Ultra-rare disruptive and damaging mutations influence educational attainment in the general population

Socio-economic status is a social construct with heritable components and genetic consequences

In civilizations, individuals are born into or sorted into different levels of socio-economic status (SES). SES clusters in families and geographically, and is robustly associated with genetic effects. Here we first review the history of scientific research on the relationship between SES and heredity. We then discuss recent findings in genomics research in light of the hypothesis that SES is a dynamic social construct that involves genetically influenced traits that help in achieving or retaining a socio-economic position, and can affect the distribution of genes associated with such traits. Social stratification results in people with differing traits being sorted into strata with different environmental exposures, which can result in evolutionary selection pressures through differences in mortality, reproduction and non-random mating. Genomics research is revealing previously concealed genetic consequences of the way society is organized, yielding insights that should be approached with caution in pursuit of a fair and functional society.

Analysis-ready VCF at Biobank scale using Zarr

Background

Variant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasises efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. Biobank scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed.

Results

Zarr is a format for storing multi-dimensional data that is widely used across the sciences, and is ideally suited to massively parallel processing. We present the VCF Zarr specification, an encoding of the VCF data model using Zarr, along with fundamental software infrastructure for efficient and reliable conversion at scale. We show how this format is far more efficient than standard VCF based approaches, and competitive with specialised methods for storing genotype data in terms of compression ratios and single-threaded calculation performance. We present case studies on subsets of three large human datasets (Genomics England: n=78,195; Our Future Health: n=651,050; All of Us: n=245,394) along with whole genome datasets for Norway Spruce (n=1,063) and SARS-CoV-2 (n=4,484,157). We demonstrate the potential for VCF Zarr to enable a new generation of high-performance and cost-effective applications via illustrative examples using cloud computing and GPUs.

Conclusions

Large row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely-used, open-source technologies has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores, while maintaining compatibility with existing file-oriented workflows.

Exome sequencing of UK birth cohorts

Birth cohort studies involve repeated surveys of large numbers of individuals from birth and throughout their lives. They collect information useful for a wide range of life course research domains, and biological samples which can be used to derive data from an increasing collection of omic technologies. This rich source of longitudinal data, when combined with genomic data, offers the scientific community valuable insights ranging from population genetics to applications across the social sciences. Here we present quality-controlled whole exome sequencing data from three UK birth cohorts: the Avon Longitudinal Study of Parents and Children (8,436 children and 3,215 parents), the Millenium Cohort Study (7,667 children and 6,925 parents) and Born in Bradford (8,784 children and 2,875 parents). The overall objective of this coordinated effort is to make the resulting high-quality data widely accessible to the global research community in a timely manner. We describe how the datasets were generated and subjected to quality control at the sample, variant and genotype level. We then present some preliminary analyses to illustrate the quality of the datasets and probe potential sources of bias. We introduce measures of ultra-rare variant burden to the variables available for researchers working on these cohorts, and show that the exome-wide burden of deleterious protein-truncating variants, S het burden, is associated with educational attainment and cognitive test scores. The whole exome sequence data from these birth cohorts (CRAM & VCF files) are available through the European Genome-Phenome Archive, and here we provide guidance for their use.

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction

This study delves into the intricate genetic and clinical aspects of Schizophrenia, a complex mental disorder with uncertain etiology. Deep Learning (DL) holds promise for analyzing large genomic datasets to uncover new risk factors. However, based on reports of non-negligible misdiagnosis rates for SCZ, case-control cohorts may contain outlying genetic profiles, hindering compelling performances of classification models. The research employed a case-control dataset sourced from the Swedish populace. A gene-annotation-based DL architecture was developed and employed in two stages. First, the model was trained on the entire dataset to highlight differences between cases and controls. Then, samples likely to be misclassified were excluded, and the model was retrained on the refined dataset for performance evaluation. The results indicate that SCZ prevalence and misdiagnosis rates can affect case-control cohorts, potentially compromising future studies reliant on such datasets. However, by detecting and filtering outliers, the study demonstrates the feasibility of adapting DL methodologies to large-scale biological problems, producing results more aligned with existing heritability estimates for SCZ. This approach not only advances the comprehension of the genetic background of SCZ but also opens doors for adapting DL techniques in complex research for precision medicine in mental health.

Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders

Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6987 probands from the Deciphering Developmental Disorders (DDD) study and 9720 controls, and found one gene, RAB2A, that passed multiple testing correction (p = 6.7 × 10-7). We then investigated whether cis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7 × 10-3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that common cis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.

H2A monoubiquitination: insights from human genetics and animal models

Metazoan development arises from spatiotemporal control of gene expression, which depends on epigenetic regulators like the polycomb group proteins (PcG) that govern the chromatin landscape. PcG proteins facilitate the addition and removal of histone 2A monoubiquitination at lysine 119 (H2AK119ub1), which regulates gene expression, cell fate decisions, cell cycle progression, and DNA damage repair. Regulation of these processes by PcG proteins is necessary for proper development, as pathogenic variants in these genes are increasingly recognized to underly developmental disorders. Overlapping features of developmental syndromes associated with pathogenic variants in specific PcG genes suggest disruption of central developmental mechanisms; however, unique clinical features observed in each syndrome suggest additional non-redundant functions for each PcG gene. In this review, we describe the clinical manifestations of pathogenic PcG gene variants, review what is known about the molecular functions of these gene products during development, and interpret the clinical data to summarize the current evidence toward an understanding of the genetic and molecular mechanism.

