Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis

In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10-7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10-5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10-10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.

The genetic determinants of recurrent somatic mutations in 43,693 blood genomes

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.

Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis

Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis1. These lesions are precursors for blood cancers2-6, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, but this effect was not seen in clones with driver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimental knockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.

Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes

Body fat distribution is a major, heritable risk factor for cardiometabolic disease, independent of overall adiposity. Using exome-sequencing in 618,375 individuals (including 160,058 non-Europeans) from the UK, Sweden and Mexico, we identify 16 genes associated with fat distribution at exome-wide significance. We show 6-fold larger effect for fat-distribution associated rare coding variants compared with fine-mapped common alleles, enrichment for genes expressed in adipose tissue and causal genes for partial lipodystrophies, and evidence of sex-dimorphism. We describe an association with favorable fat distribution (p = 1.8 × 10^-09), favorable metabolic profile and protection from type 2 diabetes (~28% lower odds; p = 0.004) for heterozygous protein-truncating mutations in INHBE, which encodes a circulating growth factor of the activin family, highly and specifically expressed in hepatocytes. Our results suggest that inhibin βE is a liver-expressed negative regulator of adipose storage whose blockade may be beneficial in fat distribution-associated metabolic disease.

Germline Mutations in CIDEB and Protection against Liver Disease

BACKGROUND

Exome sequencing in hundreds of thousands of persons may enable the identification of rare protein-coding genetic variants associated with protection from human diseases like liver cirrhosis, providing a strategy for the discovery of new therapeutic targets.

METHODS

We performed a multistage exome sequencing and genetic association analysis to identify genes in which rare protein-coding variants were associated with liver phenotypes. We conducted in vitro experiments to further characterize associations.

RESULTS

The multistage analysis involved 542,904 persons with available data on liver aminotransferase levels, 24,944 patients with various types of liver disease, and 490,636 controls without liver disease. We found that rare coding variants in APOB, ABCB4, SLC30A10, and TM6SF2 were associated with increased aminotransferase levels and an increased risk of liver disease. We also found that variants in CIDEB, which encodes a structural protein found in hepatic lipid droplets, had a protective effect. The burden of rare predicted loss-of-function variants plus missense variants in CIDEB (combined carrier frequency, 0.7%) was associated with decreased alanine aminotransferase levels (beta per allele, -1.24 U per liter; 95% confidence interval [CI], -1.66 to -0.83; P = 4.8×10^-9) and with 33% lower odds of liver disease of any cause (odds ratio per allele, 0.67; 95% CI, 0.57 to 0.79; P = 9.9×10^-7). Rare coding variants in CIDEB were associated with a decreased risk of liver disease across different underlying causes and different degrees of severity, including cirrhosis of any cause (odds ratio per allele, 0.50; 95% CI, 0.36 to 0.70). Among 3599 patients who had undergone bariatric surgery, rare coding variants in CIDEB were associated with a decreased nonalcoholic fatty liver disease activity score (beta per allele in score units, -0.98; 95% CI, -1.54 to -0.41 [scores range from 0 to 8, with higher scores indicating more severe disease]). In human hepatoma cell lines challenged with oleate, CIDEB small interfering RNA knockdown prevented the buildup of large lipid droplets.

CONCLUSIONS

Rare germline mutations in CIDEB conferred substantial protection from liver disease. (Funded by Regeneron Pharmaceuticals.).

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2-4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.

Whole genome sequence analysis of platelet traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) initiative

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI's Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine-mapping in the MHC and genomewide

Because transethnic analysis may facilitate prioritization of causal genetic variants, we performed a genomewide association study (GWAS) of psoriasis in South Asians (SAS), consisting of 2,590 cases and 1,720 controls. Comparison with our existing European-origin (EUR) GWAS showed that effect sizes of known psoriasis signals were highly correlated in SAS and EUR (Spearman ρ = 0.78; p < 2 × 10-14). Transethnic meta-analysis identified two non-MHC psoriasis loci (1p36.22 and 1q24.2) not previously identified in EUR, which may have regulatory roles. For these two loci, the transethnic GWAS provided higher genetic resolution and reduced the number of potential causal variants compared to using the EUR sample alone. We then explored multiple strategies to develop reference panels for accurately imputing MHC genotypes in both SAS and EUR populations and conducted a fine-mapping of MHC psoriasis associations in SAS and the largest such effort for EUR. HLA-C*06 was the top-ranking MHC locus in both populations but was even more prominent in SAS based on odds ratio, disease liability, model fit and predictive power. Transethnic modeling also substantially boosted the probability that the HLA-C*06 protein variant is causal. Secondary MHC signals included coding variants of HLA-C and HLA-B, but also potential regulatory variants of these two genes as well as HLA-A and several HLA class II genes, with effects on both chromatin accessibility and gene expression. This study highlights the shared genetic basis of psoriasis in SAS and EUR populations and the value of transethnic meta-analysis for discovery and fine-mapping of susceptibility loci.

