Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma.

Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (β = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.

Introduction

Intraocular pressure (IOP) is a modifiable risk factor and predictive measure for glaucoma[1-4] (S1 Fig). Genome-wide association studies (GWAS) have commonly used this endophenotype that exhibits high genetic correlation (rg = 0.71) to glaucoma, as an approach to prioritize genetic variants likely to contribute to disease risk[5]. More than 68 independent loci have been implicated with intraocular pressure by meeting the GWAS significance threshold of association (P < 5x10-8) [5-8], and a subset have reached genome-wide significance for glaucoma. For these discoveries, like most GWAS results, it has proven challenging to infer the functional consequences of common variant associations beyond cases where protein-altering variants have been directly implicated. Protein-altering variants, generally the strongest-acting genetic variants in medical genetics, include missense substitutions and protein-truncating variants, and understanding their functional consequences provides insight into the therapeutic effects of inhibiting or down-regulating the gene in which they reside[9]. Thus, identifying protein-altering variants that confer protection from disease holds particular promise for identifying therapeutic targets.

Here we leverage two population cohorts that provide complementarity for glaucoma gene discovery (Fig 1). First, UK Biobank has obtained IOP measurements in approximately 128,000 individuals in addition to case-control status for glaucoma from hospital in-patient and verbal questionnaire data in over 500,000 individuals[10-12]. Second, FinnGen has directly genotyped and aggregated disease outcomes in over 176,000 individuals from Finland, an isolated population with recent bottlenecks that offers an unprecedented advantage for studying rare variants in complex diseases[13]. With clinic-based recruitment focused on several areas including ophthalmology, and with 31.1% of the collection above age 70, FinnGen is particularly well-powered for aging-associated endpoints. We, therefore, conduct targeted association analysis with IOP measurements in UK Biobank (N = 82,253, S1 Table) to identify rare protein-altering variants that reduce IOP, and test whether those variants or others in the same genes, also confer protection to glaucoma in FinnGen (6,537 cases and 170,362 controls) and UK Biobank (4,238 cases and 250,660 controls not included in the initial IOP association analysis). The multi-cohort allelic series analysis of protein-altering variants in ANGPTL7 in a total of 10,806 glaucoma patients and over 400,000 controls identifies a significant lowering effect on IOP and protective association with glaucoma. By analyzing putative loss-of-function variants, we find concordant effect directions with the missense substitutions, suggesting that the protective mechanism may reside in the loss of gene function.

Fig 1

The overview of the study based on 514,050 individuals in the UK Biobank and FinnGen cohorts.

We identified the association between ANGPTL7 and intraocular pressure (IOP) phenotypes using the genome-wide association analysis for rare (0.01% < MAF < 1%) protein-altering variants outside of MHC region in UK Biobank and applied burden and dispersion test (Analysis 1). In FinnGen, we discovered a Finnish enriched allele, p.Arg220Cys, in ANGPTL7 and performed association and subtype analysis of glaucoma (Analysis 2). In the UK Biobank, we replicated the associations between ANGPTL7 and glaucoma using the individuals that are not included in Analysis 1 (Analysis 3). OR corresponds to the odds ratio.

Results

We conducted protein-altering variant association analysis with IOP, as measured via corneal-compensated and Goldmann-correlated tonometry, in 82,253 unrelated White British individuals in UK Biobank dataset (S2–S4 Figs, Methods) [14]. Across 41,590 rare (0.01% < MAF < 1%) protein-altering variants outside of the MHC region with genotyping array data in UK Biobank, we performed association analysis to scan for variants with IOP-lowering effects. Specifically, we took the median of the left and right eye IOP measurements, applied quantile normalization and used a generalized linear model implemented in PLINK[13] with age, sex, and the first 4 genotype principal components (PCs) as covariates (Methods). We identified one protein-altering variant significantly associated with lower both IOP measurements below the Bonferroni-corrected P value < 1.0x10-6, a missense substitution (p.Gln175His) in ANGPTL7 (P = 5.96x10-9 and 1.07x10-13, β = -0.20 and -0.16 SD 95% CI: [-0.21, -0.10] and [-0.25, -0.15], -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes for corneal compensated and Goldman-correlated IOP, respectively, Fig 1, Tables 1 and 2).

Table 1

ANGPTL7 IOP protein-altering variant association in UK Biobank.

The association statistics for corneal compensated IOP and Goldman-correlated IOP are shown. Variant includes chromosome, position, reference, and alternate allele (hg19). rsID—the rs identifier of the genetic variant. HGVSp—the HGVS amino acid nomenclature. MAF—the minor allele frequency in UK Biobank white British population. Beta—estimated regression coefficient with 95% confidence intervals. P—p-value of association.

Variant (rsID)	HGVSp	MAF (UKB)	corneal compensated IOP (INI2005254)		Goldman-correlated IOP (INI2005255)
			Beta SD[95% CI]	P	Beta SD[95% CI]	P
1:11252357:A:G(rs200058074)	p.Gln136Arg	.054%	0.012 [-0.20, 0.23]	9.1x10-1	-0.030 [-0.25, 0.19]	7.8x10-1
1:11252369:G:A(rs28991002)	p.Arg140His	.25%	-0.071 [-0.17, 0.022]	1.3x10-3	-0.15 [-0.24, -0.055]	1.9x10-3
1:11253684:G:T(rs28991009)	p.Gln175His	.81%	-0.16 [-0.21, -0.10]	6.0x10-9	-0.20 [-0.25, -0.15]	1.1x10-13
1:11253688:C:T(rs143435072)	p.Arg177Ter	.041%	-0.13 [-0.37, 0.12]	3.0x10-1	-0.29 [-0.53, -0.038]	2.4x10-2

Table 2

ANGPTL7 allelic series association summary in UK Biobank and FinnGen.

Variant: the rs identifier (rsID), the amino acid nomenclature (HGVSp), and genomic coordinate (CHR for chromosome and POS for the position in hg19), as well as reference (REF) and alternate (ALT) alleles, are shown. Dosage—genotype of individuals for protein and nucleotide sequences. Carrier frequency—carrier frequency in UK Biobank (UK) and FinnGen (Finland) for the respective genotype dosage. N with IOP—number of individuals in UK Biobank with intraocular pressure measurements corresponding to the genotype dosage. Effect size estimates—reported effect size estimates. IOP (mmHg) [95% CI]—unstandardized estimates of effect size on corneal-compensated and Goldmann-correlated intraocular pressure measurements (NB: standardized estimated effect sizes may have lower p-values due to normalization procedure). OR for glaucoma—estimate odds ratio on glaucoma risk for the respective genotype dosage. NS non-significant (p > 0.1). Effect sizes always reported with respect to alternate allele dosage.

