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Heritability, SNP- and Gene-Based Analyses of Cannabis Use Initiation and Age at Onset
research-article
Author(s):
Camelia C. Minică
,
Conor V. Dolan ,
Jouke-Jan Hottenga ,
René Pool ,
The Genome of the Netherlands Consortium,
Iryna O. Fedko ,
Hamdi Mbarek ,
Charlotte Huppertz ,
Meike Bartels ,
Dorret I. Boomsma ,
Jacqueline M. Vink
Publication date (Electronic):
19 May 2015
Journal:
Behavior Genetics
Publisher:
Springer US
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Abstract
Prior searches for genetic variants (GVs) implicated in initiation of cannabis use have been limited to common single nucleotide polymorphisms (SNPs) typed in HapMap samples. Denser SNPs are now available with the completion of the 1000 Genomes and the Genome of the Netherlands projects. More densely distributed SNPs are expected to track the causal variants better. Therefore we extend the search for variants implicated in early stages of cannabis use to previously untagged common and low-frequency variants. We run heritability, SNP and gene-based analyses of initiation and age at onset. This is the first genome-wide study of age at onset to date. Using GCTA and a sample of distantly related individuals from the Netherlands Twin Register, we estimated that the currently measured (and tagged) SNPs collectively explain 25 % of the variance in initiation (SE = 0.088; P = 0.0016). Chromosomes 4 and 18, previously linked with cannabis use and other addiction phenotypes, account for the largest amount of variance in initiation (6.8 %, SE = 0.025, P = 0.002 and 3.6 %, SE = 0.01, P = 0.012, respectively). No individual SNP- or gene-based test reached genomewide significance in the initiation or age at onset analyses. Our study detected association signal in the currently measured SNPs. A comparison with prior SNP-heritability estimates suggests that at least part of the signal is likely coming from previously untyped common and low frequency variants. Our results do not rule out the contribution of rare variants of larger effect—a plausible source of the difference between the twin-based heritability estimate and that from GCTA. The causal variants are likely of very small effect (i.e., <1 % explained variance) and are uniformly distributed over the genome in proportion to chromosomes’ length. Similar to other complex traits and diseases, detecting such small effects is to be expected in sufficiently large samples.
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Most cited references33
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Genome-wide association studies establish that human intelligence is highly heritable and polygenic
Gail Davies, Albert Tenesa, Antony Payton … (2011)
General intelligence is an important human quantitative trait that accounts for much of the variation in diverse cognitive abilities. Individual differences in intelligence are strongly associated with many important life outcomes, including educational and occupational attainments, income, health and lifespan 1,2 . Data from twin and family studies are consistent with a high heritability of intelligence 3 , but this inference has been controversial. We conducted a genome-wide analysis of 3511 unrelated adults with data on 549 692 SNPs and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits. We partitioned genetic variation on individual chromosomes and found that, on average, longer chromosomes explain more variation. Finally, using just SNP data we predicted approximately 1% of the variance of crystallized and fluid cognitive phenotypes in an independent sample (P = 0.009 and 0.028, respectively). Our results unequivocally confirm that a substantial proportion of individual differences in human intelligence is due to genetic variation, and are consistent with many genes of small effects underlying the additive genetic influences on intelligence.
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What has research over the past two decades revealed about the adverse health effects of recreational cannabis use?
Wayne Hall (2015)
To examine changes in the evidence on the adverse health effects of cannabis since 1993.