Whole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program

Many common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.

Family-based exome sequencing identifies rare coding variants in age-related macular degeneration

Genome-wide association studies (GWAS) have identified 52 independent variants at 34 genetic loci that are associated with age-related macular degeneration (AMD), the most common cause of incurable vision loss in the elderly worldwide. However, causal genes at the majority of these loci remain unknown. In this study, we performed whole exome sequencing of 264 individuals from 63 multiplex families with AMD and analyzed the data for rare protein-altering variants in candidate target genes at AMD-associated loci. Rare coding variants were identified in the CFH, PUS7, RXFP2, PHF12 and TACC2 genes in three or more families. In addition, we detected rare coding variants in the C9, SPEF2 and BCAR1 genes, which were previously suggested as likely causative genes at respective AMD susceptibility loci. Identification of rare variants in the CFH and C9 genes in our study validated previous reports of rare variants in complement pathway genes in AMD. We then extended our exome-wide analysis and identified rare protein-altering variants in 13 genes outside the AMD-GWAS loci in three or more families. Two of these genes, SCN10A and KIR2DL4, are of interest because variants in these genes also showed association with AMD in case-control cohorts, albeit not at the level of genome-wide significance. Our study presents the first large-scale, exome-wide analysis of rare variants in AMD. Further independent replications and molecular investigation of candidate target genes, reported here, would assist in gaining novel insights into mechanisms underlying AMD pathogenesis.

Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity

Large-scale human exome sequencing can identify rare protein-coding variants with a large impact on complex traits such as body adiposity. We sequenced the exomes of 645,626 individuals from the United Kingdom, the United States, and Mexico and estimated associations of rare coding variants with body mass index (BMI). We identified 16 genes with an exome-wide significant association with BMI, including those encoding five brain-expressed G protein-coupled receptors (CALCR, MC4R, GIPR, GPR151, and GPR75). Protein-truncating variants in GPR75 were observed in ~4/10,000 sequenced individuals and were associated with 1.8 kilograms per square meter lower BMI and 54% lower odds of obesity in the heterozygous state. Knock out of Gpr75 in mice resulted in resistance to weight gain and improved glycemic control in a high-fat diet model. Inhibition of GPR75 may provide a therapeutic strategy for obesity.

Pan-ancestry exome-wide association analyses of COVID-19 outcomes in 586,157 individuals

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.

Genetic architectures of proximal and distal colorectal cancer are partly distinct

OBJECTIVE

An understanding of the etiologic heterogeneity of colorectal cancer (CRC) is critical for improving precision prevention, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention. Known differences in molecular characteristics and environmental risk factors among tumors arising in different locations of the colorectum suggest partly distinct mechanisms of carcinogenesis. The extent to which the contribution of inherited genetic risk factors for CRC differs by anatomical subsite of the primary tumor has not been examined.

DESIGN

To identify new anatomical subsite-specific risk loci, we performed genome-wide association study (GWAS) meta-analyses including data of 48 214 CRC cases and 64 159 controls of European ancestry. We characterised effect heterogeneity at CRC risk loci using multinomial modelling.

RESULTS

We identified 13 loci that reached genome-wide significance (p<5×10-8) and that were not reported by previous GWASs for overall CRC risk. Multiple lines of evidence support candidate genes at several of these loci. We detected substantial heterogeneity between anatomical subsites. Just over half (61) of 109 known and new risk variants showed no evidence for heterogeneity. In contrast, 22 variants showed association with distal CRC (including rectal cancer), but no evidence for association or an attenuated association with proximal CRC. For two loci, there was strong evidence for effects confined to proximal colon cancer.

CONCLUSION

Genetic architectures of proximal and distal CRC are partly distinct. Studies of risk factors and mechanisms of carcinogenesis, and precision prevention strategies should take into consideration the anatomical subsite of the tumour.

Genome-wide analysis in 756,646 individuals provides first genetic evidence that ACE2 expression influences COVID-19 risk and yields genetic risk scores predictive of severe disease

SARS-CoV-2 enters host cells by binding angiotensin-converting enzyme 2 (ACE2). Through a genome-wide association study, we show that a rare variant (MAF = 0.3%, odds ratio 0.60, P=4.5×10^-13) that down-regulates ACE2 expression reduces risk of COVID-19 disease, providing human genetics support for the hypothesis that ACE2 levels influence COVID-19 risk. Further, we show that common genetic variants define a risk score that predicts severe disease among COVID-19 cases.

Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program

Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

Identifying Novel Susceptibility Genes for Colorectal Cancer Risk From a Transcriptome-Wide Association Study of 125,478 Subjects

BACKGROUND AND AIMS

Susceptibility genes and the underlying mechanisms for the majority of risk loci identified by genome-wide association studies (GWAS) for colorectal cancer (CRC) risk remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify putative susceptibility genes.

METHODS

Gene-expression prediction models were built using transcriptome and genetic data from the 284 normal transverse colon tissues of European descendants from the Genotype-Tissue Expression (GTEx), and model performance was evaluated using data from The Cancer Genome Atlas (n = 355). We applied the gene-expression prediction models and GWAS data to evaluate associations of genetically predicted gene-expression with CRC risk in 58,131 CRC cases and 67,347 controls of European ancestry. Dual-luciferase reporter assays and knockdown experiments in CRC cells and tumor xenografts were conducted.

RESULTS

We identified 25 genes associated with CRC risk at a Bonferroni-corrected threshold of P < 9.1 × 10-6, including genes in 4 novel loci, PYGL (14q22.1), RPL28 (19q13.42), CAPN12 (19q13.2), MYH7B (20q11.22), and MAP1L3CA (20q11.22). In 9 known GWAS-identified loci, we uncovered 9 genes that have not been reported previously, whereas 4 genes remained statistically significant after adjusting for the lead risk variant of the locus. Through colocalization analysis in GWAS loci, we additionally identified 12 putative susceptibility genes that were supported by TWAS analysis at P < .01. We showed that risk allele of the lead risk variant rs1741640 affected the promoter activity of CABLES2. Knockdown experiments confirmed that CABLES2 plays a vital role in colorectal carcinogenesis.

CONCLUSIONS

Our study reveals new putative susceptibility genes and provides new insight into the biological mechanisms underlying CRC development.

A catalog of associations between rare coding variants and COVID-19 outcomes

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19), a respiratory illness that can result in hospitalization or death. We investigated associations between rare genetic variants and seven COVID-19 outcomes in 543,213 individuals, including 8,248 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome-wide or when specifically focusing on (i) 14 interferon pathway genes in which rare deleterious variants have been reported in severe COVID-19 patients; (ii) 167 genes located in COVID-19 GWAS risk loci; or (iii) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, with results publicly browsable at https://rgc-covid19.regeneron.com.

PRF1 mutation alters immune system activation, inflammation, and risk of autoimmunity

BACKGROUND

Defective alleles within the PRF1 gene, encoding the pore-forming protein perforin, in combination with environmental factors, cause familial type 2 hemophagocytic lymphohistiocytosis (FHL2), a rare, severe autosomal recessive childhood disorder characterized by massive release of cytokines-cytokine storm.

OBJECTIVE

The aim of this study was to determine the function of hypomorph PRF1:p.A91V g.72360387 G > A on multiple sclerosis (MS) and type 1 diabetes (T1D).

METHODS

We cross-compare the association data for PRF1:p.A91V mutation derived from GWAS on adult MS and pediatric T1D in Sardinians. The novel association with T1D was replicated in metanalysis in 12,584 cases and 17,692 controls from Sardinia, the United Kingdom, and Scotland. To dissect this mutation function, we searched through the coincident association immunophenotypes in additional set of general population Sardinians.

RESULTS

We report that PRF1:p.A91V, is associated with increase of lymphocyte levels, especially within the cytotoxic memory T-cells, at general population level with reduced interleukin 7 receptor expression on these cells. The minor allele increased risk of MS, in 2903 cases and 2880 controls from Sardinia p = 2.06 × 10^-4, odds ratio OR = 1.29, replicating a previous finding, whereas it protects from T1D p = 1.04 × 10^-5, OR = 0.82.

CONCLUSION

Our results indicate opposing contributions of the cytotoxic T-cell compartment to MS and T1D pathogenesis.

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.