Variant	Dosage	Carrier frequency		N with IOP in UK	Effect size estimates [95% CI]
rsIDHGVSpCHR:POS:REF:ALT	Proteinnucleotide	UK	Finland		IOP (mmHg)		OR for glaucoma
					corneal-compensated(INI2005254)	Goldmann-correlated (INI2005255)
rs200058074p.Gln136Arg1:11252357:A:G	Gln/ArgA/G	0.11%	NA	80	-0.051[-0.89, 0.79]NS	-0.19[-0.99, 0.61]NS	NS
rs28991002p.Arg140His1:11252369:G:A	Arg/HisG/A	0.51%	0.35%	427	-0.23[-0.59, 0.14]NS	-0.51[-0.86, -0.17]**	NS
rs28991009p.Gln175His1:11253684:G:T	Gln/HisG/T	1.43%	0.24%	1355	-0.53[-0.73, -0.32]***	-0.67[-0.87, -0.47]***	0.64[0.48, 0.87] **
	His/HisT/T	0.01%	NA	5	-3.40[-6.8, -0.042*	-2.37[-5.6, 0.84]NS	NS
rs143435072p.Arg177Ter1:11253688:C:T	Arg/TerC/T	0.07%	NA	62	-0.46[-1.4, 0.5]NS	-0.95[-1.9, -0.034]*	NS
rs147660927p.Arg220Cys1:11253817C:T	Arg/CysC/T	NA	7.82%	NA	NA	NA	0.67 ***
	Cys/CysT/T	NA	0.21%	NA	NA	NA	0.31 *

Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘NS’ 1

Based on this finding, we assessed whether any additional rare variant associations in ANGPTL7 were present. We found three additional independent rare protein-altering variants in ANGPTL7 (MAF < 0.25%) including a premature stop-gain allele (p.Arg177Ter, MAF = 0.041%, Table 1). Collectively, these three variants showed a marginally significant association with lower one of the IOP measurements (P = 0.24 and 0.0012 for corneal compensated and Goldman-correlated IOP, respectively, Fig 1), with the protein-altering allele p.Arg140His also showing a marginally significant effect on its own (P = 1.3x10-3 and 1.9x10-3 for corneal compensated and Goldman-correlated IOP, respectively, Table 1). Genotyping intensity plots and the concordance of genotype calls from the array and whole-exome sequencing data were manually inspected to ensure high quality and consistent genotyping (S5 Fig, S2 Table, Methods) and alleles were confirmed to be independent (pairwise r2 < 10−4 for each, S3 Table, Methods). The burden and dispersion test showed significant p-values for ANGPTL7 (P = 1.88x10-7 [burden], 1.43x10-8 [dispersion]; and 1.44x10-14 [burden], 2.89x10-15 [dispersion] for corneal compensated and Goldman-correlated IOP, respectively, Fig 1). Collectively, those four alleles explain 0.03% and 0.07% of phenotypic variation for corneal-compensated and Goldmann-correlated tonometry IOP measures (S4 Table). These signals were consistently observed in corneal-compensated and Goldmann-correlated tonometry IOP measures for both left and right eyes (S5 Table), which is expected as the genetic correlation among those range from 0.75 to 1.0 (S6 Table).

In Khawaja et al. the authors identified a significant association in the same genomic region (chr1p36.22), which they label as the UBIAD1 region [6]. The associated region, defined by recombination rates, encompasses a relatively large area around SNP rs143038218 and includes the ANGPTL7 gene. We asked whether this signal could be explained by our allelic series of protein-altering variants in ANGPTL7. Linkage disequilibrium analysis shows that p.Gln175His has an r2 = 0.83 suggesting that the ANGPTL7 protein-altering variant was indeed responsible for the association signal observed in Khawaja et al, whereas p.Arg177Ter, p.Arg140His, and p.Gln136Arg all have r2 of approximately 0 and are under linkage-equilibrium as minor alleles are observed in different haplotypes and contribute independently to the association we observe in ANGPTL7 against IOP.

We next asked whether any of these putative IOP-lowering genetic variants showed effects consistent with reducing glaucoma risk. We focused on unrelated White British individuals that do not have IOP measures (4,238 cases and 250,660 controls, S6 Fig). For p.Gln175His in ANGPTL7, using logistic regression analysis with age, sex, and principal components (PC1-PC4) as covariates, we estimated that the variant lowers glaucoma risk by 34% (P = 0.00543; OR = 0.66 [95% CI: 0.366–0.954], Table 2). The three additional protein-altering variants did not significantly confer protection against glaucoma (burden test P = 0.77). This is consistent with power calculations, using Genetic Power Calculator [15], where our power to detect association for rare variants with a composite allele frequency of 0.345% and a binary trait in 4,238 cases and 250,660 controls at alpha = 0.05, and 0.001, i.e. P < .05 and .001, with OR of 0.7 (30% reduction in risk) is equal to 62% and 15.2%, respectively.

We then sought evidence from the FinnGen dataset that either the same or novel Finnish-enriched protein-altering variants would confirm the association of ANGPTL7 with protection from glaucoma. This additional ANGPTL7 association data, obtained in 6,537 glaucoma patients and 170,362 controls, provided strong support that protein-altering variants in ANGPTL7 protect against glaucoma (case definitions described in S7 Table). Specifically, we found that the p.Gln175His substitution has nominal evidence of association (P = 0.009, OR = 0.47) despite the variant only being present at a minor allele frequency of 0.1% in this Finnish cohort (8-fold depleted compared to UK Biobank). The remaining protein-altering variants in ANGPTL7 tested in UK Biobank were not found in the FinnGen dataset. Confirmation of an ANGPTL7 effect on glaucoma risk was seen in data from an independent Finnish-specific protein-altering missense substitution, p.Arg220Cys, which was strongly associated with protection from glaucoma (P = 2.0x10-12, OR = 0.71 [95% CI: 0.64–0.78], S7 Fig). Of note, this observation is advantaged by the property that p.Arg220Cys is found at a greater than 50-fold increased frequency in Finland compared with other populations [16], reinforcing the value of isolated, bottlenecked populations in which the allele frequency spectrum is intensely concentrated on the minority of variants passing through the bottleneck.

While registry-based diagnoses in FinnGen do not yet contain detailed ophthalmologic records, a subset of 3,375 glaucoma patients had been recorded in special health care as having primary open-angle glaucoma (POAG). In this sub-group, a stronger effect was observed (P = 1.3x10-8, OR = 0.68) versus those glaucoma cases without a definitive record of POAG (OR = 0.77 [95% CI: 0.67–0.88]), reminiscent of the stronger risk effects seen at the myocilin (MYOC) gene and other established genes in the POAG subgroup [17]. Furthermore, we find protective association to exfoliation glaucoma (P = 6.7x10-5, OR = 0.64), primary angle-closure glaucoma (P = 0.0016, OR = 0.59), and normotensive glaucoma (P = 0.07, OR = 0.78, case n = 653, Fig 1).

Given the Finnish enrichment of the known strong glaucoma risk allele, p.Gln368Ter, in MYOC (MAF in Finland = 0.3%, MAF in Non-Finnish European = 0.16%, reference sequence: NM_00026), we next asked whether carriers have risk reduced if they carry ANGPTL7 p.Arg220Cys. In FinnGen, we estimate that 7.0% of carriers for MYOC p.Gln368Ter variant is POAG cases in comparison to 2% for non-carriers. In the presence of ANGPTL7 p.Arg220Cys, only 1.3% of individuals are POAG cases, and only 2 of 86 (2.3%) who carry both MYOC risk and ANGPTL7 protective variants were POAG cases (S8 Table). This suggests ANGPTL7 protection extends to the MYOC risk group but the small counts preclude any definitive statement regarding interaction (P = 0.318, for interaction term in a logistic regression model)—given the limited number of double-carriers, larger case-control series are needed to refine our understanding as to whether ANGPTL7 p.Arg220Cys variant modifies the glaucoma risk conferred by p.Gln368Ter in MYOC.

