Background {#Sec1}
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Autism spectrum conditions (ASC) are a group of neurodevelopmental conditions characterized by difficulties in social interaction and communication alongside unusually repetitive and stereotyped behaviour and unusually narrow interests \[[@CR1]\]. ASC has an estimated heritability of around 50 % \[[@CR2], [@CR3]\], and common variants contribute to a significant proportion of the variability in the condition \[[@CR3], [@CR4]\]. ASC is polygenic and genetic variants, in addition to environmental, epigenetic and hormonal factors, contribute to ASC risk and phenotypic variability \[[@CR5]\].

Sequencing and copy number variation analyses have identified a number of rare, highly penetrant, possibly causative variants. Strategies to identify common variants through genome-wide association studies have failed to produce consistent, replicable results across cohorts \[[@CR5]\]. This may be attributed to many factors, including smaller than required sample size to adequately power these studies to identify variants with small effects. Over the last 15 years, a large number of studies have investigated common variants in candidate genes for ASC \[[@CR6]\] typically investigating variants in a small number of genes using a relatively small sample size. These studies have provided some evidence of the association of a few genes with ASC, though they are not rigorous enough to definitively identify variants and results vary based on ethnicity, sample size, study methodology and clinical ascertainment \[[@CR6]\]. One method to investigate the underlying effect using summary level data is meta-analysis \[[@CR7]\]. Though not without limitations, meta-analysis provides a fairly robust statistical framework to systematically analyse effect sizes \[[@CR7]\]. Further, the combined power of a meta-analysis greatly exceeds the power of the individual studies in a meta-analysis \[[@CR7]\].

In the field of psychiatric genetics, studies have comprehensively investigated existing candidate gene studies and used meta-analysis to investigate genetic associations \[[@CR8]--[@CR10]\]. In the field of autism genetics, such an overarching study is lacking and no study, to our knowledge, has provided a comprehensive overview of ASC genetics. To bridge this gap, we reviewed the existing literature for 552 genes implicated in ASC. Using a strict inclusion criteria, we identified common variants in 27 genes that were investigated in three or more independent cohorts. We performed meta-analyses, sensitivity analyses and subgroup analyses for these common variants and checked for publication bias in a subset of these common variants. This is the first comprehensive study of candidate gene associations in ASC.

Methods {#Sec2}
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Literature search and inclusion criteria {#Sec3}
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A preliminary literature search of genes associated with ASC was performed using SFARI gene (<https://gene.sfari.org/>) and HuGE Navigator (<http://hugenavigator.net/>). Since both of these databases do not completely document the available literature, we additionally searched PubMed, Scopus and Google Scholar. The search terms used were 'Gene name' or 'variant ID' and 'Autism' or 'Autistic Disorder' or 'Asperger Syndrome'.

Studies were included in the meta-analysis if: (1) they reported effect sizes or statistics to measure effect sizes and confidence intervals; (2) the studies were either a case-control association study or a transmission disequilibrium study of autism; (3) the variants did not deviate from Hardy-Weinberg Equilibrium (HWE) in the control group or if the sample size was too small to effectively calculate HWE due to sampling effect. Though we checked for HWE in family-based studies, this was not a requirement for including these studies as the study design overcomes the issue of population stratification; (4) cases had a diagnosis of an autism spectrum condition (Autism, PDDNOS, Asperger Syndrome) according to DSM-IV, DSM-5 or ICD-10 criteria; (5) the global minor allele frequency (MAF) of the variant investigated was greater than 0.01; (6) the studies were reported in English and (7) the common variants were investigated in independent cohorts. Authors of the articles were contacted if sufficient information was absent to use the data for meta-analysis. In addition to the published studies, we used unpublished genotype data from two cohorts from our research group at the Autism Research Centre, University of Cambridge. These cohorts are labelled 'Chakrabarti \[[@CR11]\]' and 'Warrier \[[@CR12]\]' in the current study. The characteristics of the two cohorts are described elsewhere \[[@CR11], [@CR12]\]. Details of genotyping and statistical analysis are provided in Additional file [1](#MOESM1){ref-type="media"}. We did not include data from genome-wide association studies (GWAS) as there is an overlap between participants in the candidate gene association studies and the genome-wide association studies. Since we had access to only summary data, it was impossible to ascertain the degree of overlap and remove participants accordingly. Literature search and study inclusion was performed independently by two researchers (VC and VW) from March 2014 to September 2014.

Statistical analyses {#Sec4}
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Meta-analysis was performed only if variants were investigated in three or more independent cohorts. Family-based association tests (FBATs) studies were not included as effect sizes are not calculated in FBA. For variants investigated in five or more independent cohorts, we performed a complete meta-analysis. This included the calculation of effect size and publication bias, sensitivity analysis and subgroup analysis. For variants investigated in three to five independent cohorts, we performed a partial meta-analysis restricted to the calculation of mean effect size. We did not perform a meta-analysis for variants investigated in fewer than three cohorts as there was insufficient power to significantly investigate the underlying effect. For variants with *P* values \<0.05 we calculated fail-safe N.

All analyses were performed using Comprehensive Meta-Analysis version 2.0 \[[@CR13]\]. Meta-analysis was performed using the inverse-variance weighted method. Heterogeneity in the reported effects were examined using a fixed and a random effects model. Heterogeneity was measured using *I*^2^ statistics in conjunction with Q-statistics. A fixed effect model was applied if the *P* value for Q-statistics was above 0.05 and *I*^2^ was below 60. The random effects model was used if either the *P* value was below 0.05 or *I*^2^ was above 60, as an *I*^2^ above 60 indicates that 60 % of the total observed variation is due to true heterogeneity \[[@CR7], [@CR10]\].

Egger's regression in conjunction with a funnel plot was used to assess publication bias. Sensitivity analyses were performed by removing each study from the meta-analysis and calculating the mean effect size for the remaining studies. This analysis was used to assess the contribution of each study to the final weighted effect in the analysis. Additionally, for the variants with *P* values \<0.05, we computed both classic fail-safe N and Orwin's fail-safe N to check the number of studies required to make the *P* value non-significant and make the effect size trivial respectively. For Orwin's fail-safe N, the non-significant odds ratio (OR) was kept at 1.05 or 0.95 depending on the effect direction. While this is certainly not a trivial effect size, it is difficult to identify variants with such small effects with precision given the sample sizes in the meta-analysis. Subgroup analysis was performed after stratifying based on ethnicity or study methodology to check if either of these variables affected the final effect size. We conducted the subgroup analysis only for variants investigated in five or more independent cohorts. Meta-analysis was performed only if there were at least three independent cohorts after stratification to account for power considerations.

OR and 95 % confidence intervals (CI) were used to calculate the mean effect size. For transmission disequilibrium tests (TDT), odds ratios were calculated according to methods laid out by Kazeem and Farall \[[@CR14]\]. Where possible, OR and CI were calculated using allele numbers for case-controls (CC) and transmitted and non-transmitted numbers for TDT. Where information of OR and CI was provided for the complement allele of the allele investigated in the study, the log odds ratio (LOR) and standard error (SE) were calculated and used in the meta-analysis.

Age was not regarded a confounding variable as ASC is a neurodevelopmental condition, and genetic variations are largely invariant across lifespan. However, ASC has a male-female ratio of 5:1 \[[@CR5]\], and sex is a potential confounding variable as gene expressions can vary based on sex. However, there was insufficient data to conduct a stratified analysis based on sex, so this is a limitation of the current study. Finally, due to the large number of studies carried out, we adopted a more conservative statistical significance threshold of 0.01. This is similar to what was used in a similar comprehensive meta-analysis of obsessive-compulsive disorder \[[@CR10]\]. We did not carry out a Bonferroni correction as the sample for each variant investigated was very different, and as a result, multiple tests were not carried out on the same sample.

Analysis of the PGC dataset {#Sec5}
---------------------------

While we did not choose to include data from available GWAS due to potential overlap of participants, we compared the results using the publicly available GWAS dataset from the Psychiatric Genomics Consortium (PGC). In the ASC cohort of the PGC dataset, 4788 trio cases and 4788 trio pseudocontrols as well as 161 cases and 526 controls have been genotyped. Details of the cohort, genotyping methods and statistical analysis are given elsewhere \[[@CR15]\]. We searched for effect sizes and *P* values for variants with *P* values \<0.05 in our meta-analysis. The autism PGC dataset is the largest available and accessible GWAS dataset for autism. The sample size of any of the variants investigated through meta-analysis in the study, except rs4141463 in *MACROD2*, is smaller than the sample size of the PGC autism dataset. Despite this, the PGC dataset is underpowered to detect variants with small effects. We were motivated to investigate the top variants in our study in the PGC dataset to ascertain if the candidate variants were at least nominally significant (*P* \< 0.05) and if the effect direction was concordant between the two samples.