MEPE loss-of-function variant associates with decreased bone mineral density and increased fracture risk

A major challenge in genetic association studies is that most associated variants fall in the non-coding part of the human genome. We searched for variants associated with bone mineral density (BMD) after enriching the discovery cohort for loss-of-function (LoF) mutations by sequencing a subset of the Nord-Trøndelag Health Study, followed by imputation in the remaining sample (N = 19,705), and identified ten known BMD loci. However, one previously unreported variant, LoF mutation in MEPE, p.(Lys70IlefsTer26, minor allele frequency [MAF] = 0.8%), was associated with decreased ultradistal forearm BMD (P-value = 2.1 × 10⁻¹⁸), and increased osteoporosis (P-value = 4.2 × 10⁻⁵) and fracture risk (P-value = 1.6 × 10⁻⁵). The MEPE LoF association with BMD and fractures was further evaluated in 279,435 UK (MAF = 0.05%, heel bone estimated BMD P-value = 1.2 × 10⁻¹⁶, any fracture P-value = 0.05) and 375,984 Icelandic samples (MAF = 0.03%, arm BMD P-value = 0.12, forearm fracture P-value = 0.005). Screening for the MEPE LoF mutations before adulthood could potentially prevent osteoporosis and fractures due to the lifelong effect on BMD observed in the study. A key implication for precision medicine is that high-impact functional variants missing from the publicly available cosmopolitan panels could be clinically more relevant than polygenic risk scores.

Bone mineral density (BMD) is associated with fracture risk and many genetic loci with small effect sizes have been discovered by genome-wide association studies (GWAS). Here, the authors discover a large-effect rare loss-of-function genetic variant for BMD in the MEPE gene in the Norwegian HUNT study which replicates in the UK Biobank.

Whole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants

Chronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts

With very large sample sizes, biobanks provide an exciting opportunity to identify genetic components of complex traits. To analyze rare variants, region-based multiple-variant aggregate tests are commonly used to increase power for association tests. However, because of the substantial computational cost, existing region-based tests cannot analyze hundreds of thousands of samples while accounting for confounders such as population stratification and sample relatedness. Here we propose a scalable generalized mixed-model region-based association test, SAIGE-GENE, that is applicable to exome-wide and genome-wide region-based analysis for hundreds of thousands of samples and can account for unbalanced case-control ratios for binary traits. Through extensive simulation studies and analysis of the HUNT study with 69,716 Norwegian samples and the UK Biobank data with 408,910 White British samples, we show that SAIGE-GENE can efficiently analyze large-sample data (N > 400,000) with type I error rates well controlled.

Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.

Patients With High Genome-Wide Polygenic Risk Scores for Coronary Artery Disease May Receive Greater Clinical Benefit From Alirocumab Treatment in the ODYSSEY OUTCOMES Trial

BACKGROUND

Alirocumab, an antibody that blocks PCSK9 (proprotein convertase subtilisin/kexin type 9), was associated with reduced major adverse cardiovascular events (MACE) and death in the ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab). In this study, higher baseline levels of low-density lipoprotein cholesterol (LDL-C) predicted greater benefit from alirocumab treatment. Recent studies indicate high polygenic risk scores (PRS) for coronary artery disease (CAD) identify individuals at higher risk who derive increased benefit from statins. We performed post hoc analyses to determine whether high PRS for CAD identifies higher-risk individuals, independent of baseline LDL-C and other known risk factors, who might derive greater benefit from alirocumab treatment.

METHODS

ODYSSEY OUTCOMES was a randomized, double-blind, placebo-controlled trial comparing alirocumab or placebo in 18 924 patients with acute coronary syndrome and elevated atherogenic lipoproteins despite optimized statin treatment. The primary endpoint (MACE) comprised death of CAD, nonfatal myocardial infarction, ischemic stroke, or unstable angina requiring hospitalization. A genome-wide PRS for CAD comprising 6 579 025 genetic variants was evaluated in 11 953 patients with available DNA samples. Analysis of MACE risk was performed in placebo-treated patients, whereas treatment benefit analysis was performed in all patients.

RESULTS

The incidence of MACE in the placebo group was related to PRS for CAD: 17.0% for high PRS patients (>90th percentile) and 11.4% for lower PRS patients (≤90th percentile; P<0.001); this PRS relationship was not explained by baseline LDL-C or other established risk factors. Both the absolute and relative reduction of MACE by alirocumab compared with placebo was greater in high versus low PRS patients. There was an absolute reduction by alirocumab in high versus low PRS groups of 6.0% and 1.5%, respectively, and a relative risk reduction by alirocumab of 37% in the high PRS group (hazard ratio, 0.63 [95% CI, 0.46-0.86]; P=0.004) versus a 13% reduction in the low PRS group (hazard ratio, 0.87 [95% CI, 0.78-0.98]; P=0.022; interaction P=0.04).

CONCLUSIONS

A high PRS for CAD is associated with elevated risk for recurrent MACE after acute coronary syndrome and a larger absolute and relative risk reduction with alirocumab treatment, providing an independent tool for risk stratification and precision medicine.

Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis

Chronic kidney disease (CKD) is a growing health burden currently affecting 10–15% of adults worldwide. Estimated glomerular filtration rate (eGFR) as a marker of kidney function is commonly used to diagnose CKD. We analyze eGFR data from the Nord-Trøndelag Health Study and Michigan Genomics Initiative and perform a GWAS meta-analysis with public summary statistics, more than doubling the sample size of previous meta-analyses. We identify 147 loci (53 novel) associated with eGFR, including genes involved in transcriptional regulation, kidney development, cellular signaling, metabolism, and solute transport. Additionally, sex-stratified analysis identifies one locus with more significant effects in women than men. Using genetic risk scores constructed from these eGFR meta-analysis results, we show that associated variants are generally predictive of CKD with only modest improvements in detection compared with other known clinical risk factors. Collectively, these results yield additional insight into the genetic factors underlying kidney function and progression to CKD.

Estimated glomerular filtration rate (eGFR) is a measure of kidney function and used to characterize chronic kidney disease. Here, Graham et al. identify 53 novel loci for eGFR in a GWAS meta-analysis, a subset of which are associated with other common diseases, such as diabetes and hypertension, based on PheWAS.

Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10- 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10- 4, replication P = 6.7 × 10- 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.

Discovery of common and rare genetic risk variants for colorectal cancer

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10-8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.

Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.

Sharing of whole genome sequencing (WGS) data improves study scale and power, but data from different groups are often incompatible. Here, US genome centers and NIH programs define WGS data processing standards and a flexible validation method, facilitating collaboration in human genetics research.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

It is of great interest to estimate the joint effects of multiple variants from large scale meta-analyses, in order to fine-map causal variants and understand the genetic architecture for complex traits. The summary association statistics from participating studies in a meta-analysis often contain missing values at some variant sites, as the imputation methods may not work well and the variants with low imputation quality will be filtered out. Missingness is especially likely when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing methods for conditional meta-analysis do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly inflated type-I errors for conditional analysis, which will result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.

Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes

Angiopoietin-like 4 (ANGPTL4) is an endogenous inhibitor of lipoprotein lipase that modulates lipid levels, coronary atherosclerosis risk, and nutrient partitioning. We hypothesize that loss of ANGPTL4 function might improve glucose homeostasis and decrease risk of type 2 diabetes (T2D). We investigate protein-altering variants in ANGPTL4 among 58,124 participants in the DiscovEHR human genetics study, with follow-up studies in 82,766 T2D cases and 498,761 controls. Carriers of p.E40K, a variant that abolishes ANGPTL4 ability to inhibit lipoprotein lipase, have lower odds of T2D (odds ratio 0.89, 95% confidence interval 0.85–0.92, p = 6.3 × 10⁻¹⁰), lower fasting glucose, and greater insulin sensitivity. Predicted loss-of-function variants are associated with lower odds of T2D among 32,015 cases and 84,006 controls (odds ratio 0.71, 95% confidence interval 0.49–0.99, p = 0.041). Functional studies in Angptl4-deficient mice confirm improved insulin sensitivity and glucose homeostasis. In conclusion, genetic inactivation of ANGPTL4 is associated with improved glucose homeostasis and reduced risk of T2D.

Genetic variation in ANGPTL4 is associated with lipid traits. Here, the authors find that predicted loss-of-function variants in ANGPTL4 are associated with glucose homeostasis and reduced risk of type 2 diabetes and that Angptl4^−/− mice on a high-fat diet show improved insulin sensitivity.

Common α-globin variants modify hematologic and other clinical phenotypes in sickle cell trait and disease

Co-inheritance of α-thalassemia has a significant protective effect on the severity of complications of sickle cell disease (SCD), including stroke. However, little information exists on the association and interactions for the common African ancestral α-thalassemia mutation (-α3.7 deletion) and β-globin traits (HbS trait [SCT] and HbC trait) on important clinical phenotypes such as red blood cell parameters, anemia, and chronic kidney disease (CKD). In a community-based cohort of 2,916 African Americans from the Jackson Heart Study, we confirmed the expected associations between SCT, HbC trait, and the -α3.7 deletion with lower mean corpuscular volume/mean corpuscular hemoglobin and higher red blood cell count and red cell distribution width. In addition to the recently recognized association of SCT with lower estimated glomerular filtration rate and glycated hemoglobin (HbA1c), we observed a novel association of the -α3.7 deletion with higher HbA1c levels. Co-inheritance of each additional copy of the -α3.7 deletion significantly lowered the risk of anemia and chronic kidney disease among individuals with SCT (P-interaction = 0.031 and 0.019, respectively). Furthermore, co-inheritance of a novel α-globin regulatory variant was associated with normalization of red cell parameters in individuals with the -α3.7 deletion and significantly negated the protective effect of α-thalassemia on stroke in 1,139 patients with sickle cell anemia from the Cooperative Study of Sickle Cell Disease (CSSCD) (P-interaction = 0.0049). Functional assays determined that rs11865131, located in the major alpha-globin enhancer MCS-R2, was the most likely causal variant. These findings suggest that common α- and β-globin variants interact to influence hematologic and clinical phenotypes in African Americans, with potential implications for risk-stratification and counseling of individuals with SCD and SCT.

