Steven Gazal

Loss of BRCC3 Deubiquitinating Enzyme Leads to Abnormal Angiogenesis and Is Associated with Syndromic Moyamoya

Moyamoya is a cerebrovascular angiopathy characterized by a progressive stenosis of the terminal part of the intracranial carotid arteries and the compensatory development of abnormal and fragile collateral vessels, also called moyamoya vessels, leading to ischemic and hemorrhagic stroke. Moyamoya angiopathy can either be the sole manifestation of the disease (moyamoya disease) or be associated with various conditions, including neurofibromatosis, Down syndrome, TAAD (autosomal-dominant thoracic aortic aneurysm), and radiotherapy of head tumors (moyamoya syndromes). Its prevalence is ten times higher in Japan than in Europe, and an estimated 6%-12% of moyamoya disease is familial in Japan. The pathophysiological mechanisms of this condition remain obscure. Here, we report on three unrelated families affected with an X-linked moyamoya syndrome characterized by the association of a moyamoya angiopathy, short stature, and a stereotyped facial dysmorphism. Other symptoms include an hypergonadotropic hypogonadism, hypertension, dilated cardiomyopathy, premature coronary heart disease, premature hair graying, and early bilateral acquired cataract. We show that this syndromic moyamoya is caused by Xq28 deletions removing MTCP1/MTCP1NB and BRCC3. We also show that brcc3 morphant zebrafish display angiogenesis defects that are rescued by endothelium-specific expression of brcc3. Altogether, these data strongly suggest that BRCC3, a deubiquitinating enzyme that is part of the cellular BRCA1 and BRISC complexes, is an important player in angiogenesis and that BRCC3 loss-of-function mutations are associated with moyamoya angiopathy.

Heterozygous HTRA1 mutations are associated with autosomal dominant cerebral small vessel disease

Cerebral small vessel disease represents a heterogeneous group of disorders leading to stroke and cognitive impairment. While most small vessel diseases appear sporadic and related to age and hypertension, several early-onset monogenic forms have also been reported. However, only a minority of patients with familial small vessel disease carry mutations in one of known small vessel disease genes. We used whole exome sequencing to identify candidate genes in an autosomal dominant small vessel disease family in which known small vessel disease genes had been excluded, and subsequently screened all candidate genes in 201 unrelated probands with a familial small vessel disease of unknown aetiology, using high throughput multiplex polymerase chain reaction and next generation sequencing. A heterozygous HTRA1 variant (R166L), absent from 1000 Genomes and Exome Variant Server databases and predicted to be deleterious by in silico tools, was identified in all affected members of the index family. Ten probands of 201 additional unrelated and affected probands (4.97%) harboured a heterozygous HTRA1 mutation predicted to be damaging. There was a highly significant difference in the number of likely deleterious variants in cases compared to controls (P = 4.2 × 10(-6); odds ratio = 15.4; 95% confidence interval = 4.9-45.5), strongly suggesting causality. Seven of these variants were located within or close to the HTRA1 protease domain, three were in the N-terminal domain of unknown function and one in the C-terminal PDZ domain. In vitro activity analysis of HTRA1 mutants demonstrated a loss of function effect. Clinical features of this autosomal dominant small vessel disease differ from those of CARASIL and CADASIL by a later age of onset and the absence of the typical extraneurological features of CARASIL. They are similar to those of sporadic small vessel disease, except for their familial nature. Our data demonstrate that heterozygous HTRA1 mutations are an important cause of familial small vessel disease, and that screening of HTRA1 should be considered in all patients with a hereditary small vessel disease of unknown aetiology.

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues.

Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection

Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (P = 2.38 × 10−104); the youngest 20% of common SNPs explain 3.9 times more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.

Mixed-model association for biobank-scale datasets

Biobank-based genome-wide association studies are enabling exciting insights in complex trait genetics, but much uncertainty remains over best practices for optimizing statistical power and computational efficiency in GWAS while controlling confounders. Here, we introduce a much faster version of our BOLT-LMM Bayesian mixed model association method—capable of running analyses of the full UK Biobank cohort in a few days on a single compute node—and show that it produces highly powered, robust test statistics when run on all 459K European samples (retaining related individuals). When used to conduct a GWAS for height in UK Biobank, BOLT-LMM achieved power equivalent to linear regression on 650K samples—a 93% increase in effective sample size versus the common practice of analyzing unrelated British samples using linear regression (UK Biobank documentation; Bycroft et al. bioRxiv). Across a broader set of 23 highly heritable traits, the total number of independent GWAS loci detected increased from 5,839 to 10,759, an 84% increase. We recommend the use of BOLT-LMM (retaining related individuals) for biobank-scale analyses, and we have publicly released BOLT-LMM summary association statistics for the 23 traits analyzed as a resource for all researchers.

Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits

Multiple methods have been developed to estimate narrow-sense heritability, h2, using single nucleotide polymorphisms (SNPs) in unrelated individuals. However, a comprehensive evaluation of these methods has not yet been performed, leading to confusion and discrepancy in the literature. We present the most thorough and realistic comparison of these methods to date. We used thousands of real whole-genome sequences to simulate phenotypes under varying genetic architectures and confounding variables, and we used array, imputed, or whole genome sequence SNPs to obtain ‘SNP-heritability’ estimates. We show that SNP-heritability can be highly sensitive to assumptions about the frequencies, effect sizes, and levels of linkage disequilibrium of underlying causal variants, but that methods that bin SNPs according to minor allele frequency and linkage disequilibrium are less sensitive to these assumptions across a wide range of genetic architectures and possible confounding factors. These findings provide guidance for best practices and proper interpretation of published estimates.This analysis compares methods for estimating the heritability and genetic architecture of complex traits using whole-genome data. The results provide guidance for best practices and proper interpretation of published heritability estimates.

Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits

Understanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1−p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%). We applied this method to 25 UK Biobank diseases and complex traits (N = 113,851). All traits produced negative α estimates with 20 significantly negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true α values across traits had mean −0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically significant heterogeneity across traits (P = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the α model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects. Based on this analysis an average genome-wide negative selection coefficient on the order of 10−4 or stronger is necessary to explain the α values that we inferred.

Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits

There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10−31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10−35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.A new set of functional annotations based on fine-mapped molecular quantitative trait loci from GTEx and BLUEPRINT consortium data are enriched for disease heritability across 41 diseases and complex traits.

Reconciling S-LDSC and LDAK functional enrichment estimates

Recent work has highlighted the importance of accounting for linkage disequilibrium (LD)-dependent genetic architectures in analyses of heritability, motivating the development of the baseline-LD model used by stratified LD score regression (S-LDSC) and the LDAK model. Although both models include LD-dependent effects, they produce very different estimates of functional enrichment (with larger estimates using the baseline-LD model), leading to different interpretations of the functional architecture of complex traits. Here, we perform formal model comparisons and empirical analyses to reconcile these findings. First, by performing model comparisons using a likelihood approach, we determined that the baseline-LD model attains likelihoods across 16 UK Biobank traits that are substantially higher than the LDAK model. Second, we determined that S-LDSC using a combined model (unlike methods that use the LDAK or baseline-LD models) produces robust enrichment estimates in simulations under both the LDAK and baseline-LD models, validating the combined model as a gold standard. Third, in analyses of 16 UK Biobank traits, we determined that enrichment estimates obtained by S-LDSC using the combined model were nearly identical to those obtained by S-LDSC using the baseline-LD model (concordance correlation coefficient ρc = 0.99), but were larger than those obtained using LDAK (ρc = 0.54). Notably, LDAK enrichment estimates were much higher for a non-default version of LDAK that models SNPs in perfect LD differently by assigning non-zero weights to all SNPs. Our results support the use of the baseline-LD model and confirm the existence of functional annotations that are highly enriched for complex trait heritability.

Quantification of genetic components of population differentiation in UK Biobank traits reveals signals of polygenic selection

The genetic architecture of most human complex traits is highly polygenic, motivating efforts to detect polygenic selection involving a large number of loci. In contrast to previous work relying on top GWAS loci, we developed a method that uses genome-wide association statistics and linkage disequilibrium patterns to estimate the genome-wide genetic component of population differentiation of a complex trait along a continuous gradient, enabling powerful inference of polygenic selection. We analyzed 43 UK Biobank traits and focused on PC1 and North-South and East-West birth coordinates across 337K unrelated British-ancestry samples, for which our method produced close to unbiased estimates of genetic components of population differentiation and high power to detect polygenic selection in simulations across different trait architectures. For PC1, we identified signals of polygenic selection for height (74.5±16.7% of 9.3% total correlation with PC1 attributable to genome-wide genetic effects; P = 8.4×10−6) and red hair pigmentation (95.9±24.7% of total correlation with PC1 attributable to genome-wide genetic effects; P = 1.1×10−4); the bulk of the signal remained when removing genome-wide significant loci, even though red hair pigmentation includes loci of large effect. We also detected polygenic selection for height, systolic blood pressure, BMI and basal metabolic rate along North-South birth coordinate, and height and systolic blood pressure along East-West birth coordinate. Our method detects polygenic selection in modern human populations with very subtle population structure and elucidates the relative contributions of genetic and non-genetic components of trait population differences.