Results {#Sec6}
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Literature review {#Sec7}
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We identified 463 genes that have been tested for genetic association using HuGE Navigator (as of August 2014). SFARI Gene reports 616 genes to be associated with autism (as of August 2014). Only 185 of these genes have been examined in ASC using genetic association studies. Of these, we identified 89 genes from the SFARI Gene list that were not included in the HuGE Navigator list, bringing the total list of potential genes to 552. We did not identify any additional genes from AutismKB database. Thus, we reviewed 552 genes in total for the meta-analysis.

Scopus, Google Scholar and PubMed were searched for publications relating to ASC and any of the 552 genes. We searched for common variations in these genes that have been investigated for ASC in at least three independent cohorts. Using the eligibility criteria outlined in the methods section, we identified 27 genes that could be taken forward for meta-analysis. In total, there were 58 common variants across these 27 genes that were investigated in our meta-analysis. Details of the studies included and excluded for the 27 genes are given in Additional file [1](#MOESM1){ref-type="media"}: Tables S1 and S2.

We next searched the literature for existing meta-analyses for the 58 variants and 27 genes in ASC, identifying existing meta-analyses for *OXTR* \[[@CR16]\], *RELN* \[[@CR17]\], *SLC6A4* \[[@CR18]\], *HOXA1* \[[@CR19]\], *HOXB1* \[[@CR19]\] and *MTHFR* \[[@CR20]\]*.* Detailed information of previous meta-analyses is provided in Additional file [1](#MOESM1){ref-type="media"}. As we had additional data and different inclusion criteria, we performed meta-analyses for all the variants in these six genes except rs723387731 in *HOXB1*, STin2 VNTR in *SLC6A4* and the GGC repeat in *RELN*. These three variants were excluded from the current meta-analyses as we could not identify additional data to add to the original meta-analyses. For the sake of comprehensiveness, we have included the data for these three variants in our table. Of the remaining 55 variants, we conducted a complete meta-analysis for 20 variants and a partial meta-analysis for 35 variants. A flow chart of the study protocol is given in Fig. [1](#Fig1){ref-type="fig"}.Fig. 1Schematic diagram of meta-analysis protocol

Mean effect sizes {#Sec8}
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Effect sizes for 15 variants in 12 genes had *P* values below 0.05. Nine of these variants had a *P* value below 0.01. The most significant association was rs167771 in *DRD3* (OR = 1.822, *P* value = 9.08 × 10^−6^). Seven other significant associations with *P* values \<0.01 were in *CNTNAP2* (rs7794745, OR = 0.887, *P* value = 0.001), *RELN* (rs362691, OR = 0.832, *P* value = 3.93 × 10^−5^), *OXTR* (rs2268491, OR = 1.31, *P* value = 0.004), *SLC25A12* (rs2292813, OR = 1.372, *P* value = 0.001 and rs2056202, OR = 1.227, *P* value = 0.002), *EN2* (rs1861972, OR = 1.125, *P* value = 0.006) and *MTHFR* (rs1801133, OR = 1.370, *P* value = 0.010). As expected for common variants in ASC, the odds ratios for the alleles tested were small and lay between 0.781 (0.446--1.368) for *MAOA* uVNTR and 1.822 (1.398--2.375) for *DRD3* rs167771. Details of the variants analysed, model used and the *P* values are provided in Table [1](#Tab1){ref-type="table"}. Forest plots for the nine most significant variants are in Additional file [1](#MOESM1){ref-type="media"}: Figures S1--S8.Table 1Summary of mean effect size analysesS. NoGeneVariantsAlleleGlobal MAFData setsMean OR (95% CI)Z-Value*P*-ValueModel (I2 value)Total casesTotal controlsTriosPGC *P*-valueEffect direction (odds ratio)Classic fail-safe NOrwin\'s fail safe N (OR = 1.05 or 0.95)**1***DRD3***rs167771G vs AG=0.411331.822 (1.398-2.375)4.449.08E-06Fixed effect (60)58075400.6discordant (0.980)7342***RELN***rs362691C vs GC=0.121080.832 (0.763-0.908)-4.113.93E-05Fixed effect (33.2)765765303NANA12213***SLC25A12***rs2292813C vs TT=0.208561.372 (1.161-1.621)3.721.97E-04Fixed effect (0)46545012200.78concordant (1.014)5254***CNTNAP2***rs7794745A vs TA=0.494640.887 (0.828-0.950)-3.451.00E-03Fixed effect (21.2)32252422360.18concordant (0.9594)965***SLC25A12***rs2056202T vs CT=0.242081.227 (1.079 -1.396)3.122.00E-03Fixed effect (6.5)756121112200.99discordant (0.9993)6266***OXTR***rs2268491T vs CT=0.213741.31 (1.092 -1.572)2.914.00E-03Fixed effect (0)2824404580.54concordant (1.026)3197***EN2***rs1861972A vs GG=0.24281.125 (1.035-1.224)2.756.00E-03Fixed effect (57.6)6691704953NANA16128***MTHFR***rs1801133T vs CA=0.2454101.370 (1.079-1.739)2.591.00E-02Random effects (88.2)2280723500.57concordant (1.018)80409***ASMT***rs4446909G vs AA=0.174151.195 (1.038-1.375)2.481.30E-02Fixed effect (0)106610740NANA31410***MET***rs38845A vs GA=0.363431.322 (1.013-1.724)2.411.60E-02Random effects (66.5)4055944190.2concordant (1.04)131511***SLC6A4***rs2020936T vs CG=0.22841.244 (1.036-1.492)2.351.90E-02Fixed effect (33.9)0010680.78concordant (1.01)31412***STX1A***rs4717806A vs TA=0.232240.851 (0.741-0.978)-2.282.30E-02Fixed effect (35.3)6531007375NANA0913***RELN***rs736707T vs CG=0.366091.269 (1.030-1.563)2.242.50E-02Random effects (76.5)97516951960.31concordant (1.035)1264814***PON1***rs662A vs GT=0.457130.794 (0.642-0.983)-2.123.40E-02Fixed effect (17.5)33464100.07discordant (1.058)01115***OXTR***rs237887G vs AG=0.399841.163 (1.002-1.349)1.994.70E-02Fixed effect (0)2824404580.94concordant (1.002)09**16*STX1A*rs6951030G vs TG=0.177141.383 (0.995-1.922)1.935.40E-02Random effects (76.7)653100737517*OXTR*rs2268493C vs TC=0.204930.845 (0.701-1.019)-1.767.80E-02Fixed effect (54.5)5741201018*ASMT* rs5989681 G vs CNA51.135 (0.984 - 1.308)1.748.20E-02Fixed effect (0)10661074019*HOXB1*rs72338773\*18INS vs nINSNA81.36 (0.97-1.33)NA1.18E-01Fixed Effect (NA)36244823820*RELN*rs2073559C vs TC=0.474630.955 (0.900-1.014)-1.51.35E-01Fixed effect (64.5)43749347321*RELN*GGC repeat\*16NANA71.11 (0.80--1.54)NA1.53E-01Fixed effect (0)878117016722*GLO1*rs2736654A vs CG=0.287341.307 (0.882 - 1.936)1.341.82E-01Random effects (68.7)857680023*PON1*rs854560A vs TT=0.182731.140 (0.931 - 1.395)1.272.05E-01Fixed effect (0)334641024*TPH2*rs11179000T vs AT=0.398831.130 (0.934-1.366)1.262.08E-01Fixed effect (0)22426035225*MET*rs1858830G vs CG=0.457580.905 (0.773-1.061)-1.232.19E-01Random effect (67.5)1975158979826*OXTR*rs2268490T vs CT=0.258451.135 (0.920-1.400)1.182.38E-01Fixed effect (0)29276145827*OXTR*rs2301261A vs GT=0.124841.127 (0.889-1.430)0.993.22E-01Fixed effect (39.1)6501300028*HOXA1*rs10951154G vs AC=0.2192130.925 (0.791-1.081)-0.983.28E-01Fixed effect (35.7)70599842529*BDNF*rs6265G vs AT=0.201330.919 (0.763-1.107)-0.893.72E-01Fixed effect (0)30346914030*HTR2A*rs6311A vs GT=0.443560.871 (0.643-1.181)-0.893.74E-01Random effects (74.8)17931339631*ITGB3*rs5918C vs TC=0.088930.866 (0.630-1.191)-0.883.77E-01Fixed effect (37.1)13916536332*MAOA*uVNTRshort vs longNA30.781 (0.446 - 1.368)-0.863.87E-01Random effects (72)436469033*MACROD2*rs4141463T vs CC=0.381870.913 (0.734-1.135)-0.824.11E-01Random effects (87.1)117035307115834*OXTR*rs2254298A vs GA=0.207150.813 (0.489-1.352)-0.84.25E-01Random effects (82.5)65013065735*ASMT* rs6644635C vs TNA41.056 (0.906 -1.230)0.694.88E-01Fixed effect (29.3)788819036*SLC6A4*rs2020942A vs GT=0.255031.062 (0.881-1.281)0.635.28E-01Fixed effect (0)0067837*OMG*rs11080149A vs GT=0.040940.847 (0.477 - 1.506)-0.565.72E-01Random effects (43.8)6513143138*ADA*rs7359837G vs AA=0.028231.375 (0.401 - 4.717)0.516.13E-01Random effects (89.1)334445039*OXTR*rs237894G vs CC=0.161550.961 (0.818-1.129)-0.486.26E-01Fixed effect (4)29276145840*OXTR*rs53576A vs GA=0.389450.966 (0.839-1.113)-0.486.31E-01Fixed effect (44.9)65013005741*OXTR*rs2268494A vs TA=0.068341.076 (0.760 -1.510)0.426.73E-01Fixed effect (0)769945842*SLC6A4*STin2 VNTR\*1712 vs 9/10NA81.129 (0.819--1.558)NA6.73E-01Random effects (68.7)0081443*NF1*GxAlu9 vs non-9NA41.131 (0.633 - 2.022)0.426.77E-01Random effects (85.7)262312044*GRIK2*rs2227281T vs CT=0.273840.929 (0.603-1.432)-0.347.32E-01Random effects (77.3)0050845*OXTR*rs2268495A vs GA=0.240641.059 (0.763 - 1.468)0.347.33E-01Fixed effect (60.4)28244645846*SHANK3*rs9616915C vs TC=0.343330.974 (0.834 - 1.138)-0.337.44E-01Fixed effect (60.1)34086330847*HTR2A*rs6314T vs GA=0.074740.949 (0.691-1.304)-0.327.47E-01Fixed effect (18.3)10321437048*CNTNAP2*rs2710102T vs CA=0.411330.989 (0.924-1.059)-0.317.60E-01Fixed effect (17.3)322524205149*OXTR*rs237885G vs TG=0.488460.981 (0.868 - 1.109)-0.37.62E-01Fixed effect (0)574120145850*COMT*rs4680Met vs Val (A vs G)A=0.369250.982 (0.851-1.134)-0.248.08E-01Fixed effect (49)8147413551*MTHFR*rs1801131C vs AG=0.249460.979 (0.824-1.164)-0.248.11E-01Random effects (56.3)18546819052*OXTR*rs1042778G vs AT=0.410941.02 (0.849-1.225)0.218.33E-01Fixed effect (0)28244045853*GRIK2*rs2227283A vs GA=0.327540.967 (0.686-1.363)-0.198.51E-01Random effects (65.65)0050854*EN2*rs3735653T vs CT=0.409741.007 (0.870-1.165)0.099.28E-01Fixed effect (0)17434949955*NF1*GxAlu8 vs non-8NA40.982 (0.602 - 1.601)-0.079.41E-01Random effects (79.2)262312056*SLC6A4*5-HTTLPRshort vs longNA170.994 (0.847-1.167)-0.079.42E-01Random effects (63.8)00203957*HTR2A*rs6313T vs CA=0.441331.007 (0.812-1.249)0.079.47E-01Fixed effect (0)0030358*EN2*rs1861973T vs CT=0.241061.004 (0.775-1.300)0.039.77E-01Random effects (80.8)6691704681Rows highlighted in bold show variants with *P* values below 0.01