An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans

To characterize type 2 diabetes (T2D)-associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D case and 132,532 control subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference panel. Promising association signals were followed up in additional data sets (of 14,545 or 7,397 T2D case and 38,994 or 71,604 control subjects). We identified 13 novel T2D-associated loci (P < 5 × 10^-8), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common single nucleotide variants. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion and in adipocytes, monocytes, and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.

Exome-wide association study reveals novel psoriasis susceptibility locus at TNFSF15 and rare protective alleles in genes contributing to type I IFN signalling

Psoriasis is a common inflammatory skin disorder for which multiple genetic susceptibility loci have been identified, but few resolved to specific functional variants. In this study, we sought to identify common and rare psoriasis-associated gene-centric variation. Using exome arrays we genotyped four independent cohorts, totalling 11 861 psoriasis cases and 28 610 controls, aggregating the dataset through statistical meta-analysis. Single variant analysis detected a previously unreported risk locus at TNFSF15 (rs6478108; P = 1.50 × 10-8, OR = 1.10), and association of common protein-altering variants at 11 loci previously implicated in psoriasis susceptibility. We validate previous reports of protective low-frequency protein-altering variants within IFIH1 (encoding an innate antiviral receptor) and TYK2 (encoding a Janus kinase), in each case establishing a further series of protective rare variants (minor allele frequency < 0.01) via gene-wide aggregation testing (IFIH1: pburden = 2.53 × 10-7, OR = 0.707; TYK2: pburden = 6.17 × 10-4, OR = 0.744). Both genes play significant roles in type I interferon (IFN) production and signalling. Several of the protective rare and low-frequency variants in IFIH1 and TYK2 disrupt conserved protein domains, highlighting potential mechanisms through which their effect may be exerted.

Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels-1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population-based Nord Trøndelag Health Study (HUNT) from low-pass genome sequencing. We observed that the population-matched reference panel allowed for imputation of more population-specific variants with lower frequency (minor allele frequency (MAF) between 0.05% and 0.5%). The overall imputation accuracy from the population-specific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15-fold larger. These results recapitulate the value of population-specific reference panels for genotype imputation. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We observed that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of including only one version of each genetic variant, selected for having the highest imputation quality metric. This was particularly true for lower frequency variants (MAF < 1%), even after adjusting for the additional multiple testing burden.

A Fast and Accurate Algorithm to Test for Binary Phenotypes and Its Application to PheWAS

The availability of electronic health record (EHR)-based phenotypes allows for genome-wide association analyses in thousands of traits and has great potential to enable identification of genetic variants associated with clinical phenotypes. We can interpret the phenome-wide association study (PheWAS) result for a single genetic variant by observing its association across a landscape of phenotypes. Because a PheWAS can test thousands of binary phenotypes, and most of them have unbalanced or often extremely unbalanced case-control ratios (1:10 or 1:600, respectively), existing methods cannot provide an accurate and scalable way to test for associations. Here, we propose a computationally fast score-test-based method that estimates the distribution of the test statistic by using the saddlepoint approximation. Our method is much (∼100 times) faster than the state-of-the-art Firth's test. It can also adjust for covariates and control type I error rates even when the case-control ratio is extremely unbalanced. Through application to PheWAS data from the Michigan Genomics Initiative, we show that the proposed method can control type I error rates while replicating previously known association signals even for traits with a very small number of cases and a large number of controls.

Protein-Truncating Variants at the Cholesteryl Ester Transfer Protein Gene and Risk for Coronary Heart Disease

RATIONALE

Therapies that inhibit CETP (cholesteryl ester transfer protein) have failed to demonstrate a reduction in risk for coronary heart disease (CHD). Human DNA sequence variants that truncate the CETP gene may provide insight into the efficacy of CETP inhibition.