A predisposed motor bias shapes individuality in vocal learning

The development of individuality during learned behavior is a common trait observed across animal species; however, the underlying biological mechanisms remain understood. Similar to human speech, songbirds develop individually unique songs with species-specific traits through vocal learning. In this study, we investigate the developmental and molecular mechanisms underlying individuality in vocal learning by utilizing F1 hybrid songbirds (Taeniopygia guttata cross with Taeniopygia bichenovii), taking an integrating approach combining experimentally controlled systematic song tutoring, unbiased discriminant analysis of song features, and single-cell transcriptomics. When tutoring with songs from both parental species, F1 hybrid individuals exhibit evident diversity in their acquired songs. Approximately 30% of F1 hybrids selectively learn either song of the two parental species, while others develop merged songs that combine traits from both species. Vocal acoustic biases during vocal babbling initially appear as individual differences in songs among F1 juveniles and are maintained through the sensitive period of song vocal learning. These vocal acoustic biases emerge independently of the initial auditory experience of hearing the biological father's and passive tutored songs. We identify individual differences in transcriptional signatures in a subset of cell types, including the glutamatergic neurons projecting from the cortical vocal output nucleus to the hypoglossal nuclei, which are associated with variations of vocal acoustic features. These findings suggest that a genetically predisposed vocal motor bias serves as the initial origin of individual variation in vocal learning, influencing learning constraints and preferences.

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

The impact of rare protein coding genetic variation on adult cognitive function

Compelling evidence suggests that human cognitive function is strongly influenced by genetics. Here, we conduct a large-scale exome study to examine whether rare protein-coding variants impact cognitive function in the adult population (n = 485,930). We identify eight genes (ADGRB2, KDM5B, GIGYF1, ANKRD12, SLC8A1, RC3H2, CACNA1A and BCAS3) that are associated with adult cognitive function through rare coding variants with large effects. Rare genetic architecture for cognitive function partially overlaps with that of neurodevelopmental disorders. In the case of KDM5B we show how the genetic dosage of one of these genes may determine the variability of cognitive, behavioral and molecular traits in mice and humans. We further provide evidence that rare and common variants overlap in association signals and contribute additively to cognitive function. Our study introduces the relevance of rare coding variants for cognitive function and unveils high-impact monogenic contributions to how cognitive function is distributed in the normal adult population.

Discovery of 42 genome-wide significant loci associated with dyslexia

Reading and writing are crucial life skills but roughly one in ten children are affected by dyslexia, which can persist into adulthood. Family studies of dyslexia suggest heritability up to 70%, yet few convincing genetic markers have been found. Here we performed a genome-wide association study of 51,800 adults self-reporting a dyslexia diagnosis and 1,087,070 controls and identified 42 independent genome-wide significant loci: 15 in genes linked to cognitive ability/educational attainment, and 27 new and potentially more specific to dyslexia. We validated 23 loci (13 new) in independent cohorts of Chinese and European ancestry. Genetic etiology of dyslexia was similar between sexes, and genetic covariance with many traits was found, including ambidexterity, but not neuroanatomical measures of language-related circuitry. Dyslexia polygenic scores explained up to 6% of variance in reading traits, and might in future contribute to earlier identification and remediation of dyslexia.

Ultra-rare and common genetic variant analysis converge to implicate negative selection and neuronal processes in the aetiology of schizophrenia

Both common and rare genetic variants (minor allele frequency >1% and <0.1% respectively) have been implicated in the aetiology of schizophrenia. In this study, we integrate single-cell gene expression data with publicly available Genome-Wide Association Study (GWAS) and exome sequenced data in order to investigate in parallel, the enrichment of common and (ultra-)rare variants related to schizophrenia in several functionally relevant gene-sets. Four types of gene-sets were constructed 1) protein-truncating variant (PTV)-intolerant (PI) genes 2) genes expressed in brain cell types and neurons ascertained from mouse and human brain tissue 3) genes defined by synaptic function and location and 4) intersection genes, i.e., PI genes that are expressed in the human and mouse brain cell gene-sets. We show that common as well as ultra-rare schizophrenia-associated variants are overrepresented in PI genes, in excitatory neurons from the prefrontal cortex and hippocampus, medium spiny neurons, and genes enriched for synaptic processes. We also observed stronger enrichment in the intersection genes. Our findings suggest that across the allele frequency spectrum, genes and genetic variants likely to be under stringent selection, and those expressed in particular brain cell types, are involved in the same biological pathways influencing the risk for schizophrenia.

Contribution of rare whole-genome sequencing variants to plasma protein levels and the missing heritability

Despite the success of genome-wide association studies, much of the genetic contribution to complex traits remains unexplained. Here, we analyse high coverage whole-genome sequencing data, to evaluate the contribution of rare genetic variants to 414 plasma proteins. The frequency distribution of genetic variants is skewed towards the rare spectrum, and damaging variants are more often rare. We estimate that less than 4.3% of the narrow-sense heritability is expected to be explained by rare variants in our cohort. Using a gene-based approach, we identify Cis-associations for 237 of the proteins, which is slightly more compared to a GWAS (N = 213), and we identify 34 associated loci in Trans. Several associations are driven by rare variants, which have larger effects, on average. We therefore conclude that rare variants could be of importance for precision medicine applications, but have a more limited contribution to the missing heritability of complex diseases.

Despite the success of genome-wide association studies, much of the genetic contribution to complex traits remains unexplained. Here, the authors identify effects by rare variants on plasma proteins, and estimate the contribution of rare variants to the heritability.