Subgroup analyses {#Sec9}
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We performed subgroup analyses, stratifying by ethnicity and study methodology, for variants originally investigated in five or more independent cohorts. In the stratified analyses, six variants had *P* values below 0.05. Of these, the most significant three variants (rs2292813 and rs2056202-*SLC25A12*, rs362691-*RELN*) were also significant in the non-stratified analyses. Stratification did not increase the significance for these variants. A variant in *EN2* (rs1861973) was significant after stratifying based on both ethnicity (Caucasian only) and study methodology (TDT). Another variant in *EN2* (rs1861972) was significant after stratifying for study methodology (TDT). Finally, the STin2 variant in *SLC6A4* also exhibited a significant trend in the Caucasian-only subgroup. This result indicates that at least for a few variants implicated in ASC, ethnicity and study methodology can potentially influence the outcome. Results of the subgroup analyses are provided in Table [2](#Tab2){ref-type="table"}. Forest plots for the significant and nominally significant subgroup analyses are provided in Additional file [1](#MOESM1){ref-type="media"}: Figures S9--S15.Table 2Summary of subgroup analysesS.NoGeneVariantAlleleData setsSubgroupMean OR (95% CI)Z-Value*P*-ValueModel1*ASMT*rs4446909G vs A3Caucasian1.135 (0.886 - 1.454)13.16E-01Fixed2*ASMT*rs5989681G vs C3Caucasian1.065 (0.841 - 1.349)0.526.03E-01Fixed3*COMT*rs4680A vs G4TDT0.973 (0.840 - 1.128)−0.367.17E-01Fixed4***EN2*rs1861973T vs C4TDT0.86 (0.791 - 0.954)−2.943.00E-03Fixed**5***EN2*rs1861973T vs C3Caucasian0.880 (0.801 - 0.969)−2.269.00E-03Fixed**6*EN2*rs1861972A vs G4Case--control1.186 (0.876 - 1.605)1.112.69E-01Random7***EN2*rs1861972A vs G4TDT1.126 ( 1.025 - 1.238)2.471.30E-02Fixed**8*EN2*rs1861972A vs G4Caucasian1.118 (0.807 - 1.549)1.321.86E-01Fixed9*HOXA1*rs10951154A vs G6Case--control0.876 (0.675 - 1.137)−0.993.21E-01Random10*HOXA1*rs10951154A vs G6Caucasian0.887 (0.661 - 1.190)−0.84.23E-01Random11*HOXA1*rs10951154A vs G7TDT0.963 (0.784 - 1.159)−0.486.32E-01Random12*HTR2A*rs6311A vs G4TDT0.893 (0.602 - 1.325)−0.565.73E-01Random13*HTR2A*rs6311A vs G3Caucasian0.929 (0.542 - 1.594)−0.277.90E-01Random14*MACROD2*rs4141463T vs C5Case--control1.033 (0.944 - 1.131)0.714.78E-01Random15*MET*rs1858830G vs C7Case--control0.889 (0.749 - 1.056)−1.341.80E-01Random16*MET*rs1858830G vs C3Italian0.924 (0.592 - 1.444)−0.357.29E-01Random17*MTHFR*rs1801133T vs C4Caucasian1.398 (1.249 - 1.565)5.826.60E-02Random18*MTHFR*rs1801131C vs A3Caucasian0.904 (0.782 - 1.044)−1.371.71E-01Fixed19*OXTR*rs237885G vs T3Case--control0.950 (0.817 -- 1.106)−0.655.11E-01Fixed20*OXTR*rs2268490T vs C3TDT1.281 (0.953 - 1.721)1.641.01E-01Fixed21*OXTR*rs2254298A vs G4Caucasian0.664 (0.357 - 1.235)−1.291.96E-01Fixed22*OXTR*rs2268490T vs C4Caucasian1.114 (0.882 - 1.409)0.913.66E-01Fixed23*OXTR*rs237885G vs T4Caucasian1.039 (0.885 - 1.220)0.476.40E-01Fixed24*OXTR*rs237885G vs T3TDT1.043 (0.846 - 1.285)0.396.96E-01Fixed25*OXTR*rs2254298A vs G4Case--control1.034 (0.693 - 1.542)0.168.69E-01Fixed26***RELN*rs362691C vs G6Case--control0.857 (0.783 - 0.939)−3.321.00E-03Fixed**27*RELN*rs736707T vs C8Case--control1.187 (0.953 - 1.479)1.531.27E-01Random28*RELN*rs736707T vs C3Caucasian1.307 (0.843 - 2.025)1.22.32E-01Random29***SLC25A12*rs2292813C vs T4TDT1.419 (1.158- 1.740)3.527.33E-04Fixed**30***SLC25A12*rs2056202T vs C5TDT1.275 (1.097 - 1.482)3.172.00E-03Fixed**31*SLC25A12*rs2056202T vs C3Case--control1.105 (0.862 - 1.416)0.794.31E-01Fixed32*SLC25A12*rs2056202T vs C4Caucasian1.087 (0.873 - 1.355)0.754.55E-01Fixed33*SLC6A4*5-HTTLPRshort vs long5Caucasian0.960 (0.650 - 1.418)−0.28.38E-01Fixed34***SLC6A4*STin2 VNTR12 vs 9/104Caucasian1.492 (1.068 - 2.083)2.341.90E-02Fixed**Rows highlighted in bold show variants with *P* values below 0.05