OBJECTIVE

To test whether protein-truncating variants (PTVs) at the CETP gene were associated with plasma lipid levels and CHD.

METHODS AND RESULTS

We sequenced the exons of the CETP gene in 58 469 participants from 12 case-control studies (18 817 CHD cases, 39 652 CHD-free controls). We defined PTV as those that lead to a premature stop, disrupt canonical splice sites, or lead to insertions/deletions that shift frame. We also genotyped 1 Japanese-specific PTV in 27561 participants from 3 case-control studies (14 286 CHD cases, 13 275 CHD-free controls). We tested association of CETP PTV carrier status with both plasma lipids and CHD. Among 58 469 participants with CETP gene-sequencing data available, average age was 51.5 years and 43% were women; 1 in 975 participants carried a PTV at the CETP gene. Compared with noncarriers, carriers of PTV at CETP had higher high-density lipoprotein cholesterol (effect size, 22.6 mg/dL; 95% confidence interval, 18-27; P<1.0×10-4), lower low-density lipoprotein cholesterol (-12.2 mg/dL; 95% confidence interval, -23 to -0.98; P=0.033), and lower triglycerides (-6.3%; 95% confidence interval, -12 to -0.22; P=0.043). CETP PTV carrier status was associated with reduced risk for CHD (summary odds ratio, 0.70; 95% confidence interval, 0.54-0.90; P=5.1×10-3).

CONCLUSIONS

Compared with noncarriers, carriers of PTV at CETP displayed higher high-density lipoprotein cholesterol, lower low-density lipoprotein cholesterol, lower triglycerides, and lower risk for CHD.

Optimal sequencing strategies for identifying disease-associated singletons

With the increasing focus of genetic association on the identification of trait-associated rare variants through sequencing, it is important to identify the most cost-effective sequencing strategies for these studies. Deep sequencing will accurately detect and genotype the most rare variants per individual, but may limit sample size. Low pass sequencing will miss some variants in each individual but has been shown to provide a cost-effective alternative for studies of common variants. Here, we investigate the impact of sequencing depth on studies of rare variants, focusing on singletons—the variants that are sampled in a single individual and are hardest to detect at low sequencing depths. We first estimate the sensitivity to detect singleton variants in both simulated data and in down-sampled deep genome and exome sequence data. We then explore the power of association studies comparing burden of singleton variants in cases and controls under a variety of conditions. We show that the power to detect singletons increases with coverage, typically plateauing for coverage > ~25x. Next, we show that, when total sequencing capacity is fixed, the power of association studies focused on singletons is typically maximized for coverage of 15-20x, independent of relative risk, disease prevalence, singleton burden, and case-control ratio. Our results suggest sequencing depth of 15-20x as an appropriate compromise of singleton detection power and sample size for studies of rare variants in complex disease.

Genetic studies of rare variants can help us understand the biology of human disease. With modern techniques and sufficient effort, it is possible to very accurately resolve any human genome, identifying most of its unique features. When funding is limited, applying these techniques to study human disease often involves a trade-off between examining more samples, at reduced accuracy per sample, or fewer samples, each at greater accuracy. We evaluate these trade-offs for studies of very rare variants, using both simulation and real data. We propose cost effective strategies for increasing our understanding of human disease.

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.

Reference-based phasing using the Haplotype Reference Consortium panel

Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing in a genotyped cohort, an approach that can yield high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium; HRC) using a new data structure based on the positional Burrows-Wheeler transform. We demonstrate that Eagle2 attains a ∼20× speedup and ∼10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2× the accuracy of 1000 Genomes-based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.

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.

Abstract 5230: Large scale whole genome sequencing with imputation into GWAS improves our understanding of the genetic architecture of colorectal cancer