The impact of educational attainment, intelligence and intellectual disability on schizophrenia: a Swedish population-based register and genetic study

Schizophrenia (SCZ) is highly heterogenous and no subtypes characterizing treatment response or longitudinal course well. Cognitive impairment is a core clinical feature of SCZ and a determinant of poorer outcome. Genetic overlap between SCZ and cognitive traits is complex, with limited studies of comprehensive epidemiological and genomic evidence. To examine the relation between SCZ and three cognitive traits, educational attainment (EDU), premorbid cognitive ability, and intellectual disability (ID), we used two Swedish samples: a national cohort (14,230 SCZ cases and 3,816,264 controls) and a subsample with comprehensive genetic data (4992 cases and 6009 controls). Population-based analyses confirmed worse cognition as a risk factor for SCZ, and the pedigree and SNP-based genetic correlations were comparable. In the genotyped cases, those with high EDU and premorbid cognitive ability tended to have higher polygenetic risk scores (PRS) of EDU and intelligence and fewer rare exonic variants. Finally, by applying an empirical clustering method, we dissected SCZ cases into four replicable subgroups characterized by EDU and ID. In particular, the subgroup with higher EDU in the national cohort had fewer adverse outcomes including long hospitalization and death. In the genotyped subsample, this subgroup had higher PRS of EDU and no excess of rare genetic burdens than controls. In conclusion, we found extensive evidence of a robust relation between cognitive traits and SCZ, underscoring the importance of cognition in dissecting the heterogeneity of SCZ.

Reduced reproductive success is associated with selective constraint on human genes

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants¹. Although genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known². Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily owing to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is probably mediated by genetically associated cognitive and behavioural traits, which may mean that male carriers are less likely to find reproductive partners. This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. Although this genetic association may only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time.

Modification of Heritability for Educational Attainment and Fluid Intelligence by Socioeconomic Deprivation in the UK Biobank

OBJECTIVE

Socioeconomic factors have been suggested to influence the effect of education- and intelligence-associated genetic variants. However, results from previous studies on the interaction between socioeconomic status and education or intelligence have been inconsistent. The authors sought to assess these interactions in the UK Biobank cohort of 500,000 participants.

METHODS

The authors assessed the effect of socioeconomic deprivation on education- and intelligence-associated genetic variants by estimating the single-nucleotide polymorphism (SNP) heritability for fluid intelligence, educational attainment, and years of education in subsets of UK Biobank participants with different degrees of social deprivation, using linkage disequilibrium score regression. They also generated polygenic scores with LDpred and tested for interactions with social deprivation.

RESULTS

SNP heritability increased with socioeconomic deprivation for fluid intelligence, educational attainment, and years of education. Polygenic scores were also found to interact with socioeconomic deprivation, where the effects of the scores increased with increasing deprivation for all traits.

CONCLUSIONS

These results indicate that genetics have a larger influence on educational and cognitive outcomes in more socioeconomically deprived U.K. citizens, which has serious implications for equality of opportunity.

A patient-based model of RNA mis-splicing uncovers treatment targets in Parkinson's disease

Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder with monogenic forms representing prototypes of the underlying molecular pathology and reproducing to variable degrees the sporadic forms of the disease. Using a patient-based in vitro model of PARK7-linked PD, we identified a U1-dependent splicing defect causing a drastic reduction in DJ-1 protein and, consequently, mitochondrial dysfunction. Targeting defective exon skipping with genetically engineered U1-snRNA recovered DJ-1 protein expression in neuronal precursor cells and differentiated neurons. After prioritization of candidate drugs, we identified and validated a combinatorial treatment with the small-molecule compounds rectifier of aberrant splicing (RECTAS) and phenylbutyric acid, which restored DJ-1 protein and mitochondrial dysfunction in patient-derived fibroblasts as well as dopaminergic neuronal cell loss in mutant midbrain organoids. Our analysis of a large number of exomes revealed that U1 splice-site mutations were enriched in sporadic PD patients. Therefore, our study suggests an alternative strategy to restore cellular abnormalities in in vitro models of PD and provides a proof of concept for neuroprotection based on precision medicine strategies in PD.

A rare missense variant in the ATP2C2 gene is associated with language impairment and related measures

At least 5% of children present unexpected difficulties in expressing and understanding spoken language. This condition is highly heritable and often co-occurs with other neurodevelopmental disorders such as dyslexia and ADHD. Through an exome sequencing analysis, we identified a rare missense variant (chr16:84405221, GRCh38.p12) in the ATP2C2 gene. ATP2C2 was implicated in language disorders by linkage and association studies, and exactly the same variant was reported previously in a different exome sequencing study for language impairment (LI). We followed up this finding by genotyping the mutation in cohorts selected for LI and comorbid disorders. We found that the variant had a higher frequency in LI cases (1.8%, N = 360) compared with cohorts selected for dyslexia (0.8%, N = 520) and ADHD (0.7%, N = 150), which presented frequencies comparable to reference databases (0.9%, N = 24 046 gnomAD controls). Additionally, we observed that carriers of the rare variant identified from a general population cohort (N = 42, ALSPAC cohort) presented, as a group, lower scores on a range of reading and language-related measures compared to controls (N = 1825; minimum P = 0.002 for non-word reading). ATP2C2 encodes for an ATPase (SPCA2) that transports calcium and manganese ions into the Golgi lumen. Our functional characterization suggested that the rare variant influences the ATPase activity of SPCA2. Thus, our results further support the role of ATP2C2 locus in language-related phenotypes and pinpoint the possible effects of a specific rare variant at molecular level.

Virus infection of the CNS disrupts the immune-neural-synaptic axis via induction of pleiotropic gene regulation of host responses

Treatment for many viral infections of the central nervous system (CNS) remains only supportive. Here we address a remaining gap in our knowledge regarding how the CNS and immune systems interact during viral infection. By examining the regulation of the immune and nervous system processes in a nonhuman primate model of West Nile virus neurological disease, we show that virus infection disrupts the homeostasis of the immune-neural-synaptic axis via induction of pleiotropic genes with distinct functions in each component of the axis. This pleiotropic gene regulation suggests an unintended off-target negative impact of virus-induced host immune responses on the neurotransmission, which may be a common feature of various viral infections of the CNS.