Publication bias and sensitivity analyses {#Sec10}
-----------------------------------------

Publication bias was significant only for one variant, rs2254298 in *OXTR* (Egger's test (two-tailed) *P* value = 0.03). However, the mean effect size for the variant was not significant (*P* value = 0.425). Notably, sensitivity was significant for some variants. Of the nine variants with *P* values below 0.01, we performed sensitivity analyses on the six variants with data from more than five independent cohorts (rs7794745, rs362691, rs2292813, rs2056202, rs1861972, and rs1801133). For rs1801133, most studies contributed approximately equally, with the exception of two studies \[[@CR21], [@CR22]\]; both of these studies lowered the OR. A re-analysis of the data after removing either of the two studies decreased the *P* value of the OR (original *P* value = 0.010, *P* value after removing Park et al., 2014 \[[@CR21]\] = 0.006; *P* value after removing Schmidt et al., 2011 \[[@CR22]\] = 0.003). For rs2056202, the removal of data from one study \[[@CR23]\] increased the *P* value from *P* value = 0.002 to *P* value = 0.088. Sensitivity was not an issue for the remaining four variants that were significant. However, of the nominally significant variants, sensitivity was an issue for rs4446909, rs736707 and rs1861972. Forest graphs of the sensitivity analyses for these five variants are provided in Additional file [1](#MOESM1){ref-type="media"}: Figures S16--S20.

Analysis of the PGC dataset {#Sec11}
---------------------------

Of the 15 nominally significant variants in the current meta-analyses, 11 were genotyped in the PGC GWAS cohort, and none were found to be significant. Effect direction was concordant for 8 of the 11 variants between both the datasets. Effect sizes, as expected due to the larger sample size, were smaller in the PGC dataset for all the 11 variants, and the odds ratios were closer to 1. Total sample size was also not a significant predictor of concordance of effect direction between the two datasets. However, inspection of the datasets indicate that with the exception of rs2056202 in *SLC25A12*, the other three variants discordant for effect direction were analysed in small samples in the meta-analysis (see Table [2](#Tab2){ref-type="table"}).

The lack of significance for 11 of the 15 variants in the PGC dataset forces us to re-evaluate the significance of the remaining four variants. For two variants, the classic fail-safe N is very small (three for rs4446909 in *ASMT*, and zero for rs4717806 in *STX1A*). The latter variant was analysed using a fixed effect model and becomes non-significant when analysed using a random effect model. For the remaining two variants (rs1861972 in *EN2* and rs362691 in *RELN*), the classic fail-safe N is above 10. The sample sizes, however, are modest. These analyses indicate that the first two variants are likely to be false positives. With rs1861972, the significance in *P* value is driven largely by the TDT-only subset in the original analysis (*P* value = 0.013, see Table [2](#Tab2){ref-type="table"}). Both a case-control only subset and a Caucasian-only subset were not significant (see Table [2](#Tab2){ref-type="table"}). rs1861972 is in high LD with rs1861973 (*r*^2^ = 1), and the two variants are separated by 152 base pairs. In this study, we used the random effects model to meta-analyse rs1861973 and it was not significant. Stratifying by both study methodology and ethnicity reduced the heterogeneity considerably, allowing us to use a fixed effect model. For rs1861973, both a Caucasian-only and a TDT-only subset were significant (see Table [2](#Tab2){ref-type="table"}) but this variant was not significant in the larger Caucasian-only PGC cohort. Additional research in a larger, well-powered sample is required to confirm the significance of the two variants.

Discussion {#Sec12}
==========

This is the first study to comprehensively investigate candidate gene association studies of common variants in ASC. Using two databases, we identified 552 genes that are reported to be implicated in ASC through genetic association studies. We scanned the literature for these 552 genes and, using a strict inclusion criteria, we identified 27 genes that had sufficient data to perform a meta-analysis. Eight variants across seven genes were significant for combined effect sizes with *P* values below 0.01. Data for 11 variants was present in the PGC GWAS dataset. None of the 11 variants were significant in the PGC dataset though the majority of the variants were concordant for effect direction in both the datasets.

Effect sizes for most common variants are modest for ASC, and these results are consistent with this observation. However, there was no clear correlation between effect sizes in our dataset and the PGC dataset. Effect sizes were smaller in the PGC dataset. While most of the effects lay between 0.8 and 1.2, which is expected from GWAS data, for some variants, the effect was larger. Our most significant variant (rs167771) had data only from three studies and had a relatively high OR of 1.82 to 1.40--2.38. The small sample size for this variant inflated the OR making it significant. The effect direction was discordant for the variant in the PGC dataset, and it was not significant in this dataset.

While the sample sizes for most variants were competitive for candidate gene association studies (above 500 total cases and 500 total controls), these are not sufficient to accurately calculate effect sizes. Additionally, the different study methodologies and ethnicities contributed to heterogeneity in the sample which potentially confounded the analyses. It is clear from this study that significant heterogeneity exists for a large fraction of the variants tested. In fact, heterogeneity is significantly and positively correlated with the number of independent datasets included per variant in the analyses, indicating that the current study may not have uncovered all the heterogeneity. We were able to remove some of the heterogeneity after stratifying for ethnicity and study methodology, but heterogeneity influenced the results for some for the variants even after this. This indicates that other additional factors contribute to variance in the effect. One potential source of heterogeneity is finer population stratification. Fine-scale population stratification cannot be addressed in candidate gene association studies as these test only a few variants. Further, HWE which is used to check for population admixture among other issues is performed individually for each variant in these studies thereby failing to utilize multi-marker information to correct for population stratification. We were unable to stratify based on sex or clinical ascertainment two factors known to contribute to heterogeneity in ASC. It is unclear how clinical heterogeneity maps onto genetic heterogeneity in ASC. Existing genetic studies that stratify based on IQ or other clinical phenotype and subphenotypes have had limited success \[[@CR24], [@CR25]\]. The inability to completely identify sources of heterogeneity forced us to choose between two models (fixed effect vs. random effects), when most variants are likely to have varying levels of heterogeneity. This is a significant concern for meta-analyses using candidate gene association studies. Even if sample sizes reach competitive levels, there are no techniques currently available that can accurately account for potential confounders such as ethnicity and study methodology. Both these issues can be satisfactorily addressed in GWAS.

Another cause for concern is the small number of genes with enough data to meta-analyse. Of 552 genes, we had data for only 27 of these, less than 5 %. None of the 27 genes analysed were ASC risk genes as predicted by DAWN \[[@CR26]\]. Further, with the exception of *RELN* \[[@CR27]\] and *SHANK3* \[[@CR28]\], none of these genes have sufficient evidence to categorize them as risk genes using sequencing or copy number variation studies \[[@CR27]--[@CR31]\]. A few genes in the list of 552 genes but absent from the final list of 27 genes are predicted to be ASC risk genes. This includes *GABRB3*, *GRIN2B* and *SCN2A*. However, there was not enough evidence to evaluate the role of common variants in ASC for these genes through the current meta-analysis.