Access to genotype data in over 330,000 individuals from the UK and 176,000 from the Finnish group enabled us to identify rare protein-altering homozygotes. In UK Biobank, we found 28 individuals homozygous for the p.Gln175His allele, consistent with Hardy-Weinberg expectation (n = 22.6), where we estimated a -3.40 and -2.37 mmHg drop for corneal compensated and Goldman-correlated IOP, respectively, compared to the mean IOP levels. Furthermore, the oldest reached the age of 80 and one of the 28 died (age 65). In FinnGen we found 343 individuals homozygous for the p.Arg220Cys allele, the oldest reached the age of 98, with no depletion of homozygotes compared with Hardy-Weinberg equilibrium expectation. There was no significant association of the homozygous genotype with a decreased lifespan. We did not observe homozygous p.Arg220Cys association across disease endpoints in FinnGen (Bonferroni-corrected p > 0.05, S1 Data). Together this indicates that having two copies with p.Gln175His or p.Arg220Cys in ANGPTL7 is compatible with a normal lifespan. To assess the potential impacts of those protein-truncating variants on reproductive fitness, we assessed the association of p.Gln175His with the number of live births and the number of children fathered and found no significant association (P > 0.05/4, S9 Table). Through phenome-wide association analysis (PheWAS), we did not find any significant association for non-eye phenotypes (P>1.0 x 10−5 for both in UK Biobank and FinnGen, S10 Table, S2 and S3 Data). Hence, we did not find any severe medical consequences that would be of obvious concern in developing a therapeutic to mimic the effect of these alleles.

ANGPTL7, a five-exon protein-coding gene, encodes the Angiopoietin-related protein 7, which is expressed in several human tissues including the trabecular meshwork, cornea, and retina [18-20]. We examined proteomics expression data in normal tissues and cell lines from ProteomicsDB and MOPED[21,22] and found vitreous humor tissue-specific expression of ANGPTL7 (log10 ppm = 1, S8 Fig).

Discussion

This study establishes strong genetic evidence for the involvement of ANGPTL7 in glaucoma risk in which a powerful allelic series, including multiple low-frequency missense substitutions and a single premature stop-gain substitution, is conclusively associated with reduced disease risk and endophenotype-lowering effects. Our results highlight the benefit of rare protein-altering variant analysis using multiple large cohorts, especially when the population history of the participating cohort experienced a bottleneck, which enables enrichment of rare alleles as we report with the ANGPTL7 p.Arg220Cyc allele [13]. In Finland, the most common glaucoma subtypes are POAG and the secondary exfoliation glaucoma. The main difference in glaucoma prevalence is that in Finland the exfoliation glaucoma is much more prevalent (31%) than in the UK [23]. The prevalence of POAG is similar in Finland than in other European populations. The prevalence is heavily affected by age. In one Finnish cohort study, among individuals aged 70 years or older, the prevalence of POAG was approximately 7% [24]. Relative similar prevalence for POAG is reported in European populations [25]. Many patients with POAG are undiagnosed so the prevalence is affected by sampling methods (i.e. cohort or diagnosis reported). The population cohorts from founder populations enable future recall studies focusing on individuals homozygous for the allele, which can eventually improve our understanding of the mechanism by which ANGPTL7 disruption leads to protection to glaucoma risk and lowering of IOP. The discovery of two independent protein-altering alleles with directionally consistent effects from the two analyzed populations increases our confidence in the gene’s causal link to glaucoma.

Recent studies have associated ANGPTL proteins with cardiometabolic phenotypes [26-30]. Although it has been proposed that ANGPTL7 levels are increased in obesity (and reduced after physical exercise), we do not observe any evidence of genetic association in either UK Biobank or FinnGen to support this hypothesis [31].

ANGPTL7 overexpression in primary human trabecular meshwork cells was found to alter the expression of relevant trabecular meshwork proteins of the extracellular matrix, including fibronectin, collagens type I, IV, and V, myocilin, versican, and MMP1, and ANGPTL7 protein was increased as the disease progressed in POAG beagle dogs [18]. The tissue-specific protein expression data suggest that further work in dissecting the role of ANGPL7 in all possible cell types in the eye is warranted.

When combined with the previously-reported associations with IOP and glaucoma, our results provide compelling genetic evidence of the role of ANGPTL7 in glaucoma and its subtypes including exfoliation, primary open-angle, and primary angle-closure, which may come in contrast to prior findings with lack of overlap between POAG risk and IOP loci [32]. In the context of the other established variants in glaucoma, including the protein-truncating variants in MYOC, p.Gln175His and the 57-fold Finnish-enriched p.Arg220Cys variants in ANGPTL7 exert a comparable protective effect. While our genetic discovery provides compelling evidence of involvement of ANGPTL7 in glaucoma, several important questions remain to be answered before its eventual clinical translation. First, we were not able to assess whether the missense variants are complete loss-of-function, partial loss-of-function variants, dominant negative, or gain of function given the data we have at hand. Although we do have a predicted protein-truncating variant, p.Arg177Ter, with nominal evidence of association to IOP and an estimated effect consistent with the missense substitutions, it is challenging to draw conclusions about its functional consequence from in silico predictions, as we have reported in earlier studies assessing when PTVs trigger degradation pathways like nonsense-mediated decay [33]. Second, it is unclear in which cell types these variants are acting on to confer the protective and IOP lowering effects. We anticipate that ANGPTL7 may be acting in the trabecular meshwork given its high expression in both adult and fetal trabecular meshwork (> 3000 FPKM) [34], we see high expression in both adult and fetal cornea (>200 FPKM), which introduces some challenges as how we interpret its functional role, and we hypothesize that given its high expression in cornea it may be one reason why we see stronger evidence of association in IOP Goldman correlated measures compared to corneal compensated IOP. Additionally, future studies should assess whether ANGPTL7 variants modify the progression of glaucoma, for example, whether ANGPTL7 carriers are less likely to go from glaucoma diagnosis to potential surgery. Although we are aggregating these data, we are thus far unable to draw definitive conclusions.

Because of the strong protective effect associated with the ANGPTL7 protein-altering variants (S9 Fig), further studies of ANGPTL7 inhibition and the specific action of these variant proteins should be useful in understanding the mechanism by which glaucoma protection occurs and whether this reveals a promising therapeutic opportunity similar to that which has been realized from the examples of PCSK9, APOC3 and cardiovascular disease [35-37]. Given the rapidly evolving field of gene editing and siRNA, we can only speculate that if the effect is truly loss-of-function and that gene inhibition is an appropriate strategy then these therapeutic modalities will be especially relevant. Therapeutic delivery is also a complicated challenge. Although injection to the eye is currently commonplace in practice, it is unclear whether different therapeutic modalities, e.g. antibody, siRNA, CRISPR, base-editing would be appropriate, and whether the duration of the treatment would be sufficiently durable to be effective to prevent extremely frequent injections or competitive against current therapeutic modalities. New drug delivery technologies are of interest and it is clear that a durable and efficient mode of delivery that mimics the protective effect of these mutations is an attractive strategy. Our genetic data from ANGPTL7 homozygotes with up to a 69% risk reduction for all glaucoma and 80% risk reduction for primary open-angle glaucoma suggest that this is likely to be a safe and effective strategy for therapeutic intervention.