Polygenic burden has broader impact on health, cognition, and socioeconomic outcomes than most rare and high-risk copy number variants

Copy number variants (CNVs) are associated with syndromic and severe neurological and psychiatric disorders (SNPDs), such as intellectual disability, epilepsy, schizophrenia, and bipolar disorder. Although considered high-impact, CNVs are also observed in the general population. This presents a diagnostic challenge in evaluating their clinical significance. To estimate the phenotypic differences between CNV carriers and non-carriers regarding general health and well-being, we compared the impact of SNPD-associated CNVs on health, cognition, and socioeconomic phenotypes to the impact of three genome-wide polygenic risk score (PRS) in two Finnish cohorts (FINRISK, n = 23,053 and NFBC1966, n = 4895). The focus was on CNV carriers and PRS extremes who do not have an SNPD diagnosis. We identified high-risk CNVs (DECIPHER CNVs, risk gene deletions, or large [>1 Mb] CNVs) in 744 study participants (2.66%), 36 (4.8%) of whom had a diagnosed SNPD. In the remaining 708 unaffected carriers, we observed lower educational attainment (EA; OR = 0.77 [95% CI 0.66-0.89]) and lower household income (OR = 0.77 [0.66-0.89]). Income-associated CNVs also lowered household income (OR = 0.50 [0.38-0.66]), and CNVs with medical consequences lowered subjective health (OR = 0.48 [0.32-0.72]). The impact of PRSs was broader. At the lowest extreme of PRS for EA, we observed lower EA (OR = 0.31 [0.26-0.37]), lower-income (OR = 0.66 [0.57-0.77]), lower subjective health (OR = 0.72 [0.61-0.83]), and increased mortality (Cox's HR = 1.55 [1.21-1.98]). PRS for intelligence had a similar impact, whereas PRS for schizophrenia did not affect these traits. We conclude that the majority of working-age individuals carrying high-risk CNVs without SNPD diagnosis have a modest impact on morbidity and mortality, as well as the limited impact on income and educational attainment, compared to individuals at the extreme end of common genetic variation. Our findings highlight that the contribution of traditional high-risk variants such as CNVs should be analyzed in a broader genetic context, rather than evaluated in isolation.

GEN2VCF: a converter for human genome imputation output format to VCF format

BACKGROUND

For a genome-wide association study in humans, genotype imputation is an essential analysis tool for improving association mapping power. When IMPUTE software is used for imputation analysis, an imputation output (GEN format) should be converted to variant call format (VCF) with imputed genotype dosage for association analysis. However, the conversion requires multiple software packages in a pipeline with a large amount of processing time.

OBJECTIVE

We developed GEN2VCF, a fast and convenient GEN format to VCF conversion tool with dosage support.

METHODS

The performance of GEN2VCF was compared to BCFtools, QCTOOL, and Oncofunco. The test data set was a 1 Mb GEN-formatted file of 5000 samples. To determine the performance of various sample sizes, tests were performed from 1000 to 5000 samples with a step size of 1000. Runtime and memory usage were used as performance measures.

RESULTS

GEN2VCF showed drastically increased performances with respect to runtime and memory usage. Runtime and memory usage of GEN2VCF was at least 1.4- and 7.4-fold lower compared to other methods, respectively.

CONCLUSIONS

GEN2VCF provides users with efficient conversion from GEN format to VCF with the best-guessed genotype, genotype posterior probabilities, and genotype dosage, as well as great flexibility in implementation with other software packages in a pipeline.

The mutational constraint spectrum quantified from variation in 141,456 humans

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

Translating insights from neuropsychiatric genetics and genomics for precision psychiatry

The primary aim of precision medicine is to tailor healthcare more closely to the needs of individual patients. This requires progress in two areas: the development of more precise treatments and the ability to identify patients or groups of patients in the clinic for whom such treatments are likely to be the most effective. There is widespread optimism that advances in genomics will facilitate both of these endeavors. It can be argued that of all medical specialties psychiatry has most to gain in these respects, given its current reliance on syndromic diagnoses, the minimal foundation of existing mechanistic knowledge, and the substantial heritability of psychiatric phenotypes. Here, we review recent advances in psychiatric genomics and assess the likely impact of these findings on attempts to develop precision psychiatry. Emerging findings indicate a high degree of polygenicity and that genetic risk maps poorly onto the diagnostic categories used in the clinic. The highly polygenic and pleiotropic nature of psychiatric genetics will impact attempts to use genomic data for prediction and risk stratification, and also poses substantial challenges for conventional approaches to gaining biological insights from genetic findings. While there are many challenges to overcome, genomics is building an empirical platform upon which psychiatry can now progress towards better understanding of disease mechanisms, better treatments, and better ways of targeting treatments to the patients most likely to benefit, thus paving the way for precision psychiatry.

Genetic associations with mathematics tracking and persistence in secondary school

Maximizing the flow of students through the science, technology, engineering, and math (STEM) pipeline is important to promoting human capital development and reducing economic inequality. A critical juncture in the STEM pipeline is the highly cumulative sequence of secondary school math courses. Students from disadvantaged schools are less likely to complete advanced math courses. Here, we conduct an analysis of how the math pipeline differs across schools using student polygenic scores, which are DNA-based indicators of propensity to succeed in education. We integrated genetic and official school transcript data from over 3000 European-ancestry students from U.S. high schools. We used polygenic scores as a molecular tracer to understand how the flow of students through the high school math pipeline differs in socioeconomically advantaged versus disadvantaged schools. Students with higher education polygenic scores were tracked to more advanced math already at the beginning of high school and persisted in math for more years. Analyses using genetics as a molecular tracer revealed that the dynamics of the math pipeline differed by school advantage. Compared to disadvantaged schools, advantaged schools buffered students with low polygenic scores from dropping out of math. Across all schools, even students with exceptional polygenic scores (top 2%) were unlikely to take the most advanced math classes, suggesting substantial room for improvement in the development of potential STEM talent. These results link new molecular genetic discoveries to a common target of educational-policy reforms.

Genetic Association of Finger Photoplethysmography-Derived Arterial Stiffness Index With Blood Pressure and Coronary Artery Disease

Objective- Arterial stiffness index (ASI) is independently associated with blood pressure (BP) and coronary artery disease (CAD) epidemiologically. However, it is unknown whether these associations represent causal relationships. Here, we assess whether genetic predisposition to increased ASI is associated with elevated BP and CAD risk. Approach and Results- We first performed a large-scale epidemiological association of finger photoplethysmography-derived ASI in the UK Biobank, finding significant associations with systolic BP (β=0.55 mm Hg; [95% CI, 0.45-0.65]; P=5.77×10^-24; N=137 858), diastolic BP (β=1.05 mm Hg; [95% CI, 0.99-1.11]; P=7.27×10^-272; N=137 862), and incident CAD (hazard ratio, 1.08; [95% CI, 1.04-1.11]; P=1.5×10^-6; N=3692 cases, 126 615 controls) in multivariable models. We then performed an ASI genome-wide association study analysis in 131 686 participants from the UK Biobank. Across participants not in the ASI genome-wide association study, a 6-variant ASI polygenic risk score was calculated. Each SD increase in genetic ASI was associated with systolic BP (β=4.63 mm Hg; [95% CI, 2.1-7.2]; P=3.37×10^-4; N=208 897), and diastolic BP (β=2.61 mm Hg; [95% CI, 1.2-4.0]; P=2.85×10^-4; N=208 897); however, no association was observed with incident CAD (hazard ratio, 1.12; [95% CI, 0.55-2.3]; P=0.75; N=223 061; 7534 cases). The lack of CAD association observed was replicated among 184 305 participants (60 810 cases) from the CARDIOGRAMplusC4D (Coronary Artery Disease Genetics Consortium; odds ratio, 0.56; [95% CI, 0.26-1.24]; P=0.15). Conclusions- Our data support the conclusion that finger photoplethysmography-derived ASI is an independent, genetically causal risk factor for BP, but do not support the notion that ASI is a suitable surrogate for CAD risk.

Exome sequencing in schizophrenia-affected parent-offspring trios reveals risk conferred by protein-coding de novo mutations

Protein-coding de novo mutations (DNMs) are significant risk factors in many neurodevelopmental disorders, whereas schizophrenia (SCZ) risk associated with DNMs has thus far been shown to be modest. We analyzed DNMs from 1,695 SCZ-affected trios and 1,077 published SCZ-affected trios to better understand the contribution to SCZ risk. Among 2,772 SCZ probands, exome-wide DNM burden remained modest. Gene set analyses revealed that SCZ DNMs were significantly concentrated in genes that were highly expressed in the brain, that were under strong evolutionary constraint and/or overlapped with genes identified in other neurodevelopmental disorders. No single gene surpassed exome-wide significance; however, 16 genes were recurrently hit by protein-truncating DNMs, corresponding to a 3.15-fold higher rate than the mutation model expectation (permuted 95% confidence interval: 1-10 genes; permuted P = 3 × 10-5). Overall, DNMs explain a small fraction of SCZ risk, and larger samples are needed to identify individual risk genes, as coding variation across many genes confers risk for SCZ in the population.

The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. MGRB individuals have fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK Biobank cohorts, consistent with risk depletion. Age-related somatic changes are correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing.

Healthspan and healthy aging are areas of research with potential socioeconomic impact. Here, the authors present the Medical Genome Reference Bank (MGRB) which consist of over 4,000 individuals aged 70 years and older without a history of the major age-related diseases and report on results from whole-genome sequencing and association analyses.

Cognitive Genomics: Recent Advances and Current Challenges

PURPOSE OF REVIEW

We review recent progress in uncovering the complex genetic architecture of cognition, arising primarily from genome-wide association studies (GWAS). We explore the genetic correlations between cognitive performance and neuropsychiatric disorders, the genetic and environmental factors associated with age-related cognitive decline, and speculate about the future role of genomics in the understanding of cognitive processes.

RECENT FINDINGS

Improvements in genomic methods, and the increasing availability of large datasets via consortia cooperation, have led to a greater understanding of the role played by common and rare variants in the genomics of cognition, the highly polygenic basis of cognitive function and dysfunction, and the multiple biological processes involved. Recent research has aided in our understanding of the complex biological nature of genomics of cognition. Further development of data banks and techniques to analyze this data hold significant promise for understanding cognitive ability, and for treating cognitively related disability.

Exome sequencing in amyotrophic lateral sclerosis implicates a novel gene, DNAJC7, encoding a heat-shock protein

To discover novel genes underlying amyotrophic lateral sclerosis (ALS), we aggregated exomes from 3,864 cases and 7,839 ancestry-matched controls. We observed a significant excess of rare protein-truncating variants among ALS cases, and these variants were concentrated in constrained genes. Through gene level analyses, we replicated known ALS genes including SOD1, NEK1 and FUS. We also observed multiple distinct protein-truncating variants in a highly constrained gene, DNAJC7. The signal in DNAJC7 exceeded genome-wide significance, and immunoblotting assays showed depletion of DNAJC7 protein in fibroblasts in a patient with ALS carrying the p.Arg156Ter variant. DNAJC7 encodes a member of the heat-shock protein family, HSP40, which, along with HSP70 proteins, facilitates protein homeostasis, including folding of newly synthesized polypeptides and clearance of degraded proteins. When these processes are not regulated, misfolding and accumulation of aberrant proteins can occur and lead to protein aggregation, which is a pathological hallmark of neurodegeneration. Our results highlight DNAJC7 as a novel gene for ALS.

htsget: a protocol for securely streaming genomic data

Summary

Standardized interfaces for efficiently accessing high-throughput sequencing data are a fundamental requirement for large-scale genomic data sharing. We have developed htsget, a protocol for secure, efficient and reliable access to sequencing read and variation data. We demonstrate four independent client and server implementations, and the results of a comprehensive interoperability demonstration.