The majority of the studies analysed were of Caucasian ethnicity. We were able to stratify for a Caucasian ethnicity for some of the variants, but were not able to stratify for other ethnicities due to power considerations. It is also noteworthy that the PGC autism dataset used a Caucasian sample for analyses, and to our knowledge, there is no well-powered GWAS that investigates the role of common variants in autism in other ethnicities. Since the minor allele frequencies of the alleles tested and the variants tagged by these allele can vary depending on ethnicity, this makes it difficult to compare the results of the non-stratified meta-analyses with the PGC autism dataset. Replicating the top variants in well-powered samples from different ethnicities will help understand the ethnicity-specific risk for each variant.

The candidate gene association studies typically have small samples, which overestimate effect sizes. The lack of replication do not indicate that these loci do not contribute to the aetiology of ASC, but, rather, that there is insufficient evidence to implicate it in ASC. ASC is highly polygenic, and more than 49 % of its heritability can be attributed to common variants \[[@CR3]\]. As effect size for each individual common variant are likely to be very modest and not likely to exceed an OR of 1.3, this indicates that there are several common variants that contribute to the condition. Disentangling this would require very large sample sizes, much larger than those in the current PGC autism GWAS. It is evident, from the current study, that candidate gene association studies in ASC have been underpowered to reliably detect causative variants with precision.

Conclusions {#Sec13}
===========

While recent studies \[[@CR2], [@CR3]\] have identified that common variants, *en masse*, contribute to a significant fraction of ASC, there have not been any sufficiently powered studies to date to identify important common variants. We attempted to address this issue using a meta-analysis of candidate gene association studies. Though this is the first comprehensive study of candidate gene association studies in ASC, it failed to identify causative variants---11 of 15 variants with *P* values \<0.05 were not significant in a larger sample from the PGC. Data was unavailable for the remaining five variants in the PGC dataset. We discuss the potential issues with such an approach and underline the need for much larger sample sizes to accurately identify common variants that contribute to ASC.

**Additional file** {#Sec14}
===================

Additional file 1: Table S1.Studies included and study characteristics and**Table S2.** Studies Excluded; **Figures S1--S20.** Forest plots of significant variants from global and subgroup analysis, and sensitivity plots; details of data from our lab, details of previous meta-analyses, and references. (PDF 707 kb)

ASC

:   autism spectrum conditions

CC

:   case-control

CI

:   confidence intervals

FBAT

:   family-based association test

GWAS

:   genome-wide association study

HWE

:   Hardy-Weinberg Equilibrium

LOR

:   log odds ratio

MAF

:   minor allele frequency

OR

:   odds ratio

PGC

:   Psychiatric Genomics Consortium

SE

:   standard error

Varun Warrier and Vivienne Chee contributed equally to this work.

Bhismadev Chakrabarti and Simon Baron-Cohen are equal senior co-authors.

**Competing interests**

The authors declare that they have no competing interests.

**Authors' contributions**

VW, SBC and BC co-designed the study. VC, VW and PS analysed the data. SBC obtained the funding for the study. All authors wrote and edited the manuscript. All authors read and approved the manuscript.

We are grateful to Dr. Anitha Ayyappan Pillai, Prof. Elisabetta Trabetti and Dr. Wouter Staal for data-sharing. We thank Florina Uzefovsky for her critical comments and advice. This study was funded by grants from Target Autism Genome, the Autism Research Trust, Wellcome Trust Sanger Centre, and the Medical Research Council UK. VW is funded by the Nehru Trust for Cambridge University, St. John's College, and Cambridge Commonwealth Trusts. This study was submitted for the partial fulfilment of an MSc degree for VJC from Imperial College London, and a PhD degree for VW from the University of Cambridge.


Q:

Добавление в локальное хранилище (localStorage) значения со стилями

Есть, допустим, такая ситуация. Необходимо в localStorage добавить "Hello, world!", но чтобы после перезагрузки текст оставался красным цветом. Возможно ли такое сделать? Подскажите, пожалуйста, каким образом или перенаправьте на похожие темы.
Код самый простейший, просто для примера:
    <!DOCTYPE html>

A:

Если переключать именно класс с цветом, то можно так:

    let wrapper = document.getElementById('wrapper');

    let someText = document.createElement('h1');
    let someBtn = document.createElement('input');
    let someBtn2 = document.createElement('input');

    someBtn.setAttribute('type', 'button');
    someBtn.setAttribute('value', 'Change color');
    someBtn2.setAttribute('type', 'button');
    someBtn2.setAttribute('value', 'Add to local storage');

    someText.innerHTML = 'Hello, world!';
    wrapper.appendChild(someText);
    wrapper.appendChild(someBtn);
    wrapper.appendChild(someBtn2);

    let NameOfClass = "red"; // Название класса

    let col = localStorage.getItem('color1'); //Проверяем параметр в хранилище
    if (col){ //Если есть
        someText.className = col; //Присваиваем класс
    }

    someBtn.addEventListener('click', () => {
        someText.className = NameOfClass;
    });

    someBtn2.addEventListener('click', () => {
        JSON.stringify(localStorage.setItem('text', someText.textContent));
        localStorage.setItem('color1', NameOfClass); //Сохраняем класс в хранилище
    });
        .red {
            color: red;
        }

Вариант с цветами, а не с классами.

    let wrapper = document.getElementById('wrapper');

    let someText = document.createElement('h1');
    let someBtn = document.createElement('input');
    let someBtn2 = document.createElement('input');

    someBtn.setAttribute('type', 'button');
    someBtn.setAttribute('value', 'Change color');
    someBtn2.setAttribute('type', 'button');
    someBtn2.setAttribute('value', 'Add to local storage');

    someText.innerHTML = 'Hello, world!';
    wrapper.appendChild(someText);
    wrapper.appendChild(someBtn);
    wrapper.appendChild(someBtn2);

    let color = ""; // Создаём переменную

    let col = localStorage.getItem('color1'); //Проверяем параметр в хранилище
    if (col){ //Если есть
        someText.style.color = col; //Присваиваем цвет
    }

    someBtn.addEventListener('click', () => {
        color = "red"; // Задаём нужный цвет
        someText.style.color = color;
    });

    someBtn2.addEventListener('click', () => {
        JSON.stringify(localStorage.setItem('text', someText.textContent));
        localStorage.setItem('color1', color); //Сохраняем класс в хранилище
    });

Пример с несколькими цветами:

        let wrapper = document.getElementById('wrapper');
        let wrapper1 = document.getElementById('wrapper');
        let wrapper2 = document.getElementById('wrapper');

        let someText = document.createElement('h1');
        let someText1 = document.createElement('h2');
        let someText2 = document.createElement('h3');
        let someBtn = document.createElement('input');
        let someBtn2 = document.createElement('input');

        someBtn.setAttribute('type', 'button');
        someBtn.setAttribute('value', 'Change color');
        someBtn2.setAttribute('type', 'button');
        someBtn2.setAttribute('value', 'Add to local storage');

        someText.innerHTML = 'Hello, world1!';
        wrapper.appendChild(someText);
        wrapper.appendChild(someBtn);
        wrapper.appendChild(someBtn2);

        someText1.innerHTML = 'Hello, world2!';
        wrapper1.appendChild(someText1);

        someText2.innerHTML = 'Hello, world3!';
        wrapper2.appendChild(someText2);

        let color = []; // Создаём переменную

        let col = JSON.parse(localStorage.getItem('color1'));
        if (col){ //Если есть
            someText.style.color = col[0]; //Присваиваем цвет
            someText1.style.color = col[1]; //Присваиваем цвет
            someText2.style.color = col[2]; //Присваиваем цвет
        }

        someBtn.addEventListener('click', () => {
            color.push("red"); // Задаём нужный цвет
            someText.style.color = color[0];
            color.push("blue"); // Задаём нужный цвет
            someText1.style.color = color[1];
            color.push("orange"); // Задаём нужный цвет
            someText2.style.color = color[2];
        });

        someBtn2.addEventListener('click', () => {
            JSON.stringify(localStorage.setItem('text', someText.textContent));
            localStorage.setItem('color1', JSON.stringify(color)); //Сохраняем класс в хранилище
        });


Upton Town Hall

Upton Town Hall is a historic town hall at 1 Main Street in Upton, Massachusetts.  The Late Gothic Revival/Queen Anne brick building was built in 1884 to a design by Worcester architects E. Boyden & Son.  It features irregular massing typical of Queen Anne style, with a variety of gables and roof lines.  When first built, the building housed town offices and the library, the latter of which has since moved out.  The interior includes a richly-decorated auditorium.