Methods

Compliance with ethical regulations and informed consent

This research has been conducted using the UK Biobank Resource under Application Number 24983, “Generating effective therapeutic hypotheses from genomic and hospital linkage data” (criteriahttp://www.ukbiobank.ac.uk/wp-content/uploads/2017/06/24983-Dr-Manuel-Rivas.pdf). Based on the information provided in Protocol 44532 the Stanford IRB has determined that the research does not involve human subjects as defined in 45 CFR 46.102(f) or 21 CFR 50.3(g). All participants of UK Biobank provided written informed consent (more information is available at https://www.ukbiobank.ac.uk/2018/02/gdpr/). For the Finnish Institute of Health and Welfare (THL) driven FinnGen preparatory project (here called FinnGen), all patients and control subjects had provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, older cohorts were based on study specific consents and later transferred to the THL Biobank after approval by Valvira, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Valvira. The Ethical Review Board of the Hospital District of Helsinki and Uusimaa approved the FinnGen study protocol Nr HUS/990/2017. The FinnGen preparatory project is approved by THL, approval numbers THL/2031/6.02.00/2017, amendments THL/341/6.02.00/2018, THL/2222/6.02.00/2018 and THL/283/6.02.00/2019. All DNA samples and data in this study were pseudonymized.

Genome-wide association analysis in UK Biobank

Population stratification in UK Biobank

We used genotype data from the UK Biobank dataset release version 2 and the hg19 human genome reference for all analyses in the study. To minimize the variabilities due to population structure in our dataset, we restricted our analyses to include 337,151 White British individuals (S2 Fig) based on the following five criteria [11,38] reported by the UK Biobank in the file “ukb_sqc_v2.txt”:

self- reported white British ancestry (“in_white_British_ancestry_subset” column)

used to compute principal components (“used_in_pca_calculation” column)

not marked as outliers for heterozygosity and missing rates (“het_missing_outliers” column)

do not show putative sex chromosome aneuploidy (“putative_sex_chromo- some_aneuploidy” column)

have at most 10 putative third-degree relatives (“excess_relatives” column).

Of note, we included the entire age range of the UK Biobank cohort for our analysis to maximize the power of association analysis.

Intraocular pressure phenotype definitions in UK Biobank

We focused on Goldmann-correlated and corneal-compensated IOP measurements of left and right eyes from UK Biobank (Field IDs: 5254, 5255, 5262, and 5263, S1 Table). For each field, there were up to two measurements, which corresponds to the initial assessment visit (2006–2010) and the first repeat assessment visit (2012–13). We additionally defined the median Goldmann-correlated and corneal-compensated IOP phenotypes by taking the median of up to 4 measurements for each (Global Biobank Engine phenotype IDs: INI2005254 and INI2005255, S1 Table), combining the left and right eye measurements.

Rare protein-altering variant genome-wide association scan for IOP

For the white British individuals (n = 337,151) in UK Biobank [11], we applied genome-wide association analysis for directly genotyped variants and phenotypes with inverse-normal transformation (--pheno-quantile-normalize option) using generalized linear regression model implemented in PLINK v2.00aLM (12 Nov. 2019) with age, sex, types of genotyping array, and the first 4 genotype principal components, where array is an indicator variable that indicates whether the individual was genotyped using the UK BiLEVE array or the UK Biobank array, as described elsewhere [38,39]. The inverse-normal transformation (--pheno-quantile-normalize option in PLINK2) is a non-parametric phenotype normalization procedure and it forces the phenotype to a standard normal distribution, preserving just the quantiles. For example, if the original phenotype values are 9, 4, 9, and 7 in that order, the quantiles are 0.75, 0.125, 0.75, 0.375, and the transformed phenotype values are the inverse-normal-cdf of each of the quantile value (https://www.cog-genomics.org/plink/2.0/data#quantile_normalize). The genome-wide association summary statistics are available at NIH’s instance of figshare.

Glaucoma association analysis in individuals without IOP measurements

To assess the potential effects of identified putative IOP-lowering genetic variants on glaucoma risk, we applied the genome-wide association analysis for glaucoma (Global Biobank Engine phenotype ID: HC276) focusing on 254,898 individuals (4,238 cases and 250,660 controls) in UK Biobank who do not have any of the IOP measurements (Fig 1). The glaucoma phenotype was previously defined as a part of “high confidence” disease outcome phenotypes by combining disease diagnoses (UK Biobank Field ID 41202, 41204, 40001, and 40002) from the UK National Health Service Hospital Episode Statistics (ICD10 codes: H40.[0-6,8,9], H42.8, and Q15.0) with self-reported non-cancer diagnosis questionnaire (UK Biobank Field ID 20002), as summarized as an UpSet plot in S6 Fig [11,12,40].

We used logistic regression with the firth-fallback option using a generalized linear regression model implemented in PLINK v2.00aLM (12 Nov. 2019) with age, sex, types of genotyping array, and the first 4 genotype principal components. The genome-wide association summary statistics are available at NIH’s instance of figshare[41].

Targeted regression analysis of identified rare variants in ANGPTL7

To assess the impacts of identified rare variants in ANGPTL7 on unnormalized IOP, we performed linear regression for IOP. Specifically, we used the following formula and called the linear model implemented in R.

lm (IOP ~ age + as.factor(sex) + as.factor(Array) + PC1 + PC2 + PC3 + PC4, as.factor(SNV), family = binomial(link = "logit"))

Genotyping quality control in UK Biobank

Manual inspection of intensity plots

For the identified rare (0.01% < MAF < 1%) protein-altering variants in ANGPTL7 (reference sequence: NM_021146), we generated and inspected intensity plots with McCarthy Group’s ScatterShot using “UKB—All Participants” module [42].

Variant-calling consistency analysis

For individuals with whole-exome sequencing data (n = 49,960), we extracted the genotype calls of coding variants in ANGPTL7 using PLINK v2.00aLM (2 April 2019) and compared the consistency between the array-genotyped and whole-exome sequencing dataset [39,43].

Burden and dispersion tests of rare protein-altering variants

To assess associations with rare protein-altering variants, we performed a burden and dispersion test implemented in multiple rare variants and phenotypes (MRP) package with farebrother option (https://github.com/rivas-lab/ANGPTL7/tree/master/gene_based_test) [44,45]. The approach implemented in the MRP package is a generalization of the gene-based test for a single phenotype described in the Supplementary Material of Band et al. [46] Region-based test and subsection labeled calculating p-values. We used the GWAS summary statistics of rare (0.01% < MAF < 1%) protein-altering variants characterized form the procedure above as the input data and performed the genome-wide burden and dispersion tests. The results of the burden and dispersion analysis are publicly available at NIH’s instance of figshare[47].