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Genome-Wide Analysis Points to Roles for Extracellular Matrix Remodeling, the Visual Cycle, and Neuronal Development in Myopia
Amy Kiefer, Joyce Tung, Chuong B. Do … (2013)
Introduction Myopia, or nearsightedness, is the most common eye disorder worldwide. In the United States, an estimated 30–40% of the adult population has clinically relevant myopia (more severe than −1 diopter), and the prevalence has increased markedly in the last 30 years [1], [2]. Myopia is a refractive error that results primarily from increased axial length of the eye [3]. The increased physical length of the eye relative to optical length causes images to be focused in front of the retina, resulting in blurred distance vision. The etiology of myopia is multifactorial [3]. Briefly, postnatal eye growth is directed by visual stimuli that evoke a signaling cascade within the eye. This cascade is initiated in the retina and passes through the retinal pigment epithelium (RPE) and choroid to guide remodeling of the sclera (the white outer wall of the globe) (cf. [4], [5]). Animal models implicate these visually-guided alterations of the scleral extracellular matrix in the eventual development of myopia [4], [6]. The human eye grows from an average of 17 mm at birth to 21–22 mm in adulthood [7]. By ages 5–6 only about 2% of children are myopic [7]. Although the eye grows only 0.5 mm through puberty [8], the incidence of myopia increases sevenfold during this time [7], peaking between the ages 9–14 [9]. Myopia developed during childhood or early adolescence generally worsens throughout adolescence and then stabilizes by age 20. Compared to myopia that develops in childhood or adolescence, adult onset myopia tends to be less severe [10]–[12]. The majority of myopia cases are primary and nonsyndromic [3]; however, myopia can arise as a complication of other conditions, such as severe prematurity, cataracts, and keratoconus [13], [14], and is sometimes associated with certain connective tissue disorders, such as Stickler syndrome [15]. Although epidemiological studies have implicated numerous environmental factors in the development of myopia, most notably education, outdoor exposure, reading, and near work [3], it is well established that genetics plays a substantial role. Twin and sibling studies have provided heritability estimates that range from 50% to over 90% [16]–[20]. Children of myopic parents tend to have longer eyes and are at higher than average risk of developing myopia in childhood [21]. Segregation analyses suggest that multiple genes are involved in the development of myopia [22], [23]. To date, there have been seven genome-wide association studies (GWAS) on myopia or related phenotypes (pathological myopia, refractive error, and ocular axial length): two in Europeans [24], [25] and five in Asian populations [26]–[30]. Each of these publications has identified a different single association with myopia. In addition there have been several linkage studies (see [3], [31] for reviews) and an exome sequencing study of severe myopia [32]. In contrast to the previous GWAS that used degree of refractive error as a quantitative dependent measure, we analyzed data for 45,771 individuals from the 23andMe database who reported whether they had been diagnosed with nearsightedness, and if so, at what age. We performed a genome-wide survival analysis on age of onset of myopia, discovering 22 genome-wide significant associations with myopia age of onset, 20 of which are novel. Ten of the novel and one of the previously identified associations replicate in a separate (smaller and more coarsely phenotyped) cohort of 8,323 individuals. Results/Discussion Participants reported via web-based questionnaires whether they had been diagnosed with nearsightedness, and if so, at what age. Only those participants who reported onset between five and 30 years of age were included to limit cases of secondary myopia (e.g., myopia due to premature birth or cataracts). Further filtering was performed to limit errors in reporting (see Methods). All participants were customers of 23andMe and of primarily European ancestry; no pair was related at the level of first cousins or closer. We performed a genome-wide survival analysis using a Cox proportional hazards model on data for 45,771 individuals (“discovery set”). The Cox model assumes that there is an (unknown) baseline probability of developing myopia at every year of age. The model then tests whether each single nucleotide polymorphism (SNP) is associated with a significantly higher or lower probability of developing myopia compared to baseline. The Cox model can be thought of as a generalization of an analysis of myopia age of onset. In contrast to an analysis of age of onset, the Cox model allows for the inclusion of non-myopic controls, resulting in increased statistical power. Analyses controlled for sex and five principal components of genetic ancestry. An additional, non-overlapping set of 8,323 participants who reported on their use of corrective eyewear for nearsightedness before the age of ten were used as a replication set. See Table 1 for characteristics of the two cohorts. 