The building was listed on the National Register of Historic Places in 1999.

See also
National Register of Historic Places listings in Worcester County, Massachusetts

References

Category:City and town halls on the National Register of Historic Places in Massachusetts
Category:Buildings and structures in Worcester County, Massachusetts
Category:Town halls in Massachusetts
Category:National Register of Historic Places in Worcester County, Massachusetts
Category:Historic district contributing properties in Massachusetts


// +build !linux

package seccomp

// These are copied from linux/amd64 syscall values, as a reference for other
// platforms to have access to
const (
	CloneNewIPC    = 0x8000000
	CloneNewNet    = 0x40000000
	CloneNewNS     = 0x20000
	CloneNewPID    = 0x20000000
	CloneNewUser   = 0x10000000
	CloneNewUTS    = 0x4000000
	CloneNewCgroup = 0x02000000
)


# Filter Chain Effects

The name effect is maybe a little bit confusing, as `crop` or `thumbnail` does not sound like an effect at all, but there are other things which are effects. Actually an effect is an array of items in the `chain()` method of a [Filter](app-filters.md).

Each chain effect can have several arguments which changes the behavior of the effect. Since we have integrated the [Yii 2 Imagine Extension](https://github.com/yiisoft/yii2-imagine) the arguments to passed are equals to the supported methods in the extension. The thumbnail behavior has been strongly improved by this extension to calculate images based on values without doing the math on your own.

### Available Effects

|Name       |Description
|---        |---
|thumbnail  |Create a thumbnail based on the height/width or calculate missing height/widths. You can also directly crop images with the thumbnail effect.
|crop       |Crop an image to defined size on the given point/location of the image

## Thumbnail

The most powerful and commonly used effect is thumbnail.

|Option     |Required   |Description
|---        |---        |---
|width      |yes        |The width in pixels to create the thumbnail, can be `null` to auto calculate the size to keep the ratio.
|height     |yes        |The height in pixels to create the thumbnail, can be `null` to auto calculate the size to keep the ratio.
|mode       |no         |Mode of resizing original image to use in case both width and height specified, by default its `self::THUMBNAIL_MODE_OUTBOUND`.
|saveOptions|no         |Array with options to pass to the imagine `save()` method, example quality.

If one of thumbnail dimensions is set to `null`, another one is calculated automatically based on aspect ratio of original image. Note that calculated thumbnail dimension may vary depending on the source image in this case.

If both dimensions are specified, resulting thumbnail would be exactly the width and height specified. How it's achieved depends on the mode.

If `self::THUMBNAIL_MODE_OUTBOUND` mode is used, which is default, then the thumbnail is scaled so that its smallest side equals the length of the corresponding side in the original image. Any excess outside of the scaled thumbnail’s area will be cropped and the returned thumbnail will have the exact specified width and height.

If thumbnail mode is `self::THUMBNAIL_MODE_INSET`, the original image is scaled down so it is fully contained within the thumbnail dimensions. The rest is filled with background that could be configured via [[Image::$thumbnailBackgroundColor]] and [[Image::$thumbnailBackgroundAlpha]].

Generate thumbnail but calculate the height for the given width of 500

```php
[self::EFFECT_THUMBNAIL, [
    'width' => 500,
    'height' => null,
    'saveOptions' => ['quality' => 80]]
]
```
Generate thumbnail cropped to 500x500 pixel:

```php
[self::EFFECT_THUMBNAIL, [
    'width' => 500,
    'height' => 500,
    'saveOptions' => ['quality' => 80]]
]
```

Generate thumbnail cropped to 500x500 pixel but does at background color to fill up the file if it does not match the inset bounds.

```php
[self::EFFECT_THUMBNAIL, [
    'width' => 500,
    'height' => 500,
    'mode' => self::THUMBNAIL_MODE_INSET,
    'saveOptions' => ['quality' => 80]]
]
```

## Crop

|Option     |Required    |Description
|---        |---        |---
|width      |yes        |The crop width
|height     |yes        |The crop height
|start      |no         |The starting point. This must be an array with two elements representing `x` and `y` coordinates. If not provided its `[0,0]`.
|saveOptions|no         |Array with options to pass to the imagine `save()` method, example quality.

Crop the image to 500x500 pixel.

```php
[self::EFFECT_CROP, [
    'width' => 500,
    'height' => 500,
    'saveOptions' => ['quality' => 80]]
]
```

Crop the image to 500x500 pixel but start on 10,10 coordinates.

```php
[self::EFFECT_CROP, [
    'width' => 500,
    'height' => 500,
    'start' => [10, 10]
    'saveOptions' => ['quality' => 80]]
]
```


<annotation>
<folder>widerface</folder>
<filename>25--Soldier_Patrol_25_Soldier_Patrol_Soldier_Patrol_25_704.jpg</filename>
<source>
<database>wider face Database</database>
<annotation>PASCAL VOC2007</annotation>
<image>flickr</image>
<flickrid>-1</flickrid>
</source>
<owner>
<flickrid>yanyu</flickrid>
<name>yanyu</name>
</owner>
<size>
<width>1024</width>
<height>661</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>face</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>548</xmin>
<ymin>108</ymin>
<xmax>674</xmax>
<ymax>286</ymax>
</bndbox>
<lm>
<x1>618.344</x1>
<y1>199.862</y1>
<x2>628.429</x2>
<y2>205.464</y2>
<x3>622.826</x3>
<y3>245.804</y3>
<x4>585.848</x4>
<y4>251.406</y4>
<x5>589.21</x5>
<y5>253.647</y5>
<visible>1</visible>
<blur>0.79</blur>
</lm>
<has_lm>1</has_lm>
</object>
</annotation>


Modern lawns require a great deal of care and attention in order to nurture a thick green carpet of grass. Such care includes consistent mowing, watering, and thatching, as well as periodic overseeding and fertilization. In addition, it is imperative that a healthy lawn be aerated at least twice a year and, preferably, even more often. Aeration usually entails creating a multitude of closely spaced small holes in the surface of the ground to assist in the absorption of oxygen and nutrients into the soil. The holes also tend to increase moisture penetration into the soil and serve as receptacles for grass seed to prevent the seed from being washed away and to promote germination and growth.
Numerous lawn aeration devices have been available. One such device comprises a large cylindrical drum studded about its periphery with a plurality of short radially extending spikes. The drum is rolled or pulled over the ground and, as it rolls, the spikes are driven into the soil to create shallow holes. While this device is widely used and has proven somewhat successful, it is nevertheless plagued with numerous problems and shortcomings inherent in its design. For example, since the spikes necessarily engage the ground at an angle and are rotated laterally through the soil, significant force is required to penetrate the ground and move the spikes through the dirt. As a result, the drums of these devices generally are relatively large and usually are filled with water or sand to provide sufficient weight to drive the spikes into and through the soil. In some instances, auxiliary racks are provided to load even more weight onto such aerators. To frustrate matters more, the density of spikes on the drum and thus the density of holes the aerator can make in the soil is severely limited since the weight of the device is inherently inadequate to drive more than a few of the spikes at a time into and through the soil. Finally, as each of the spikes of this device is forcibly driven into the soil, it pushes aside and down the dirt to make room for the spike. This is the action that actually creates the hole; however, it also necessarily compacts and hardens the soil around the sides and bottom of the hole. As a result, penetration of air from within the hole into surrounding soil is reduced as is the penetration of moisture. Consequently, the efficiency and advantages of the aeration are reduced.
Another lawn aeration device seeks to address the soil compaction problems of drum and spike aerators by providing hollow spikes that actually pierce the ground and remove a plug of soil to create a hole. During each penetration of the ground, another soil plug is forced upwardly through the hollow spike and the plugs are simply ejected from the spike at its upper extent. While these types of devices, commonly known as pluggers or core type aerators, tend to reduce the compaction of soil around the sides of the holes, they nevertheless do not eliminate it. This is because the soil must still be parted to accommodate the thickness on the walls of the hollow spikes as they pierce the soil. In addition, such hollow spike aerators still require significant force to drive them into and through the soil and thus still require large, heavy, and cumbersome structures for proper operation. This is due to the relatively high sliding friction of the soil plug through the hole in the spike requiring extra force to drive the plug through the soil. As a result of this extra force, the extracted plugs are usually highly compacted dowel-like plugs, which do not break up and disperse quickly.
Because of their weight, many lawn aerators, and almost all commercial lawn aerators, are motorized. This not only makes them expensive but also renders them difficult to use. When the heavy spike drums are driven over the ground by their motors, they naturally bump, bounce, and shake about as the spikes are driven into and through the soil. This can create significant fatigue for users of these aerators. Further, the unitary drum construction of these devices renders them very difficult to turn at the end of an aerating run and the drum often must be manually scraped about in an arc to achieve the turn. This is not only cumbersome, it also tends to destroy healthy grass already growing in the lawn and can create an unsightly mess, particularly in moist or wet soil.
Slicer aerators having harrow-like discs or thin curved knife blades that create or cut a narrow furrow, slice, or series of narrow slices in the soil, are also available. These devices embody a problem in that they tend to create narrow soil openings that close up very quickly, especially when stepped upon. In addition, the force required for penetration of the soil can be significant in these types of aerators so that substantial weight must be provided for their operation. As a result, these type of aerators, which are common for commercial use, tend to be mounted on frames that can be pulled behind a tractor. Obviously, this limits the use of these aerators significantly and generally makes them useful only for large scale operations such as the aeration of golf courses and sports fields. Even if a slicer aerator concept was adapted to a small lawn, the thin easily closed slices produced by these aerators still would be unacceptable.
Many of the problems associated with prior art lawn aerators have been addressed for small scale consumer aeration through the inventions described and claimed in applicant's pending U.S. patent applications, of which the present application is a continuation-in-part. The inventions disclosed in these pending applications are hereby incorporated by reference. In general, these unique inventions include a novel aerator wheel having a hub and four curved tines projecting therefrom. The placement, curvature, size, and configuration of the tines has been found to provide surprisingly exceptional soil penetration as the aeration wheel rolls across the ground. This is because the configuration of the tines takes advantage of knife-like soil piercing and the assistive force of a previous tine pulling itself out of the soil to provide much of the force necessary to drive the next successive tine into the soil. As a result, these aerator wheels require relatively little additional downward force for their proper operation. This phenomenon makes it possible to mount two or three of the aerator wheels on the back of a consumer lawnmower where the small force required to drive the wheels across the ground for aeration is provided by the weight of the mower itself.
While the inventions of the parent applications hereto have proven especially useful for attachment and use with consumer lawnmowers, they have not proven completely acceptable for use with large commercial walk-behind lawnmowers. This is due at least in part to the fact that, with commercial lawnmowers, it is desirable to provide six or more spiked aerator wheels across the mower breadth for providing aeration holes in the ground of sufficient density. This requirement in conjunction with the weight distribution on a typical commercial lawnmower makes it undesirable to use the weight of the mower itself to drive the aerator wheel spikes into and through the soil because the resulting lift on the mower can cause it to loose traction.
Accordingly, there exists a continuing need for an aerator assembly that can be conveniently mounted to the back deck of a commercial lawnmower for aerating the soil as the lawn is mowed. Such an aerator assembly should provide at least six spiked aerator wheels for acceptable aeration density and should not make use of the weight of the mower itself to drive the aerator wheels into and through the soil. In addition, such a commercial aerator assembly should track the movement of the lawnmower, even when the mower is engaged in tight turns and even when it is reversed and moved backwards. The aerator assembly should not interfere with the normal operation of the lawnmower to which it is attached and should provide superior aeration by producing holes in the soil with loose dirt sides and by depositing dirt from the hole onto the top of the ground for disbursement. Finally, such a commercial aerator should be economical to produce, market and use and should be simple to install on the back of a commercial lawnmower deck. It is to the provision of such a commercial aerator assembly that the present invention is primarily directed.


Q:

Thought experiment about our location in the Universe

[I am sorry if this isn't the best place for this - it was either here or maybe on the Worldbuilding SE.]
I recently re-read one of my favourite childhood sci-fi books (Chocky by John Wyndham), and in it a young boy is asked by an alien being, where in the Universe our planet is.
It occurred to me that this is almost impossible to describe.
Nowadays, with Wikipedia and the Internet, we can get some of the way by stating that we're in a galaxy that is approximately 10 billion years old, light takes approximately 100000 years to cross it, and which is part of a local group, itself part of a cluster, and that part of a super-cluster.
Unfortunately the alien might then ask a follow-up clarification question such as what is a year?
So we go to basics - in base 60 (as opposed to binary or base 10), our basic unit of time is called a second, and there are 60 seconds in a minute, 60 minutes in an hour and 24 hours in a day - which is the time our planet takes to rotate once completely. Our planet orbits a local star (that we call the sun) and each complete orbit takes approximately one year, and light takes around 8 minutes to get from our local star to our planet.
Unfortunately our alien might then ask, but how long is a second? - so we then get into atomic clocks, caesium 133 and phases of radiation, followed by the next inevitable question: what is caesium. So we have to get down to the Periodic Table of the Elements and decribe an atom, atomic weight, and how to define caesium in a way that a far off advance civilization could relate to - not to mention perhaps a tangent about Prime Numbers.
So if our alien was lucky enough to be in contact with someone like Professor Brian Cox, or a physyicist with astronomical leanings, how would such a description and conversation go?

A:

It all depends on shared knowledge. What you are getting at is known in philosophy as indeterminacy of translation. But note that the alien is already talking to the human: there has to be a language present, and languages do contain a lot of assumptions about the world (tenses imply that there is time, and the question itself implies space, for example).
Now, consider a sequence of larger and larger scale maps of the neighbourhood, running from one showing the solar system to one showing the cosmic web. That seems to settle the "where" question using pattern matching fairly well...
...except that there is the obvious question of what the maps show. In this particular example it is stellar baryonic matter, about 0.002 of the total mass/energy budget. If the alien is made of dark matter this will not make much sense except that it would on the larger scales recognise the topology of the web of halo structures it was familiar with. If it knew about baryonic matter it would infer that this was a map of baryonic stuff. Similarly a gaseous alien might think we have left out the 90% of baryonic matter that is intergalactic plasma. But the point is, the right kind of map or explanation would latch onto something in the knowledge of the alien and allow them to start inferring more meaning.
Since this is not just sending a single message but a conversation there is a lot of room for testing and exploration:
"So you are telling me where you are using a map showing 'pulsars'. Are they [untranslatable1] or [untranslatable2]?"
"What is the difference between [untranslatable1] and [untranslatable2]?"
"[untranslatable1] is spatially localised but extends across time,
 [untranslatable2] is temporally localised but spread out in space."
"Then [untranslatable1] sounds more reasonable. We know them as compact stars that rotate fast and send radio waves."
"Are 'stars' also spatially localised and extend across time?"
...
It won't be easy, and it is quite possible that our map doesn't correspond to a known region (if the aliens are communicating in some weird non-local way; had they communicated in a local way they would have known the answer to the questions already). But pattern matching and interactive bootstrapping from the shared language and knowledge-base would be a way of getting around the indeterminacy of translation.


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Edit: Most of these numbers are actually wrong because of an error on ESPN which I used for my data, but the error only improves the numbers in favor of my central point not against.Basically scratch one possession from the Panthers and reduce their starting field position to 20.17 yard line

Somethings to consider when railing against this offense

We averaged over 30 yards per drive. (31.62). That is actually higher than what Seattle gave up for the season last year (29.86) and is only slightly below what we averaged last season (33.82) which was good for top 10 in the league (9th). That means that the expected yardage per drive when comparing the two teams is 31.84 YPDs (the average of our offense and their defense from a year ago)

We had an average starting field position on Sunday of our own the 24.25. As compared to last season league averages that would have ranked us 31st in the NFL. If a scoring zone is the opponent’s 33 yard line (50 yard field goal) we would have to outperform our drive expected averages by 9 yards above average or at approximately 125% of expected efficiency just to get a field goal.

So that shows us two things. With that type of field position we were, on average, not going to get into scoring position. And that field position would be most important factor to dictate scoring opportunities in this game by our offense.

It also shows that the offense performed on average about how it should be expected to perform considering past offensive play and Seattle's past defensive play.

When we fumbled the punt, and lost the field position game, we were put in a point where based off of our statistics we were going to have immense trouble putting ourselves in scoring positions.

This is context of a game that does not have anything to do with playcalling or aggressiveness. It is just how the game played out.

When we lost field position, we put ourselves in a crippling position to not be successful or score many points.

Our field position for our last three possessions after the punt fumble was on average our own 16.67 yard line.

So that makes scoring opportunities even less likely. In order to put ourselves in scoring position after the punt fumble we would have had to perform at approximately 160% of expected efficiency.

That is just for FIELD GOALS. Not even considering TDs.

This is why I have said, the fumble on the punt, although Seattle did not score any points off of it, was the play that cost us the game. even more so than the Williams fumble at the end.

7 points is not a lot, and we had opportunities and messed them up.

In a game with only 8 possessions and 50 plays, these minor mistakes become glaring, and result in games where you only score 7 points.

The offense was not unusually conservative, nor did underperform, it played commensurate with what should have been expected based on the conditions of the game, and the opponent in which we played.

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I thought we moved the ball fairly well. Just couldn't finish for foolish errors and mistakes.. These stats reflect that as it is just the truth.

If you were watching the game objectively the difference was simple... The playmaking ability of Russell Wilson. Mistakes aside, because both teams made mistakes, he made game winning plays. With that said we still could have and should have won. I am as positive as ever about our chances this season.


College Career

Year Rec Yards Avg Long TD 2014 26 442 17 55 6 2013 17 367 21.6 72 3 2012 8 162 20.3 63 0

At first glance, the numbers don't look very impressive at all. Darren Waller failed to eclipse 1,000 yards over the course of this entire career, with his best year resulting in at a paltry 442 yards. The numbers don't tell the entire story of Waller's career, however. He initially struggled to adjust to the college game, and wasn't a significant part of the offense until 2013. However, he was still the number two receiving target behind Deandre Smelter by a large margin. Much like Synjyn Days, Waller only put up big numbers for part of his final season after in injury opened the door for him to be the go-to guy. Also, like Days, Waller stepped up big. After an injury to Smelter in the regular season finale, Waller lit up the postseason with two huge games. His stats from those games are shown below:

Opponent Rec Yards Avg Long TD Florida State 5 73 14.6 25 1 Mississippi State 5 114 22.8 41 1

Waller came up big in both of these games, especially the Orange Bowl. The receptions were limited in both games, but his catches went for big gains. The big play potential is what has the NFL interested, but there is a risk involved. As I stated in the Days scouting report, there is a risk involved in drafting a player who only put up great numbers for a short period of time. With only two big games in his entire career, Waller may be the epitome of this type of gamble.

Combine Results

Waller was the only Georgia Tech prospect to participate in drills at the combine, and he was able to greatly improve his stock in the process. I've always thought of Waller as a "poor man's Kelvin Benjamin" given the similarities in their strengths and weaknesses coming into the draft. Both had limited production during their careers, had great size, and were considered raw, lacking the necessary route running techniques. In addition, both had questionable effort and concentration. at the college level and were considered high risk. What made Benjamin more highly regarded was his catch radius. He was able to make ridiculous catches far away from his body, something Waller hasn't shown. Benjamin also had more production during his senior year than Waller. Due to the similarities, I compared their combine numbers.

Player Height Weight (lb) Arm (in) Hand (in) 40 Yard Dash (s) Vertical Jump (in) Broad Jump (in) 3 cone (s) Short Shuttle (s) Long Shuttle (s) Bench Reps Darren Waller 6'6" 238 33 1/4 9 4.46 37 125 7.07 4.25 11.35 12 Kelvin Benjamin 6'5" 240 34 7/8 10 1/4 4.61 32.5 119 7.33 4.39 12.08 13

Tools, tools, tools. Waller's got more of them than your local Home Depot. These numbers are ridiculous, and far better than Kelvin Benjamin's numbers from a year ago. He's taller, faster, and had more burst at the combine. Waller's long shuttle was one of the best at the Combine. These numbers show he has the athleticism to run the entire route tree and gain separation, even if he has limited experience doing this in games. Waller's arms aren't quite as long as Benjamin's, and his smaller hands will only exacerbate concerns about drops at the next level. However, the numbers overall show tremendous upside that will entice teams to take the a risk in drafting him.

Film Study

This film study will primarily focus on the games where Waller had the highest volume of work: Florida State and Mississippi State, but there will be clips from other games scattered in to provide examples of Waller's traits.

Strengths

Sideline Awareness

Waller shows good awareness of both where his feet are and where the sideline is. There are several plays where he shows the presence of mind to get one or both of his feet down on sideline catches, making him an even more reliable target for Justin Thomas. This is a great trait to have for catches in the end zone, where "toe-tapping" is often required in order to catch the ball in close coverage. Waller's great sideline play against Georgia Southern shows off his ability. It's simply a great catch. Thomas put the ball where only Waller could get it, and Waller made sure he came down with it.

Size

Ok, so size is definitely more of a measurable than a film strength, but the way in which Waller uses his size is what makes it a strength. At 6' 6 5/8", Waller has ridiculous height that will cause severe match up problems no matter where he is placed on the field. Against corners, he uses his size to shield the defender from the ball and uses his height to go up and grab the ball at a higher point than any corner could hope to reach. If the ball is thrown right, it's almost unfair. This trait is great for the red zone, as it allows for the QB to throw jump balls, trusting that his receiver will come down with the ball or draw pass interference. One of the simplest and best representations of this ability came against Georgia. Waller runs into the end zone, turns around and boxes the corner out with his body, he then climbs the ladder to catch the ball at its high point. The corner can't do anything but watch Waller score the touchdown.

Physicality

At almost 240 lbs, Waller is difficult to bring down. Most cornerback prospects hover around 200 lbs, so Waller will be able to use his extra bulk to beat smaller corners for contested catches. This is another trait that is valuable in the red zone, as there isn't a ton of room to gain separation on a short field. These first 2 strengths will make Waller an extremely dangerous threat that teams will covet. "Matchup Nightmare" is a football buzzword that has become more and more heavily used to describe players in recent years. Waller certainly seems to fit the bill. In this play from the Virginia game, Waller goes up for a fade route in a fashion similar to the previous clip, but has to fight for the ball this time. He still comes down with it.

The next play came from the ACC championship, and was one of Waller's best plays in a Georgia Tech uniform. He uses his strength to break away from FSU CB PJ Williams, a highly regarded prospect, and takes it to the house. This isn't Williams' best tackle attempt, but the size and physical mismatch are evident in this play.

Deep Threat

Waller displays surprising speed for his size, clocking in at 4.46 seconds in the 40-yard dash at the Combine. This combined with his height make him a fantastic deep threat. He doesn't quite have the speed to really burn NFL corners, but has just enough speed, and his size and physicality will still make him incredibly difficult to defend down field. If he can get any separation whatsoever, he'll be able to easily high point the ball once it gets to him. Waller's long touchdown from the Orange Bowl will provide a good demonstration. Waller beats the cornerback deep, and all the defender can do is hold on for dear life. Even though the holding prevented Waller from gaining good separation once the ball arrived, he still went up and caught the ball where the defender had no hope of breaking up the play.

The deep ball is another means by which Waller is a "Matchup Nightmare." If he's placed outside, he can beat the corner deep in man coverage. If a safety is used to assist in covering Waller, then that takes attention away from the middle of the defense. When Waller is lined up on the inside, linebackerss have no business covering him, and bringing a safety down to do so leaves 1-on-1 opportunities for receivers on the outside. No matter what you do, you can't win.

So Darren Waller is the perfect WR prospect who is absolutely impossible to cover and will be the next coming of Calvin Johnson in the NFL. Right? Right? Guys? Well, not really. The strengths show an intriguing promise that has NFL teams interested, but there are some serious flaws to Waller's game that he will need to address in order to reach his full potential.

Weaknesses

Motor

When watching film, Waller noticeably takes some plays off. Most of these occur when he is not the primary target or is not blocking on the play side, but the lack of 100 percent effort will not sit well with teams. His lack of effort could telegraph the play to opponents if they recognize a pattern. In addition, not blocking on the backside could allow his man to make a play in pursuit. Waller will probably get grilled about this in interviews with teams, and will need to show teams that he intends to up his effort level as a professional.

Run Blocking

At Waller's size, one would expect this to be a strength, but he is a surprisingly poor run blocker. Waller's blocking technique involves using his body to shield the defender from run plays. Sometimes this works, but if he used proper technique he'd be using his hands to drive the corner down field. