized. This is a formidable gap in biomedical knowledge. Accordingly, in December 2011, the NIH established the Centers for Mendelian Genomics (CMGs) to provide the collaborative framework and infrastructure necessary for undertaking large-scale whole-exome sequencing and discovery of the genetic variants responsible for Mendelian phenotypes. In partnership with 529 investigators from 261 institutions in 36 countries, the CMGs assessed 18,863 samples from 8,838 families representing 579 known and 470 novel Mendelian phenotypes as of January 2015. This collaborative effort has identified 956 genes, including 375 not previously associated with human health, that underlie a Mendelian phenotype. These results provide insight into study design and analytical strategies, identify novel mechanisms of disease, and reveal the extensive clinical variability of Mendelian phenotypes. Discovering the gene underlying every Mendelian phenotype will require tackling challenges such as worldwide ascertainment and phenotypic characterization of families affected by Mendelian conditions, improvement in sequencing and analytical techniques, and pervasive sharing of phenotypic and genomic data among researchers, clinicians, and families.

Rare A2ML1 variants confer susceptibility to otitis media

A duplication variant within the middle ear-specific gene A2ML1 cosegregates with otitis media in an indigenous Filipino pedigree (LOD score = 7.5 at reduced penetrance) and lies within a founder haplotype that is also shared by 3 otitis-prone European-American and Hispanic-American children but is absent in non-otitis-prone children and >62,000 next-generation sequences. We identified seven additional A2ML1 variants in six otitis-prone children. Collectively, our studies support a role for A2ML1 in the pathophysiology of otitis media.

Insights from exome sequencing for endocrine disorders

Whole-exome sequencing has emerged as a fast and effective tool for the elucidation of genetic defects underlying both rare and common human diseases. Increased availability and decreased costs of next-generation sequencing have enabled investigators to use this approach not only in individual patients with rare diseases, but also to screen large cohorts or populations for the genetic determinants of diseases. Within the field of endocrinology, exome sequencing has led to major advancements in our understanding of many disorders including adrenal disease, growth and puberty disorders and type 2 diabetes mellitus, as well as a multitude of rare genetic syndromes with prominent endocrine involvement. In this Review, we provide an overview of these new insights and discuss the role that exome sequencing is expected to have in endocrine research and future clinical practice.

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs

There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations-MASS, RAREMETAL, MetaSKAT, and seqMeta-in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.

Systematic Validation of RNF213 Coding Variants in Japanese Patients With Moyamoya Disease

Background

A founder variant of RNF213, p.R4810K (c.14429G>A, rs112735431), was recently identified as a major genetic risk factor for moyamoya disease (MMD) in Japan. Although the association of p.R4810K was reported to be highly significant and reproducible, the disease susceptibility of other RNF213 variants remains largely unknown. In the present study, we systematically evaluated the coding variants detected in Japanese patients and controls for associations with MMD.

Methods and Results

To detect variants of RNF213, all coding exons were sequenced in 27 Japanese MMD patients without p.R4810K. We also validated all previously reported variants in our case–control samples and tested for associations in combination with previous Japanese study cohorts, including the 1000 Genomes Project data set, as population-based controls. Forty-six missense variants other than p.R4810K were identified among 370 combined patients and 279 combined controls in Japan. Sixteen of 46 variants were polymorphisms with minor allele frequency >1%, and, after conditioning on the p.R4810K genotype, were not associated with MMD. We conducted a variable threshold test using Combined Annotation-Dependent Depletion on the remaining 30 rare variants (minor allele frequency <1%), and the results showed that the frequency of potentially functional variants was significantly higher in patients than in controls (permutation, minimum P=0.045).

Conclusions

Not only p.4810K but also other functional missense variants of RNF213 conferred susceptibility to MMD. Our analysis also revealed that ≈20% of Japanese MMD patients did not harbor susceptibility variants of RNF213, indicating the presence of other susceptibility genes for MMD.

Genetic linkage analysis in the age of whole-genome sequencing

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data.