Availability and implementation

http://samtools.github.io/hts-specs/htsget.html

Supplementary information

Supplementary data are available at Bioinformatics online.

A framework for the investigation of rare genetic disorders in neuropsychiatry

De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

Truncating variant burden in high-functioning autism and pleiotropic effects of LRP1 across psychiatric phenotypes

BACKGROUND

Previous research has implicated de novo and inherited truncating mutations in autism-spectrum disorder. We aim to investigate whether the load of inherited truncating mutations contributes similarly to high-functioning autism, and to characterize genes that harbour de novo variants in high-functioning autism.

METHODS

We performed whole-exome sequencing in 20 high-functioning autism families (average IQ = 100).

RESULTS

We observed no difference in the number of transmitted versus nontransmitted truncating alleles for high-functioning autism (117 v. 130, p = 0.78). Transmitted truncating and de novo variants in high-functioning autism were not enriched in gene ontology (GO) or Kyoto Encyclopedia of Genes and Genomes (KEGG) categories, or in autism-related gene sets. However, in a patient with high-functioning autism we identified a de novo variant in a canonical splice site of LRP1, a postsynaptic density gene that is a target for fragile X mental retardation protein (FRMP). This de novo variant leads to in-frame skipping of exon 29, removing 2 of 6 blades of the β-propeller domain 4 of LRP1, with putative functional consequences. Large data sets implicate LRP1 across a number of psychiatric disorders: de novo variants are associated with autism-spectrum disorder (p = 0.039) and schizophrenia (p = 0.008) from combined sequencing projects; common variants using genome-wide association study data sets from the Psychiatric Genomics Consortium show gene-based association in schizophrenia (p = 6.6 × E−07) and in a meta-analysis across 7 psychiatric disorders (p = 2.3 × E−03); and the burden of ultra-rare pathogenic variants has been shown to be higher in autism-spectrum disorder (p = 1.2 × E−05), using whole-exome sequencing from 6135 patients with schizophrenia, 1778 patients with autism-spectrum disorder and 7875 controls.

LIMITATIONS

We had a limited sample of patients with high-functioning autism, related to difficulty in recruiting probands with high cognitive performance and no family history of psychiatric disorders.

CONCLUSION

Previous studies and ours suggest an effect of truncating mutations restricted to severe autism-spectrum disorder phenotypes that are associated with intellectual disability. We provide evidence for pleiotropic effects of common and rare variants in the LRP1 gene across psychiatric phenotypes.

The project MinE databrowser: bringing large-scale whole-genome sequencing in ALS to researchers and the public

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive fatal neurodegenerative disease affecting one in 350 people. The aim of Project MinE is to elucidate the pathophysiology of ALS through whole-genome sequencing at least 15,000 ALS patients and 7500 controls at 30× coverage. Here, we present the Project MinE data browser ( databrowser.projectmine.com ), a unique and intuitive one-stop, open-access server that provides detailed information on genetic variation analyzed in a new and still growing set of 4366 ALS cases and 1832 matched controls. Through its visual components and interactive design, the browser specifically aims to be a resource to those without a biostatistics background and allow clinicians and preclinical researchers to integrate Project MinE data into their own research. The browser allows users to query a transcript and immediately access a unique combination of detailed (meta)data, annotations and association statistics that would otherwise require analytic expertise and visits to scattered resources.

Ultra-Rare Genetic Variation in the Epilepsies: A Whole-Exome Sequencing Study of 17,606 Individuals

Sequencing-based studies have identified novel risk genes associated with severe epilepsies and revealed an excess of rare deleterious variation in less-severe forms of epilepsy. To identify the shared and distinct ultra-rare genetic risk factors for different types of epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,436 controls of European ancestry. We focused on three phenotypic groups: severe developmental and epileptic encephalopathies (DEEs), genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy; we saw the strongest enrichment in individuals with DEEs and the least strong in individuals with NAFE. Moreover, we found that inhibitory GABA_A receptor genes were enriched for missense variants across all three classes of epilepsy, whereas no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEEs and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the lead associations; such genes included CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study, the largest epilepsy WES study to date, confirms a convergence in the genetics of severe and less-severe epilepsies associated with ultra-rare coding variation, and it highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology.

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). However, systematic annotation of synaptic genes and ontology of synaptic processes are currently lacking. We established SynGO, an interactive knowledge base that accumulates available research about synapse biology using Gene Ontology (GO) annotations to novel ontology terms: 87 synaptic locations and 179 synaptic processes. SynGO annotations are exclusively based on published, expert-curated evidence. Using 2,922 annotations for 1,112 genes, we show that synaptic genes are exceptionally well conserved and less tolerant to mutations than other genes. Many SynGO terms are significantly overrepresented among gene variations associated with intelligence, educational attainment, ADHD, autism, and bipolar disorder and among de novo variants associated with neurodevelopmental disorders, including schizophrenia. SynGO is a public, universal reference for synapse research and an online analysis platform for interpretation of large-scale -omics data (https://syngoportal.org and http://geneontology.org).

Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis

Streptococcus pneumoniae is a common nasopharyngeal colonizer, but can also cause life-threatening invasive diseases such as empyema, bacteremia and meningitis. Genetic variation of host and pathogen is known to play a role in invasive pneumococcal disease, though to what extent is unknown. In a genome-wide association study of human and pathogen we show that human variation explains almost half of variation in susceptibility to pneumococcal meningitis and one-third of variation in severity, identifying variants in CCDC33 associated with susceptibility. Pneumococcal genetic variation explains a large amount of invasive potential (70%), but has no effect on severity. Serotype alone is insufficient to explain invasiveness, suggesting other pneumococcal factors are involved in progression to invasive disease. We identify pneumococcal genes involved in invasiveness including pspC and zmpD, and perform a human-bacteria interaction analysis. These genes are potential candidates for the development of more broadly-acting pneumococcal vaccines.

Contribution of rare and common variants to intellectual disability in a sub-isolate of Northern Finland

The contribution of de novo variants in severe intellectual disability (ID) has been extensively studied whereas the genetics of mild ID has been less characterized. To elucidate the genetics of milder ID we studied 442 ID patients enriched for mild ID (>50%) from a population isolate of Finland. Using exome sequencing, we show that rare damaging variants in known ID genes are observed significantly more often in severe (27%) than in mild ID (13%) patients. We further observe a significant enrichment of functional variants in genes not yet associated with ID (OR: 2.1). We show that a common variant polygenic risk significantly contributes to ID. The heritability explained by polygenic risk score is the highest for educational attainment (EDU) in mild ID (2.2%) but lower for more severe ID (0.6%). Finally, we identify a Finland enriched homozygote variant in the CRADD ID associated gene.

Manhattan Harvester and Cropper: a system for GWAS peak detection

Background

Selection of interesting regions from genome wide association studies (GWAS) is typically performed by eyeballing of Manhattan Plots. This is no longer possible with thousands of different phenotypes. There is a need for tools that can automatically detect genomic regions that correspond to what the experienced researcher perceives as peaks worthwhile of further study.

Results

We developed Manhattan Harvester, a tool designed for “peak extraction” from GWAS summary files and computation of parameters characterizing various aspects of individual peaks. We present the algorithms used and a model for creating a general quality score that evaluates peaks similarly to that of a human researcher. Our tool Cropper utilizes a graphical interface for inspecting, cropping and subsetting Manhattan Plot regions. Cropper is used to validate and visualize the regions detected by Manhattan Harvester.

Conclusions

We conclude that our tools fill the current void in automatically screening large number of GWAS output files in batch mode. The interesting regions are detected and quantified by various parameters by Manhattan Harvester. Cropper offers graphical tools for in-depth inspection of the regions. The tools are open source and freely available.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2600-4) contains supplementary material, which is available to authorized users.

Association Between Titin Loss-of-Function Variants and Early-Onset Atrial Fibrillation

Importance

Atrial fibrillation (AF) is the most common arrhythmia affecting 1% of the population. Young individuals with AF have a strong genetic association with the disease, but the mechanisms remain incompletely understood.

Objective

To perform large-scale whole-genome sequencing to identify genetic variants related to AF.

Design, Setting, and Participants

The National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine Program includes longitudinal and cohort studies that underwent high-depth whole-genome sequencing between 2014 and 2017 in 18 526 individuals from the United States, Mexico, Puerto Rico, Costa Rica, Barbados, and Samoa. This case-control study included 2781 patients with early-onset AF from 9 studies and identified 4959 controls of European ancestry from the remaining participants. Results were replicated in the UK Biobank (346 546 participants) and the MyCode Study (42 782 participants).

Exposures

Loss-of-function (LOF) variants in genes at AF loci and common genetic variation across the whole genome.

Main Outcomes and Measures

Early-onset AF (defined as AF onset in persons <66 years of age). Due to multiple testing, the significance threshold for the rare variant analysis was P = 4.55 × 10-3.

Results

Among 2781 participants with early-onset AF (the case group), 72.1% were men, and the mean (SD) age of AF onset was 48.7 (10.2) years. Participants underwent whole-genome sequencing at a mean depth of 37.8 fold and mean genome coverage of 99.1%. At least 1 LOF variant in TTN, the gene encoding the sarcomeric protein titin, was present in 2.1% of case participants compared with 1.1% in control participants (odds ratio [OR], 1.76 [95% CI, 1.04-2.97]). The proportion of individuals with early-onset AF who carried a LOF variant in TTN increased with an earlier age of AF onset (P value for trend, 4.92 × 10-4), and 6.5% of individuals with AF onset prior to age 30 carried a TTN LOF variant (OR, 5.94 [95% CI, 2.64-13.35]; P = 1.65 × 10-5). The association between TTN LOF variants and AF was replicated in an independent study of 1582 patients with early-onset AF (cases) and 41 200 control participants (OR, 2.16 [95% CI, 1.19-3.92]; P = .01).

Conclusions and Relevance

In a case-control study, there was a statistically significant association between an LOF variant in the TTN gene and early-onset AF, with the variant present in a small percentage of participants with early-onset AF (the case group). Further research is necessary to understand whether this is a causal relationship.

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0 and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ~20,000 individuals in the Generation Scotland family cohort genotyped for ~700,000 single-nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWAS of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence, and education is consistent with mutation-selection balance.

Effect of inbreeding on intellectual disability revisited by trio sequencing

In outbred Western populations, most individuals with intellectual disability (ID) are sporadic cases, dominant de novo mutations (DNM) are frequent, and autosomal recessive ID (ARID) is very rare. Because of the high rate of parental consanguinity, which raises the risk for ARID and other recessive disorders, the prevalence of ID is significantly higher in near- and middle-east countries. Indeed, homozygosity mapping and sequencing in consanguineous families have already identified a plethora of ARID genes, but because of the design of these studies, DNMs could not be systematically assessed, and the proportion of cases that are potentially preventable by avoiding consanguineous marriages or through carrier testing is hitherto unknown. This prompted us to perform whole-exome sequencing in 100 sporadic ID patients from Iran and their healthy consanguineous parents. In 61 patients, we identified apparently causative changes in known ID genes. Of these, 44 were homozygous recessive and 17 dominant DNMs. Assuming that the DNM rate is stable, these results suggest that parental consanguinity raises the ID risk about 3.6-fold, and about 4.1 to 4.25-fold for children of first-cousin unions. These results do not rhyme with recent opinions that consanguinity-related health risks are generally small and have been "overstated" in the past.

Genetic Association of Albuminuria with Cardiometabolic Disease and Blood Pressure

Excretion of albumin in urine, or albuminuria, is associated with the development of multiple cardiovascular and metabolic diseases. However, whether pathways leading to albuminuria are causal for cardiometabolic diseases is unclear. We addressed this question using a Mendelian randomization framework in the UK Biobank, a large population-based cohort. We first performed a genome-wide association study for albuminuria in 382,500 individuals and identified 32 new albuminuria loci. We constructed albuminuria genetic risk scores and tested for association with cardiometabolic diseases. Genetically elevated albuminuria was strongly associated with increased risk of hypertension (1.38 OR; 95% CI, 1.27-1.50 per 1 SD predicted increase in albuminuria, p = 7.01 × 10-14). We then examined bidirectional associations of albuminuria with blood pressure which suggested that genetically elevated albuminuria led to higher blood pressure (2.16 mmHg systolic blood pressure; 95% CI, 1.51-2.82 per 1 SD predicted increase in albuminuria, p = 1.22 × 10-10) and that genetically elevated blood pressure led to more albuminuria (0.005 SD; 95% CI 0.004-0.006 per 1 mmHg predicted increase in systolic blood pressure, p = 2.45 × 10-13). These results support the existence of a feed-forward loop between albuminuria and blood pressure and imply that albuminuria could increase risk of cardiovascular disease through blood pressure. Moreover, they suggest therapies that target albuminuria-increasing processes could have antihypertensive effects that are amplified through inhibition of this feed-forward loop.

Project MinE: study design and pilot analyses of a large-scale whole-genome sequencing study in amyotrophic lateral sclerosis

The most recent genome-wide association study in amyotrophic lateral sclerosis (ALS) demonstrates a disproportionate contribution from low-frequency variants to genetic susceptibility to disease. We have therefore begun Project MinE, an international collaboration that seeks to analyze whole-genome sequence data of at least 15 000 ALS patients and 7500 controls. Here, we report on the design of Project MinE and pilot analyses of successfully sequenced 1169 ALS patients and 608 controls drawn from the Netherlands. As has become characteristic of sequencing studies, we find an abundance of rare genetic variation (minor allele frequency < 0.1%), the vast majority of which is absent in public datasets. Principal component analysis reveals local geographical clustering of these variants within The Netherlands. We use the whole-genome sequence data to explore the implications of poor geographical matching of cases and controls in a sequence-based disease study and to investigate how ancestry-matched, externally sequenced controls can induce false positive associations. Also, we have publicly released genome-wide minor allele counts in cases and controls, as well as results from genic burden tests.

Common genetic variants contribute to risk of rare severe neurodevelopmental disorders

There are thousands of rare human disorders that are caused by single deleterious, protein-coding genetic variants¹. However, patients with the same genetic defect can have different clinical presentations^2-4, and some individuals who carry known disease-causing variants can appear unaffected⁵. Here, to understand what explains these differences, we study a cohort of 6,987 children assessed by clinical geneticists to have severe neurodevelopmental disorders such as global developmental delay and autism, often in combination with abnormalities of other organ systems. Although the genetic causes of these neurodevelopmental disorders are expected to be almost entirely monogenic, we show that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome-wide common variant burden by showing, in an independent sample of 728 trios (comprising a child plus both parents) from the same cohort, that this burden is over-transmitted from parents to children with neurodevelopmental disorders. Our common-variant signal is significantly positively correlated with genetic predisposition to lower educational attainment, decreased intelligence and risk of schizophrenia. We found that common-variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, which suggests that common-variant risk affects patients both with and without a monogenic diagnosis. In addition, previously published common-variant scores for autism, height, birth weight and intracranial volume were all correlated with these traits within our cohort, which suggests that phenotypic expression in individuals with monogenic disorders is affected by the same variants as in the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in neurodevelopmental disorders that are typically considered to be monogenic.

Deep-coverage whole genome sequences and blood lipids among 16,324 individuals

Large-scale deep-coverage whole-genome sequencing (WGS) is now feasible and offers potential advantages for locus discovery. We perform WGS in 16,324 participants from four ancestries at mean depth >29X and analyze genotypes with four quantitative traits-plasma total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. Common variant association yields known loci except for few variants previously poorly imputed. Rare coding variant association yields known Mendelian dyslipidemia genes but rare non-coding variant association detects no signals. A high 2M-SNP LDL-C polygenic score (top 5th percentile) confers similar effect size to a monogenic mutation (~30 mg/dl higher for each); however, among those with severe hypercholesterolemia, 23% have a high polygenic score and only 2% carry a monogenic mutation. At these sample sizes and for these phenotypes, the incremental value of WGS for discovery is limited but WGS permits simultaneous assessment of monogenic and polygenic models to severe hypercholesterolemia.

Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation.¹ Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature,^2–5 it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0%, 6.1%, 3.5%, 3.2% and 1.5% of the population at greater than three-fold increased risk for coronary artery disease (CAD), atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For CAD, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk.⁶ We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care and discuss relevant issues.

Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans.

Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum

There is a limited understanding about the impact of rare protein-truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein-truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, and ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization, and reduced age at enrollment. Gene sets implicated from GWASs did not show a significant protein-truncating variants burden beyond what was captured by established Mendelian genes. In conclusion, we provide a thorough investigation of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.

Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies

Motivation

Genome-wide association studies are now shifting focus from analysis of common to rare variants. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. We propose to approximate power to a varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus.

Results

We perform extensive simulation studies to assess the accuracy of the proposed approximations in realistic settings. Using these simplified power calculations, we develop an analytic framework to obtain bounds on genetic architecture of an underlying trait given results from genome-wide association studies with rare variants. Finally, we provide insights into the required quality of annotation/functional information for identification of likely causal variants to make meaningful improvement in power.

Availability and implementation

A shiny application that allows a variety of Power Analysis of GEnetic AssociatioN Tests (PAGEANT), in R is made publicly available at https://andrewhaoyu.shinyapps.io/PAGEANT/.

Contact

nilanjan@jhu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.