10.1371/journal.pgen.1003299.t001 Table 1 Cohort statistics. Number % female Age (SE) Age of onset (SE) Discovery, myopic 25,999 45.9 47.7 (15.5) 13.6 (5.8) Discovery, not myopic 19,772 40.3 49.6 (17.1) — Replication, myopic at 10 1,488 45.3 46.7 (14.9) Replication, not myopic at 10 6,835 45.2 53.7 (15.1) — Sex, current age, and age of onset for discovery and replication cohorts. Table 2 shows the top SNPs for all 35 genetic regions associated with myopia with a -value smaller than . All -values from the GWAS have been corrected for the inflation factor of GC = 1.167. A total of 22 of the SNPs cross our threshold for genome-wide significance ( , see Figure S1). These 22 include two SNPs previously associated with refractive error in GWAS of European populations: rs524952 near GJD2 and ACTC1 and rs28412916 near RASGRF1 [24], [25], [33]. -values genome-wide are shown in Figure 1; Figure S2 shows the quantile-quantile plot for the analysis. Table S3 shows all SNPs with -values under . 10.1371/journal.pgen.1003299.g001 Figure 1 Negative -values genome wide for myopia. Regions are named with their postulated candidate gene or genes. -values under have been cut off (only the LAMA2 and LRRC4C regions are affected). See Figure S1 for plots in each region with a significant association. 10.1371/journal.pgen.1003299.t002 Table 2 Index SNPs for regions with . rsid chr Position Genes MAF allele HR (CI) -value rs12193446 6 129820038 LAMA2 0.094 0.991 A/G 0.788 (0.763–0.813) rs1381566 11 40149607 LRRC4C 0.181 0.873 T/G 1.149 (1.122–1.176) 0.0038 rs17648524 16 7459683 RBFOX1 0.365 0.974 G/C 1.102 (1.082–1.122) 0.36 rs7744813 6 73643289 KCNQ5 0.405 0.958 A/C 0.909 (0.893–0.926) 0.15 rs3138142 12 56115585 RDH5 0.218 0.831 C/T 0.890 (0.870–0.911) 0.011 chr8:60178580 8 60178580 TOX/CA8 0.358 0.971 C/G 0.914 (0.897–0.931) 0.0061 rs524952 15 35005886 GOLGA8B/GJD2 0.469 0.982 T/A 1.089 (1.070–1.108) 0.040 rs2137277 8 40734662 SFRP1 0.189 0.922 A/G 0.901 (0.880–0.923) 0.46 rs1550094 2 233385396 PRSS56 0.305 0.965 A/G 1.087 (1.067–1.107) 0.031 rs2908972 17 11407259 SHISA6 0.397 0.969 T/A 1.074 (1.055–1.093) 0.034 rs17412774 2 146773948 PABPCP2 0.450 0.976 A/C 0.933 (0.917–0.950) 0.067 rs11145746 9 71834380 TJP2 0.198 0.887 G/A 1.087 (1.063–1.112) 0.87 rs28412916 15 79378167 RASGRF1 0.401 0.989 A/C 1.067 (1.048–1.086) 0.08 rs5022942 4 81959966 BMP3 0.229 0.991 G/A 1.076 (1.054–1.098) 0.0093 rs745480 10 85986554 RGR 0.473 0.975 C/G 1.063 (1.044–1.081) 0.095 rs2155413 11 84634790 DLG2 0.466 0.997 C/A 1.061 (1.043–1.080) 0.023 rs13091182 3 141133960 ZBTB38 0.333 0.994 G/A 0.940 (0.923–0.958) 0.31 rs17400325 2 178565913 PDE11A 0.050 0.933 T/C 1.144 (1.099–1.190) 0.027 rs17428076 2 172851936 DLX1 0.237 0.985 C/G 0.935 (0.916–0.955) 0.53 rs6480859 10 79081948 KCNMA1 0.363 0.987 C/T 1.058 (1.039–1.077) 0.40 chr14:54413001 14 54413001 BMP4 0.489 0.933 G/C 0.946 (0.929–0.963) 0.21 rs4291789 13 100672921 ZIC2 0.326 0.724 C/G 1.069 (1.046–1.092) rs10963578 9 18338649 SH3GL2/ADAMTSL1 0.200 0.958 G/A 0.936 (0.915–0.957) 0.15 rs11939401 4 80818417 ANTXR2 0.203 0.999 C/T 0.939 (0.919–0.959) 0.13 rs1843303 3 4185124 SETMAR 0.303 0.981 T/C 1.055 (1.036–1.075) 0.042 chr11:65348347 11 65348347 EHBP1L1 0.017 0.558 G/A 0.770 (0.700–0.846) 0.54 rs4367880 10 114795256 TCF7L2 0.199 0.959 G/C 1.063 (1.040–1.087) 0.57 rs61988414 14 42313443 LRFN5 0.168 0.878 A/G 1.071 (1.045–1.097) 0.84 rs9365619 6 164251746 QKI 0.457 0.999 C/A 1.048 (1.031–1.067) 0.047 rs4245599 10 60365755 BICC1 0.466 0.952 G/A 1.049 (1.031–1.068) 0.44 rs10512441 17 31239645 MYO1D/TMEM98 0.203 0.919 C/T 1.062 (1.039–1.085) 0.39 rs9902755 17 47220726 B4GALNT2 0.470 0.668 C/T 1.059 (1.037–1.081) 0.91 rs6702767 1 200844547 GPR25 0.485 0.982 G/A 1.048 (1.030–1.066) 0.40 chr17:79585492 17 79585492 NPLOC4 0.393 0.604 G/A 1.063 (1.039–1.087) 0.032 rs6487748 12 9435768 PZP 0.491 0.981 A/G 1.048 (1.030–1.066) 0.42 Index SNPs for regions with ( -corrected) -values under . Positions and alleles are given relative to the positive strand of build 37 of the human genome; alleles are listed as major/minor. The listed genes are the postulated candidate gene in