Independence analysis of alleles

Pairwise r2 computation within British individuals in UK Biobank

We computed pairwise r2 for the identified rare protein-altering variants in ANGPTL7 within British individuals in UK Biobank using PLINK v1.90b6.7 64-bit (2 Dec 2018) with--ld hwe-midp subcommand [39].

Number of individuals with the combination of genotypes in UK Biobank

Using the extracted genotype calls from for the identified rare protein-altering variants in ANGPTL7 (see Variant-calling consistency analysis section), we counted the number of British individuals by the combination of genotypes. We computed the expected number of individuals under Hardy-Weinberg equilibrium model and the independence assumption:

The expected frequencies of REF/REF, REF/ALT, and ALT/ALT carriers are (1-AF)2, 2 x AF(1-AF), and AF2, respectively.

The expected genotyping rate is independently estimated by the observed genotyping rate for each variant.

The expected frequency of the combination of genotypes is computed under the independent assumption among alleles

Local heritability analysis

To estimate the proportion of phenotypic variation explained by the rare protein-altering variants in ANGPTL7, we used Haseman-Elston (HE) regression using the cross product of the phenotypes for pairwise individuals implemented in genome-wide complex trait analysis (GCTA) version 1.92.4beta2 [48,49]. We computed the genetic relationship matrix (GRM) using the 4 rare protein-altering variants in ANGPTL7 and used it for the HE regression analysis [50].

Genetic correlation analysis

To estimate the genetic correlation, we used bivariate-HEreg using the cross product of the phenotypes for pairwise individuals implemented in GCTA version 1.92.4beta2. We computed GRM based on non-rare (MAF > 1%) variants on the genotyping array and used it for the bivariate-HEreg analysis.

PheWAS-analysis in UK Biobank

Using the summary statistics that are previously described and hosted on Global Biobank Engine (GBE) [11,38,51], we performed the phenome-wide association studies for 173 disease outcomes in the UK Biobank (https://github.com/rivas-lab/ANGPTL7/blob/master/notebook/ukbb_phewas/phenotypes_used_for_PheWAS.txt). Briefly, the summary statistics are generated by linear regression (for continuous traits) or logistic regression with the firth-fallback option (for binary outcomes) using the--glm subcommand implemented in PLINK v2.00a with age, sex, and the first 4 genotype PCs as covariates. We summarized the associations with P < 1x10-4 and SE < 0.5 (S10 Table). The full PheWAS association results are available on Global Biobank Engine [51].

https://gbe.stanford.edu/RIVAS_HG19/variant/1-11252357-A-G

https://gbe.stanford.edu/RIVAS_HG19/variant/1-11252369-G-A

https://gbe.stanford.edu/RIVAS_HG19/variant/1-11253684-G-T

https://gbe.stanford.edu/RIVAS_HG19/variant/1-11253688-C-T

PheWAS-analysis in FinnGen

In FinnGen, we performed a phenome-wide association analysis (PheWAS) of the identified variant comprising of 2,264 disease endpoints (S7 Fig, S2 and S3 Data).

Disease endpoint definition in FinnGen

The disease endpoints were defined using nationwide registries for deaths, hospital discharges, outpatient specialist appointments, cancer registry, and drug purchases registry, harmonizing over the International Classification of Diseases (ICD) revisions 8, 9, and 10, cancer-specific ICD-O-3, (NOMESCO) procedure codes, Finnish-specific Social Insurance Institute (KELA) drug reimbursement codes and ATC-codes. These registries spanning decades were electronically linked to the cohort baseline data using the unique national personal identification numbers assigned to all Finnish citizens and residents. A full list of FinnGen endpoints is available online for Freeze 4 (https://www.finngen.fi/en/researchers/clinical-endpoints). The endpoints with fewer than 100 cases, near-duplicate endpoints, and developmental “helper” endpoints were excluded from the final PheWas (column “OMIT”).

Outlier removal and PCA in FinnGen R4

FinnGen data was combined with 1000 genomes data and samples of non-Finnish ancestry (n = 2,880) and duplicates (n = 2035) were removed. King software [52] was used for relationship inference and approximately (> = 3rd degree related) independent set of 131,863 samples and 36,944 good quality (variant filters: remove chromosome X, imputation info< = 0.95, genotype imputed posterior probability<0.95, missingess>0.01) LD-pruned (r2<0.1) common (MAF > = 0.05) variants were used for computing PCA with Plink 1.9 [39]. The remaining 46,916 samples were then projected onto those PCs. Further 1,880 samples were removed due to missing phenotype data or mismatching sex and 176,899 samples were used in the analysis.

Association analysis

SAIGE mixed-model logistic regression was used for association analysis. Age, sex, 10 PCs and genotyping batch were used as covariates [53]. Each genotyping batch was included as a covariate for an endpoint if there were at least 10 cases and 10 controls in that batch to avoid convergence issues. Variants with minimum allele count < = 5 or imputation info < = 0.6 were excluded from the analysis. We report associations with P < 1x10-4 (S7 Fig, S1 and S2 Data).

Interaction analysis of ANGPTL7 and MYOC

To assess whether there is an interaction between ANGPTL7 and MYOC, we performed a logistic regression analysis using R glm() function with binomial response and logit link function with an interaction term, i.e. ANGPTL7 x MYOC. We found no evidence of interaction effect, P = 0.318.

Association analysis with reproductive fitness

Using the number of live births (UK Biobank Field ID: 2734, Global Biobank Engine phenotype ID: INI2734) and the number of children fathered (UK Biobank Field ID: 2405, Global Biobank Engine phenotype ID: INI2405), we performed association analysis for the four identified protein-altering variants using R script with age, types of genotyping array, and the first 4 genotype principal components as covariates. The analysis script is available at the GitHub repository (https://github.com/rivas-lab/ANGPTL7/).

Homozygote analysis

Homozygote in UK Biobank

For UK Biobank British individuals, we extracted the genotype calls with PLINK v2.00aLM (2 April 2019) and identified homozygous carrier of p.Gln175His allele [39]. We examined the year of birth (UK Biobank Field ID 34) and age at death (UK Biobank Field ID 40007) [10].

Homozygote in FinnGen

For Finnish FinnGen individuals, we extracted the genotypes using bcftools v1.9 and identified homozygote carriers of p.Arg220Cys [54]. To examine FinnGen disease endpoints among homozygote variant carriers, we compared the number disease endpoint cases in homozygote individuals to the number of cases of in the FinnGen samples using Fisher’s exact test (S1 Data).

Cascade plot analysis

Cascade plot of IOP association statistics in UK Biobank

Using the genome-wide association summary statistics for the median corneal compensated and Goldman-correlated IOP measurements in UK Biobank, we plotted the minor allele frequency and the BETA (SD) for the LD-pruned variants with P < 5x10-8. The LD pruning was performed using PLINK 1.9 with “--indep 50 5 2” as described before [11,38].

Effect size comparison of glaucoma associations with cascade plot

We collected the previously described association statistics from the following tables in literature.