Whole-genome sequencing (WGS) has started a new era in human genetics in which data can be used to more fully understand the role of genetic variation in common complex diseases, including the role of less frequent and rare variants and structural variation. To explore the impact of these variants on colorectal cancer risk we conducted the first large scale WGS study for colorectal cancer (CRC) including 1,961 CRC cases and 981 controls. These WGS data as well as those from the Haplotype Reference Consortium were imputed in 13,104 CRC cases and 15,521 controls with genome-wide association study (GWAS) data that are part of the Colorectal Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Focusing on rare and less frequent variants, insertions and deletions we observed potentially novel variants: a less frequent variant (MAF = 0.026) on chromosome 5 located in NREP/STARD4-AS1 (p = value 4E-08); and a novel rare multi-allelic variant (MAF = 0.003) on chromosome 9 near KLF9 and TRPM3 (p-value 2E-09; the other allele of this multi-allelic variant had a MAF of 0.0003 and p-value of 0.55). Furthermore, we observed an independent locus close to the known region 8q24 that was located upstream of GSDMC (MAF = 0.16, p-value 5E-08). Within the known region 8q23/EIF3H we identified several low frequency variants with similar MAF (0.0181 to 0.0204) including a 6bp deletion with p-values between 4E-08 and 1E-09 that were independent of the common variant signal in this region. In addition, we identified statistically significant (p&lt;5E-08) deletions, insertions, and an essential splice site within known GWAS loci that present interesting candidates for functional studies. We will follow up these findings in independent samples from the Colorectal Cancer Transdisciplinary Study (CORECT) and CCFR, as well as additional samples currently genotyped in GECCO. In conclusion, next generation sequencing combined with imputation in large GWAS data sets has the potential to identify novel low frequency and rare genetic variants, aid fine-mapping of known CRC susceptibility loci and point to interesting functional candidates.

Citation Format: Jeroen Huyghe, Sai Chen, Hyun M. Kang, Tabitha A. Harrison, Sonja I. Berndt, Stephane Bézieau, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Gallinger J. Steven, Stephen B. Gruber, Andrea Gsur, Michael Hoffmeister, Thomas J. Hudson, Loic Le Marchand, Polly A. Newcomb, John D. Potter, Conghui Qu, Martha L. Slattery, Joshua D. Smith, Emily White, Li Hsu, Goncalo R. Abecasis, Deborah A. Nickerson, Ulrike Peters. Large scale whole genome sequencing with imputation into GWAS improves our understanding of the genetic architecture of colorectal cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5230.

Abstract 4489: Using functional data from Roadmap Epigenomics to inform analysis of rare variants linked to gene expression in a large colorectal cancer study

To investigate the role of low frequency and rare genetic variation in colorectal cancer (CRC) susceptibility, the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colorectal Cancer Family Registry (CCFR) conducted whole genome sequencing and imputed into genome-wide association studies (GWAS) of 14,718 CRC cases and 12,186 controls. These data provide a unique opportunity to investigate rare variants, which contribute to the majority of the variation in the genome. To improve power for discovering rare CRC susceptibility variants (&lt;1% MAF), Roadmap Epigenomics data were used to construct biologically relevant testing sets of enhancers, promoters and exons for gene-based association testing across the genome. Since enhancers exert their effects by impacting expression of target genes, we defined enhancer-gene networks by linking enhancer(s) to target gene expression using Roadmap chromatin state maps and gene expression. Variants in linked enhancers from digestive and immune tissues were aggregated together with variants in the promoter and non-synonymous coding variants in the target gene. We tested 9,884 variant sets for association with CRC risk using the Mixed effects Score Test (MiST). Our most significant findings are for acyl-Coenzyme A dehydrogenase, C-2 to C-3 short chain precursor-ACADS (p = 1×10−4), AlkB homologs, including AlkB homolog 1-ALKBH1 (p = 2×10−4), and SRA stem-loop interacting RNA binding protein-SLIRP (p = 2×10−4). We will replicate these findings within the Colorectal Cancer Transdisciplinary Study (CORECT), as well as additional samples currently genotyped in CCFR and GECCO (over 25,000 CRC cases and controls). Although the top findings are statistically non-significant in this initial dataset, each of these genes linked to molecular pathways implicated in CRC carcinogenesis (fatty acid metabolism, DNA/RNA repair, and Nuclear Receptor signaling pathway, which interacts with the Wnt, beta-catenin pathways to result in a diverse array of cellular effects including altered cellular adhesion, tissue morphogenesis, and oncogenesis). Our current findings suggest that although functional insight can improve power for novel discovery, even larger sample sizes and/or pathway-based analyses are necessary to understand the role of rare variants in CRC carcinogenesis.

Citation Format: Stephanie A. Bien, Tabitha A. Harrison, Paul L. Auer, Flora Qu, Jeroen Huyghe, Barbara Banbury, Peyton Greenside, Goncalo R. Abecasis, Sonja I. Berndt, Stephane Bézieau, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Sai Chen, Joshua D. Smith, Loic Le Marchand, Christopher Carlson, Polly A. Newcomb, Christian Fuchsberger, Marty L. Slattery, Hyun M. Kang, Emily White, John Potter, Steven J. Gallinger, Michael Hoffmeister, Stephen B. Gruber, Deborah A. Nickerson, Ulrike Peters, Anshul Kundaje, Li Hsu. Using functional data from Roadmap Epigenomics to inform analysis of rare variants linked to gene expression in a large colorectal cancer study. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4489.