each region. is the estimated imputation accuracy; HR is the hazard ratio per copy of the minor allele; -value is the -value in the discovery cohort; is the -value in the replication cohort. Significant replication -values are bolded. Of the 22 SNPs significant in the discovery set, 11 were also significant in the replication set (Table 2). Of the 11 SNPs that did not replicate, only two showed different signs between the discovery and replication sets ( ). Given these results, and considering that the replication set was much smaller than the discovery set and measured age of onset less exactly, we suspect that much of the lack of replication is due to lack of power. We defined a genetic myopia propensity score as the number of copies of the risk alleles across all 22 SNPs identified via the discovery set. The propensity score showed a strong association with early onset myopia (less than 10 years old) in our replication cohort ( , odds ratio 1.075 per risk allele). The top decile of genetic propensity had 1.97 greater odds of developing myopia before the age of 10 than the bottom decile. In a Cox model fit to the discovery set, the propensity score explains 2.9% of the total variance. Note that this estimate may be inflated, as it is calculated on the discovery population. In this model, someone in the 90th percentile of risk (a score of 21.95) is nearly twice as likely to develop myopia by the age of 25 as someone in the 10th percentile of risk (score of 15.01), Figure 2. 10.1371/journal.pgen.1003299.g002 Figure 2 Estimated survival curves by genetic propensity score. The genetic propensity score is computed as the number of risk alleles across the 22 genome-wide significant SNPs. Curves show estimated survival probability (i.e., the probability of not having developed myopia) by age under the fitted Cox model for the 10th, 50th, and 90th percentiles of scores (15.01, 18.46, and 21.95, respectively). Of the 20 novel associations, many lie in or near genes with direct links to processes related to myopia development. Two of them lie in regions associated with myopia in linkage studies: rs1550094 in PRSS56 (MIM: 609995) [34] and chr14:54413001 near BMP4 (MIM: 255500) [35]. Two suggestive associations also are in such regions: rs4245599 in BICC1 (MIM: 612717) [36] and rs9902755 in B4GALNT2 (MIM: 608474) [37]. Below, we briefly sketch out possible connections between these associations and extracellular matrix (ECM) remodeling, the visual cycle, eye and body growth, retinal neuron development, and general neuronal development or signaling. Extracellular Matrix Remodeling The strongest association is a SNP in an intron of LAMA2 (laminin, alpha 2 subunit, rs12193446, , hazard ratio (HR) = 0.79). Laminins are extracellular structural proteins that are integral parts of the ECM. Changes in the composition of the ECM of the sclera have been shown to alter the axial length of the eye [5]. Laminins play a role in the development and maintenance of different eye structures [38], [39]. The laminin alpha 2 chain in particular is found in the extraocular muscles during development [38], and may act as an adhesive substrate and possibly a guidance cue for retinal ganglion cell growth cones [40]. We also found a suggestive association related to laminin (rs11939401, , HR = 0.939) approximately 17 kb upstream of ANTXR2 (anthrax toxin receptor 2). ANTXR2 binds laminin and possibly collagen type IV [41] and thus may also be involved in extracellular matrix remodeling. The Visual Cycle Two of the novel associations are in or near genes involved in the regeneration of 11-cis-retinal, the light sensitive component of photoreceptors, a process commonly referred to as the visual cycle of the retina. These associations are with rs3138142, , HR = 0.89, in RDH5 (retinol dehydrogenase 5 (11-cis/9-cis)) and rs745480 ( , HR = 1.06), a SNP 18 kb upstream of RGR, which encodes the retinal G protein-coupled receptor. The SNP rs3138142 is a synonymous change in RDH5. It has been linked to RDH5 expression [42], [43], and it is part of an Nr2f2 (nuclear receptor subfamily 2, group F, member 2) transcription factor binding motif in mouse [44], [45]. Both RDH5 and RGR play crucial roles in the regeneration of 11-cis retinal in the RPE [46]. Mutations in RDH5 have been linked with fundus albipunctatus, a rare form of congenital stationary night blindness (for a recent review, see [47]) and progressive cone dystrophy [48], and mutations in RGR have been linked with autosomal recessive and autosomal dominant retinitis pigmentosa [49], [50]. We also identified an association within another gene that functions in the RPE: rs7744813 ( , HR = 0.91), a SNP in KCNQ5 (potassium voltage-gated channel, KQT-like subfamily, member 5). KCNQ5 encodes a potassium channel found in the RPE and neural retina. These channels are believed to contribute to ion flow across the RPE [51], [52] and to affect the function of cone and rod photoreceptors [52]. Eye and Body Growth Five of our associations show possible links to eye or overall morphology. The first is a missense mutation in PRSS56 (A224T, rs1550094, , HR = 1.09). Other mutations in PRSS56 have been shown to cause strikingly small eyes with severe decreases in axial length [53]–[55]. Two other associated SNPs are near genes that encode bone morphogenetic proteins: chr14:54413001 ( , HR = 0.95) near BMP4 (bone morphogenic protein-4), and rs5022942 ( , HR = 1.08) in BMP3 (bone morphogenic protein-3). Inherited BMP4 mutations have been associated with syndromic microphthalmia and various eye, brain and digital malformations [56], [57]. Although BMP3 is primarily known for its role in bone development (e.g., it is linked to skeletal defects in humans and skull shapes in dogs [58], [59]), it was found to be uniquely expressed in keratocytes, specialized mesenchymal cells that are important for development of the cornea by producing and maintaining the extracellular matrix of the corneal stroma [60]. One associated SNP, rs13091182 ( , HR = 0.94), in ZBTB38 (zinc finger and BTB domain-containing protein 38), is in linkage disequilibrium (LD) with a SNP previously associated with height (rs6763931; ) [61]. The final SNP with a link to morphology is rs17428076 ( , HR = 0.94), near DLX1 (homo sapiens distal-less homeobox 1). Disruption of DLX1 has been shown to result in poor optic cup regeneration in planarians and small eyes in mice [62], [63]. Retinal Ganglion Cell Projections Two of the novel associations are near genes that affect the outgrowth of retinal ganglion neurons during development. The first is rs4291789 ( , HR = 1.07), which lies 34 kb downstream of ZIC2 (Zic family member 2). ZIC2 regulates two independent parts of ipsilateral retinal ganglion cell development: axon repulsion at the optic chiasm midline [64], [65], and organization of the axonal projections at their final targets in the brain [66]. The second, rs2137277 ( , HR = 0.90), is a variant in ZMAT4 (zinc finger, matrin-type 4). ZMAT4 has no known link to vision, but this variant also lies 385 kb downstream of SFRP1 (secreted frizzled-related protein 1). SFRP1 is involved in the differentiation of the optic cup from the neural retina [67], retinal neurogenesis [68], the development and function of photoreceptor cells [69], [70], and the growth of retinal ganglion cells [71]. Neuronal Signaling and Development Finally, we found five associations with SNPs in genes involved in neuronal development and signaling, but without a known role in vision development or the vision cycle: in KCNMA1 (potassium large conductance calcium-activated channel, subfamily M, alpha member 1; rs6480859, , HR = 1.06); in RBFOX1 (RNA binding protein, fox-1 homolog; rs17648524, , HR = 1.10); in LRRC4C, leucine rich repeating region containing 4C, also known as NGL-1 (rs1381566, , HR = 1.15); in DLG2 (discs, large homolog 2; rs2155413, , HR = 1.06); and in TJP2 (tight junction protein 2; rs11145746, , HR = 1.09). KCNMA1 encodes the pore-forming alpha subunit of a MaxiK channel, a family of large conductance, voltage and calcium-sensitive potassium channels involved in the control of smooth muscle and neuronal excitation. RBFOX1 belongs to a family of RNA binding proteins that regulates the alternative splicing of several neuronal transcripts implicated in neuronal development and maturation [72]. LRRC4C encodes a binding partner for netrin G1 and promotes the outgrowth of thalamocortical axons [73]. DLG2 plays a critical role in the formation and regulation of protein scaffolding at postsynaptic sites [74]. TJP2 has been linked with hearing loss: its duplication and subsequent overexpression are found in adult-onset progressive nonsyndromic hearing loss [75]. Conclusion This study represents the largest GWAS on myopia in Europeans to date. This cohort of 45,771 individuals led to the discovery of 20 novel associations as well as replication of the two previously reported associations in Europeans. Ten of these novel associations replicate in our much smaller replication set of 8,323 individuals. In contrast to the earlier studies that used refractive error as a quantitative outcome, we used a Cox proportional hazards model with age of onset of myopia as our major endpoint. This model yielded greater statistical power than a simple case-control study of myopia. Of the 22 significant SNPs found using this model, all but two had smaller -values when a hazards model was employed, and only 20 would be genome-wide significant using a case-control analysis on the same dataset (Table S1). The proportional hazards model assumes that the effect of each SNP on myopia risk does not vary by age. When we tested the validity of this assumption for the 22 significant SNPs, only the one in LAMA2 (rs12193446) showed evidence of different effects at different ages (Table S2). While this violation should not lead to overly small -values for this SNP in the GWAS, it does make risk prediction based on these results less straightforward. For example, rs12193446 shows a large effect on myopia hazard at an early age, peaking around 11 years, and then a null or even negative effect on hazard at older ages (Figure S3). This age dependent hazard suggests that different biological processes may affect the development of myopia at different ages. Our findings further suggest that there may be somewhat different genetic factors underlying myopia age of onset and refractive error. Because adult onset myopia tends to be less severe than myopia developed in childhood or adolescence [10]–[12], age of onset is likely correlated with refractive error, but it is not known how strongly. Many of the associations with myopia age of onset that we found are stronger than the two previously detected associations with refractive error (near GJD2 and near RASGRF1). Notably, the latter association, near RASGRF1, also failed to replicate in a recent meta-analysis [33]. The fact that many of our associations with strong effects on age of onset have not shown up in previous refractive error GWAS implies that some genetic factors may affect the age of onset independent of eventual severity, and that the strength of different genetic associations with myopia may depend on the specific phenotype under study. We also note that our phenotype was based on participants' reports rather than clinical assessments. Although in theory errors in recall could have affected our results, we expect that the vast majority of people are able to recall when they first wore glasses with at most a few years of error. In fact, a subset of those eligible to be part of our discovery cohort provided age of myopia diagnosis in two independent places (see Methods for details). Out of 1,463 people who reported age of diagnosis in both surveys and met our inclusion criteria (European ancestry, age at diagnosis between five and 30 and less than current age), 96.0% reported ages that differed by at most three years and 97.8% by at most five years. The five associations previously reported in pathological myopia or refractive error GWAS in Asian populations [26]–[30] show no overlap with the significant or suggestive regions found here. Nor did we find an association with the ZNF644 locus that was identified as the site of high-penetrance, autosomal dominant mutations in Han Chinese families with apparent monogenic inheritance of high-grade myopia [32]. This lack of overlap could result from different genetic factors being involved in myopia across populations. It has been suggested that pathological myopia, which represents less than 2% of cases in the United States [1], has different underlying genetic factors than non-pathological myopia [31]. Our identification of 20 novel genetic associations suggests several novel genetic pathways in the development of human myopia. These findings augment existing research on the development of myopia, which to date has been studied primarily in animal models of artificially induced myopia. Some of the associations are consistent with the current view, based largely on animal models, that a visually-triggered signaling cascade from the retina ultimately guides the scleral remodeling that leads to eye growth, and that the RPE plays a key role in this process [4]. A number of the novel associations point to the potential importance of early neuronal development in the eventual development of myopia, particularly the growth and topographical organization of retinal ganglion cells. These associations suggest that early neuronal development may also contribute to future refractive errors. We expect that these findings will drive new research into the complex etiology of myopia. Methods Human Subjects All participants were drawn from the customer base of 23andMe, Inc., a consumer genetics company. This cohort has been described in detail previously [76], [77]. Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services (E&I Review). Phenotype Data Participants in the discovery cohort were asked online as part of a medical history questionnaire or an eyesight questionnaire: “Have you ever been diagnosed by a doctor with any of the following vision conditions?: Nearsightedness (near objects are clear, far objects are blurry) (Yes/No/I don't know)”. If they answered “yes”, they were asked, “At what age were you first diagnosed with nearsightedness (near objects are clear, far objects are blurry)? Your best guess is fine.” Those reporting an age of onset either greater than their current age or outside of the range 5–30 were removed from analysis. All participants also reported current age. A total of 4,758 participants reporting age of onset outside of 5–30 and 87 reporting age of onset in the future were removed. To limit errors in reporting, we excluded from the discovery cohort those who provided discordant answers in the medical history and eyesight questionnaires. We defined discordance as a disagreement in diagnosis or a difference in more than 5 years in age of onset. A total of 92 people with discordant age of onset and 276 with discordant diagnosis were removed. Many of these people would have been filtered out by our other restrictions: only 32 of the 92 with discordant ages of onset would not have been removed for other reasons (mostly because their stated age of onset was not between 5–30), and only 139 of the 276 with discordant diagnoses. These 32 and 139 are out of 1,463 and 2,845 eligible people, respectively, leading us to estimates of 97.8% and 95.1% concordance in age of onset and myopia diagnosis (after the filters mentioned above were applied). The replication cohort consisted of 8,323 23andMe customers who were not part of the discovery cohort and were not closely related (at 700 cM or greater IBD) to each other or to anyone in the discovery cohort. They provided information on myopia age of onset in one of two ways. 5,265 answered a single question: “Did you wear glasses or other corrective eyewear for nearsightedness before the age of 10? (Yes/No/I'm not sure).” The other 3,058 provided age of onset in the same manner as the discovery cohort. Note that these 3,058 were people that would have been eligible for the discovery cohort, however, they provided data in the time in between our analysis of the discovery and replication cohorts. Their data was converted to the same binary scale as the first group. Genotyping and Imputation Participants were genotyped and additional SNP genotypes were imputed against the August 2010 release of the 1000 genomes data as described previously [78]. Briefly, they were genotyped on at least one of three genotyping platforms, two based on the Illumina HumanHap550+ BeadChip, the third based on the Illumina Human OmniExpress+ BeadChip. The platforms included assays for 586,916, 584,942, and 1,008,948 SNPs respectively. Genotypes for a total of 11,914,767 SNPs were imputed in batches of roughly 10,000 individuals, grouped by genotyping platform. Of these, 7,087,609 met our criteria of 0.005 minor allele frequency, average across batches of at least 0.5, and minimum across batches of at least 0.3. (The minimum requirement was added to filter out SNPs that imputed poorly in the batches consisting of the less dense platform.) Statistical Analysis In order to minimize population substructure while maximizing statistical power, the study was limited to individuals with European ancestry. Ancestry was inferred from the genome-wide genotype data, and principal component analysis was performed as in [76], [79]. The combined discovery and replication populations were filtered by relatedness to remove participants at a first cousin or closer relationship. More precisely, no two participants shared more than 700 cM of DNA identical by descent (IBD, approximately the lower end of sharing between a pair of first cousins). IBD was calculated using the methods described in [80]. For the survival analysis, let the hazard function be the rate of developing myopia at time . Then the Cox proportional hazards model is for an arbitrary baseline hazard function and covariates (genotype), (sex), (age), and (projections onto principal components). was coded as a dosage from 0–2 as the estimated number of minor alleles present. For each SNP, we fit a Cox proportional hazards model using R [81] and computed a p-value using a likelihood ratio test for the genotype term. All SNPs with -values under after genomic control correction were considered genome-wide significant. The hazard ratio (HR) reported throughout can be interpreted as the multiplicative change in the rate of onset of myopia per copy of the minor allele (e.g., ). To test the proportional hazards assumption, we tested for independence of the scaled Schoenfeld residuals for each significant SNP and time using cox.zph (Table S2). Replication -values in Table 2 are one-sided -values from a likelihood ratio test for a logistic regression model controlling for age, sex, and five principal components. For Figure 2, we computed a myopia propensity score for each individual as the (estimated) number of risk alleles among the 22 genome-wide significant SNPs. We then fit a Cox model including that score, sex, and five principal components. To estimate proportion variance explained for this model, we used a pseudo- using likelihoods (similar to the Nagelkerke pseudo for logistic regression). That is, we calculated the variance explained as where is the null likelihood and the likelihood for the full model. This is one of several methods used to compute variance explained for Cox proportional hazards models [82]. Supporting Information Figure S1 Region plots for genome-wide significant associations Colors depict the squared correlation ( ) of each SNP with the most associated SNP (shown in purple). Gray indicates SNPs for which information was missing. (PDF) Click here for additional data file. Figure S2 Quantile-quantile plot for myopia survival analysis Actual ( -corrected) -values versus the null. (PDF) Click here for additional data file. Figure S3 Smoothed log-hazard ratios as a function of age for four SNPs In each plot, the straight line shows the estimated log-hazard ratio (beta) for each SNP in the proportional hazards model. The solid curve is a spline fit to beta estimated at different ages; the dotted curves are approximate 95% confidence intervals. The -value in each caption is the result of a test of the proportional hazards assumption. The sign of all coefficients has been made positive for ease of comparison (so (a), (c), and (d) are flipped relative to the main text). Note that among the examples here, only (a) shows evidence of deviation from the proportional hazards assumption after correction for 22 tests. (PDF) Click here for additional data file. Table S1 -values for survival and case-control analyses. -values for SNPs in the survival analysis used in the paper as well as in a case-control logistic regression on the same set of individuals. The survival analysis gives a smaller -value for 30 of 35 SNPs and has 22 genome-wide significant ( ) as compared to 20 for the case-control. -values in both cases are adjusted for the genomic control inflation factor of 1.16. (PDF) Click here for additional data file. Table S2 Tests of deviation from the proportional hazards assumption. -values for significant SNPs for deviation from the proportional hazards assumption in the Cox model. For each SNP, we fit a Cox proportional hazards model including the SNP, sex, and five principal components as predictors, and then tested for independence of the scaled Schoenfeld residuals with time. Only one SNP deviates significantly from this assumption after correction for 22 tests. Plots for four example SNPs are shown in Figure S3. (PDF) Click here for additional data file. Table S3 Statistics for all SNPs with . All 6,141 SNPs with ( -corrected) -values under in the discovery cohort. Positions and alleles are given relative to the positive strand of build 37 of the human genome; alleles are listed as major/minor. The gene column shows the position of the SNP in context of the nearest genes. The SNP position is within the brackets, and the number of dashes gives approximate distances. The MAF is the minor allele frequency in Europeans, is the estimated imputation accuracy, HR is the hazard ratio per copy of the minor allele, and -value is the -value in the discovery cohort. (XLS) Click here for additional data file.
Author and article information
Contributors
Camelia C. Minică: +31 20 59 83035
,
c.c.minica@vu.nl
Journal
Journal ID (nlm-ta): Behav Genet
Journal ID (iso-abbrev): Behav. Genet
Title:
Behavior Genetics
Publisher:
Springer US
(New York
)
ISSN
(Print):
0001-8244
ISSN
(Electronic):
1573-3297
Publication date
(Electronic):
19
May
2015
Publication date PMC-release: 19
May
2015
Publication date
(Print):
2015
Volume: 45
Issue: 5
Pages: 503-513
Affiliations
Article
Publisher ID: 9723DOI: 10.1007/s10519-015-9723-9
PMC ID: 4561059
PubMed ID: 25987507
SO-VID: f6a789e0-fe5c-479a-8a5d-57a6f82d4250
Copyright © © The Author(s) 2015
License:
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
History
Date
received
: 5
November
2014
Date
accepted
: 27
April
2015
Categories
Subject:
Original Research
Custom metadata
issue-copyright-statement © Springer Science+Business Media New York 2015
ScienceOpen disciplines: Genetics