Choquet, et al. 2018 [55], Table 2 and Table 3

Khawaja, et al. 2018 [6], Table 1

Shiga, et al. 2018 [56], Table 1 and Table 2

MacGregor, et al. 2018 [5], Supplementary Table S1

Hysi, et al. 2014 [8], Table 1

We plotted the minor allele frequency and odds ratio for variants with P < 5x10-8 for glaucoma.

List of FinnGen members.

FinnGen consists of the people listed in the Supplementary text.

(DOCX)

Click here for additional data file.

Phenotype distributions of intraocular pressure measurements.

Phenotype distributions of the corneal-compensated (A) and Goldman-correlated (B) IOP measurements (the median of left and right eyes) stratified by glaucoma disease status in unrelated White British in UK Biobank displayed as a Tukey’s box plot overlapping on a violin plot. In the box plot, the middle bold horizontal line represents the median, the lower and upper hinges show the first and third quartiles, the lower and upper whiskers represent 1.5 * interquartile range from the hinges. The data points beyond whiskers are plotted individually.

(TIF)

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The identification of unrelated White British individuals in UK Biobank.

The identification of unrelated White British individuals in UK Biobank. The first two genotype principal components (PCs) are shown on the x- and y-axis and the identified unrelated White British individuals (Methods) are shown in red.

(TIF)

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Genome-wide protein-altering variant association analysis of intraocular pressure phenotypes in UK Biobank.

Genome-wide protein-altering variant association analysis of corneal compensated (A) and Goldman-correlated (B) intraocular pressure in UK Biobank. The rare (0.01% < MAF < 1%) protein-altering variants with P < 0.01 are shown. The red dashed horizontal line represents the genome-wide significance threshold (P = 10−6). The variants are shown in red (odd autosomes) or blue (even autosomes). The genomic coordinates of the variants are shown on the x-axis and the statistical significance of univariate analysis is shown on the y-axis.

(TIF)

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The protein-altering variant GWAS QQ plot for intraocular pressure phenotypes.

The protein-altering variant GWAS QQ plot for corneal compensated (A) and Goldman-correlated (B) intraocular pressure. The variants outside of MHC region with 0.01% < MAF < 1% are included in the analysis.

(TIF)

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The intensity plots for ANGPTL7 protein-altering variants with 0.01% < MAF < 1%.

The intensity plots for ANGPTL7 protein-altering variants with 0.01% < MAF < 1%. (A) rs200058074 (p.Gln136Arg). (B) rs28991002 (p.Arg140His). (C) rs28991009 (p.Gln175His). (D) rs143435072 (p.Arg177Ter).

(TIF)

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The breakdown of the data sources used for the definition of glaucoma in UK Biobank.

The breakdown of the data sources used for the definition of glaucoma in UK Biobank. The combination of self-reported glaucoma (coded as "1277" in UKB Data coding ID 6) and ICD-10 codes from hospital inpatient data are used for the glaucoma definition in UK Biobank. The number of individuals in the white British individuals without IOP measurements are shown.

(TIF)

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Phenome-wide association analysis of p.Arg220Cys in FinnGen.

Phenome-wide association analysis of p.Arg220Cys in FinnGen. -log10(P-value) is displayed on the y-axis. Disease endpoints grouped by disease categories are displayed on the x-axis. Highlighted associations with P < 1x10-4 are shown.

(TIF)

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Protein expression in normal tissues and cell lines from ProteomicsDB and MOPED for ANGPTL7.

Protein expression in normal tissues and cell lines from ProteomicsDB and MOPED for ANGPTL7.

(TIF)

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The cascade plots for intraocular pressure and glaucoma.

The cascade plot for corneal compensated (A) and Goldman-correlated (B) intraocular pressure association analysis in UK Biobank. The cascade plot for glaucoma (C) from published genome-wide significant GWAS associations (gray) and the variants highlighted in our paper. The minor allele frequency and the BETA (SD) are plotted for the LD-pruned variants with P < 5x10-8. The odds ratios are included for LD pruned published variants with P < 5x10-8 for glaucoma.

(TIF)

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The homozygous carrier analysis results for ANGPTL7 p.Arg220Cys allele in FinnGen.

The homozygous carrier analysis results for ANGPTL7 p.Arg220Cys allele in FinnGen. The phenotype (Phenotype and Phenotype_description), the number of cases and controls and case frequency in homozygous carriers (HOM_case, HOM_cntrl, and HOM_case_%, respectively), the number of cases and controls and case frequency in all individuals (ALL_case, ALL_cntrl, and ALL_case_%, respectively), odds ratio (odds_ratio), and p-value from Fisher’s exact test (Fisher_p-value) are shown for disease endpoints.

(XLSX)

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The PheWAS results for ANGPTL7 p.Arg220Cys allele for eye-related phenotypes in FinnGen.

The PheWAS results for ANGPTL7 p.Arg220Cys allele for eye-related phenotypes in FinnGen. The phenotype (phenotype_code and phenotype_string), phenotype category (category), beta value (beta), odds ratio (odds_ratio), and p-value (pval) of the association are shown for each of the eye phenotypes.

(XLSX)

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The PheWAS results for ANGPTL7 p.Arg220Cys allele for non-eye related phenotypes in FinnGen.

The PheWAS results for ANGPTL7 p.Arg220Cys allele for non-eye related phenotypes in FinnGen. The phenotype (phenotype_code and phenotype_string), phenotype category (category), beta value (beta), odds ratio (odds_ratio), and p-value (pval) of the association are shown for each of the phenotypes that are not labeled as “Diseases of the eye and adnexa” in phenotype category.

(XLSX)

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List of UK Biobank phenotypes analyzed in the study.

List of UK Biobank phenotypes analyzed in the study. Phenotype name, the source field ID in UK Biobank (UKB Field ID), phenotype ID in Global Biobank Engine (GBE ID), the number of individuals (N) are shown.

(XLSX)

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Consistency of the genotype calls for four protein-altering variants in ANGPTL7 between genotyping array and exome sequencing data.

Consistency of the genotype calls for four protein-altering variants in ANGPTL7 between genotyping array and exome sequencing data. Variant including chromosome, position, reference, and alternate allele (hg19), the rs identifier of the genetic variant (rsID), amino acid nomenclature (HGVSp), genotype call from the array (Array) and exome data (Exome), and the number of individuals (N). Inconsistent variant calls are highlighted in bold font.

(XLSX)

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Number of individuals stratified by genotype of rare (0.01% < MAF < 1%) protein-altering variants in ANGPTL7.

Number of individuals stratified by genotype of rare (0.01% < MAF < 1%) protein-altering variants in ANGPTL7. The combination of genotypes is shown in the first four columns (rs200058074, rs28991002, rs28991009, and rs143435072) as well as the number of British individuals with the genotype combination in UK Biobank (n_observed). The expected number of individuals is computed under the Hardy-Weinberg equilibrium model and the independence assumption (n_expected, Method).

(XLSX)

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GCTA estimates of phenotypic variance explained by the 4 rare variants in ANGPTL7 for the IOP measures and glaucoma.

GCTA estimates of phenotypic variance explained by the 4 rare variants in ANGPTL7 for the IOP measures and glaucoma. The phenotype (Phenotype and GBE_ID), the estimated local heritability (V(G)/Vp), standard error (SE), and p-value (P) are shown. The SE and P are estimated based on from Jackknife method.

(XLSX)

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ANGPTL7 IOP protein-altering variant association for 6 IOP measurements in UK Biobank.

ANGPTL7 IOP protein-altering variant association for 6 IOP measurements in UK Biobank. The association statistics for 6 IOP traits (corneal compensated IOP [median INI2005254, right: INI5254, and left: INI5262] and Goldman-correlated IOP [median INI2005255, right: INI5255, and left: INI5263]) are shown. The phenotype (GBE_ID), variant (chromosome, position, reference, and alternate allele [hg19]), rsID, the HGVS amino acid nomenclature (HGVSp), and the estimated regression coefficient with 95% confidence intervals (BETA [95% CI]), and p-value of association (P) are shown.

(XLSX)

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Pairwise genetic correlation of IOP phenotypes. The genetic correlation (rg) for pairs of traits (Trait 1 and Trait 2, shown as GBE ID for 6 IOP traits (corneal compensated IOP [median INI2005254, right: INI5254, and left: INI5262] and Goldman-correlated IOP [median INI2005255, right: INI5255, and left: INI5263]) is shown with the standard error estimates (SE) based on Jackknife.

(XLSX)

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Glaucoma definitions in FinnGen.

Glaucoma definitions in FinnGen. ICD-codes are used in the Finnish hospital discharge and cause-of-death registries. ATC-codes are used in the Social Insurance Institution prescription drug purchase registry. All endpoint definitions in the FinnGen phenome-wide association analysis are available online (https://www.finngen.fi/fi/node/68).

(XLSX)

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FinnGen summary of MYOC p.Gln168Ter and ANGPTL7 p.Arg220Cys carriers in primary open glaucoma and all glaucoma cases.

FinnGen summary of MYOC p.Gln168Ter and ANGPTL7 p.Arg220Cys carriers in primary open glaucoma cases (A) and all glaucoma (B). The numbers of primary open-angle glaucoma (POAG) cases/controls stratified by genotype are shown. FinnGen summary of genotype counts for ANGPTL7 p.Arg220Cys in unrelated individuals in primary open glaucoma cases (C) and all glaucoma (D).

(XLSX)

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ANGPTL7 protein-altering variant association with reproductive fitness.

ANGPTL7 protein-altering variant association with reproductive fitness, (A) the number of live births and (B) the number of children fathered. Variant includes chromosome, position, reference, and alternate allele (hg19). rsID—the rs identifier of the genetic variant. HGVSp—the HGVS protein sequence name. MAF—the minor allele frequency in UK Biobank British population. Beta—estimated regression coefficient with 95% confidence intervals. P—p-value of association.

(XLSX)

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The PheWAS results for the four protein-altering variants in ANGPTL7 in UK Biobank.

The PheWAS results for the four protein-altering variants in ANGPTL7 in UK Biobank. The association summary statistics with P < 1.0 x 10−3 and SE < .5 are shown.

(XLSX)

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15 Oct 2019

Dear Dr Rivas,

Thank you very much for submitting your Research Article entitled 'Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma' to PLOS Genetics. Your manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the current manuscript. Based on the reviews, we will not be able to accept this version of the manuscript, but we would be willing to review again a much-revised version. We cannot, of course, promise publication at that time.

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While this manuscript has potential to be high-impact, there are major flaws cited by the reviewers which must be addressed.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Primary open-angle glaucoma (POAG), and its endophenotypes, including intraocular pressure (IOP), are strongly heritable, and numerous risk loci have been identified through genomwide association studies (GWAS). The role of rare genetic variation, on the other hand, has received far less attention. This manuscript addresses the role of rare variants in IOP using two large cohorts, the UK Biobank and FinnGen. A single-variant association analysis of the UK Biobank sample revealed several nonsynonymous coding variants in ANGPTL7 that, on average, lower IOP. An independent missense variant in ANGPTL7 the FinnGen sample also reduces IOP, suggesting a general protective role of ANGPTL7 inactivation in POAG.

This study is of high potential impact, considering the large public health burden of POAG in the elderly and the paucity of knowledge of the contribution of rare genetic variation. A strong point of this article is the large sample size, suitable for studies of rare variants, and meticulous quality control and documentation of the analysis on the UK Biobank cohort. The manuscript is written fairly well, with a few typos and minor grammatical errors.

However, the manuscript is marred by improperly conducted statistical analyses and lack of important information in the Methods and Discussion. Specifically, a meta-analysis is inappropriate for measuring the aggregate effect of multiple genetic variants. A simple burden test would be more appropriate, as well as an estimate of the proportion of phenotypic variation explained by the variants. If the authors feel that a meta-analysis is suitable here, they need to explain their rationale very carefully. Moreover, an analysis of genetic correlation produced an impossible correlation estimate of 1.08, suggesting that some other quantity besides correlation is being measured. These serious errors cast doubt on the reliability of the main findings, even though the primary association analysis seems sound. It is surprising that a genomewide gene-level association analysis of rare variants (e.g., burden test or SKAT), which would increase power to detect rare-variant aggregate effects, was not conducted. Methods for several analyses are missing, including the measurement of genetic correlation (Suppl. Fig 7) and the PheWAS (Suppl. Fig. 8). The PheWAS is only mentioned once in the main text, and only for citing the association result for Glaucoma; the significance of the other results from the PheWAS are not discussed.

The Discussion is perfunctory and is lacking important content. What are the limitations of the study? Specifically, how does the lack of overlap between POAG risk loci and IOP loci (e.g., Springelkamp et al., 2017, PMID 28073927) affect the significance of these results for treating POAG? What are the implications of the differences in allele frequency of IOP-lowering variants in the Finnish and UK populations? How do the new findings for ANGPTL7 fit in with what is already known about its role in POAG and IOP?

Specific comments:

1. Introduction, p. 2: Khawaja et al. (ref. 6) alone identified 68 risk loci for IOP. The total number of risk loci, including previous studies, including is larger. Choquet et al (2018) PMID 29235454 and Hysi et al. (2014) PMID 25173106 should also be cited, The term “unequivocally implicated” should be made clearer: does this mean replicated within one study? between two or more independent studies?

2. Results, p. 3: How was the joint association analysis for the three less significant ANGPTL7 SNPs performed? By a burden test?

3. Results p. 4: The lack of significance in the associations with glaucoma may also be explained by misclassification in the glaucoma phenotype on account of its being based on EHR, and by the likely presence of normal-tension POAG patients within the glaucoma cases.

4. Results, pp. 5-6. The last paragraph of the Results belongs in the Discussion, except for the sentence on tissue-specific expression of ANGPTL7.

5. Discussion: Does the Finnish population have a different prevalence of POAG than the UK?

6. Methods: Was the entire age range of the UK Biobank dataset included? The genetic determinants of IOP before age 40 may well be different than in older individuals. Was the average age of rare-variant carrying individuals much different from that of the entire sample?

7. Methods, p. 8 top: What is the “Array” predictor in the logistic regression model?

8. Suppl. Fig. S1: Considering the very large sample size, a density plot comparing IOP in cases and controls will provide more information than a boxplot. See Fig. 4 of Martin et al. (2017) PMID 28366442 for an example of overlapping density distributions.

9. Suppl. Fig. S7: This information would be much more concisely shown in a table, or even in the text. It is not clear what the correlations are between: the three x-axis labels each mention only one variable.

10. Suppl. Fig. S8: This PheWAS analysis isn’t mentioned anywhere in the text, except to indicate that the association of R220C with glaucoma was highly significant. Can it be omitted from the paper except for the glaucoma-related phenotypes?

11. Suppl. Table S1: The data would be more straightforward to interpret in the form of a small table for each variant with counts for each genotype pair observed (including NA), rather than one large table with the counts in a single column.

12. Suppl. Table S6: This appears to be raw, unformatted output, and should be formatted as a table.

Reviewer #2: This is an interesting study examining rare variant associations with glaucoma and its major endophenotype, IOP. There are several issue that need addressing.

Major comments:

- It is an odd approach to take Goldmann-correlated IOP of the right eye as a primary measure, and then not display results for the left eye measure or the corneal-compensated measures (only show genetic correlations). Are the authors hypothesizing that genetic associations with IOP may only influence one eye and not the author? If not, a better approach is to include both eyes and adjust for the correlation using a random-effects approach, or to simply take the mean of right and left eye measures.

- Why is the primary analysis for Goldmann-correlated IOP? Corneal compensated IOP has been shown to be more reflective of true physiological IOP, and less influenced by corneal artefact. Could ANGPTL7 variation actually be influencing the cornea rather than IOP?

- Were the IOP variables cleaned prior to analysis? If so, how?

- What does the meta-analyzed effect estimate mean when combining effects at multiple different variants (Supp Tables 6 and 9)? Is this the effect you would expect to see if someone had all these variants together? It seems odd to me that you would search for IOP-lowering variants in a gene, and then meta-analyze these selected variant effects together. Surely this is biased and misleading? Unless the authors can make a very strong rationale, I would remove sections on "combined significance".

- The definition of glaucoma in UK Biobank , a major outcome variable in the paper, is not clear. How many were identified using self-report? How many by hospital episode statistics? Why did the authors not limit to POAG HES codes? How were controls defined, given that the glaucoma question was not administered to the whole cohort? Given this is a key outcome variable, I would recommend the authors present a flow chart for derivation of glaucoma status as well as IOP.

- Is it possible that the protein alteration increases function of the gene? What evidence do the authors have that the functional consequence of the identified variants is reduced gene function? Unless strong, the authors should temper the strength of the language they use to describe the effect.

- The discussion is disappointingly short. How does this finding sit with other genetic discoveries for IOP and glaucoma? How does this fit in with what is known about IOP-related anatomy and physiology? What type of treatments might target the gene or its downstream effects, and how would the drug be delivered? Is there a plausible explanation for the hypothesis that the authors suggest regarding modifying the glaucoma risk of patients with MYOC mutations?

Minor comments:

- IOP is not the sole predictive factor for glaucoma

- The statement that there are "total of 68 independent loci have been unequivocally implicated in glaucoma" seems incorrect - the papers the authors cite do not reflect this on deeper reading.

- The text in the 2nd paragraph regarding "signals were consistently observed in left eye IOP measure" is not clear. Were previous analyses only carried on right eyes (if so, this is not clearly stated in the Results)? Are they referring to Goldmann-correlated IOP here? Results should be presented more robustly. Anyway, the authors may change their analytical approach based on the above.

Reviewer #3: This is a well-written paper describes several rare ANGPTL7 protein-coding variants that are associated with lower intraocular pressure (IOP) in participants from the UK Biobank and associated with decreased risk of glaucoma in the FinnGen dataset. Several points to address:

1) The overall beta for intraocular pressure reduction by heterozygous variants is very small and even the homozygous Gln175His would not be expected be within the resolution of clinical measurement or to be clinically relevant. This should be discussed especially in regard to therapeutic development.

2) The authors note that an ANGPTL7 rare variant is likely responsible for the 1p36 signal reported in Khawaja et al. It would be interesting to note if this signal has been observed in other IOP GWAS such as Choquet et al., 2018.

3) While overall the examination of the ANGPTL7 effects on MYOC368ter cases is interesting there are several questions about this result. First, since the FinnGen glaucoma cases are not actually examined, but defined by ICD codes, its possible that some of the MYOC 368ter 'noncases' are actually cases- this is particularly relevant when considering a recent study that has shown that some patients with MYOC 368ter can have glaucoma without intraocular pressure elevation (Fingert et al., JAMA Ophthalmology). Second, was the distribution of ANGPTL7 variant carriers among MYOC 368ter carriers statistically significant?

4) A limitation of the study is that all the glaucoma cases are defined by ICD codes without any clinical validation. These codes used to define case-control status also include 'glaucoma secondary to eye trauma', 'secondary to eye infection or other eye disorders' and 'secondary to drugs'. Eye traumas are not genetic, while drugs causing glaucoma are primarily corticosteroids, which could drive these results considering the potential role of ANGPTL7 in steroid-responsive glaucoma (see point 6 below). Moreover, including all types of glaucoma is concerning as various forms of glaucoma have very different mechanisms and some can be difficult to distinguish without expert evaluation. Given the very high prevalence of exfoliation glaucoma in Finland this would be of special concern in the FinnGen population. Further replication of these findings in a cohort of individuals diagnosed by clinical experts would be helpful.

5) As ANGPTL7 has been shown to be increased in glaucoma secondary to steroid (glucocorticoid) exposure and this type of glaucoma has a specific ICD code is it possible to examine this subgroup among the UK Biobank cases? Even better in patients who have been clinically diagnosed to have this type of glaucoma by glaucoma experts? Is it possible that this subgroup is driving the UKBiobank results? Showing that these variants are protective in POAG patients examined by a clinical expert with knowledge of the history of steroid exposure in the patient would also be helpful.

6) There is very little discussion of any potential protective role for the ANGPTL7 protein or functionally how loss of function variants could impact intraocular pressure and glaucoma. Additionally, while the nonsense variant is likely to be loss of function, this may not actually be the case as the most common MYOC variant (368ter) is actually a gain of function. Its not clear if the missense alleles are loss or gain of function. Again, similar to MYOC the missense alleles are gain of function. This information is very relevant to the development of ANGPTL7 based therapies.

7) This sentence is confusing, “Given these findings, we next asked whether any of these putative IOP-lowering genetic variants showed effects consistent with reducing glaucoma risk in an independent set of unrelated British individuals that do not have IOP measures (4,269 cases and 251,355 controls).” Are these glaucoma cases not included in the set of UK Biobank individuals with eye phenotype data?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

19 Dec 2019

Submitted filename: ANGPTL7_Glaucoma_review_response_20191218.pdf

Click here for additional data file.

18 Feb 2020

Dear Dr Rivas,

We are pleased to inform you that your manuscript entitled "Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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17 Apr 2020

PGENETICS-D-19-01516R1

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma

Dear Dr Rivas,

We are pleased to inform you that your manuscript entitled "Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.

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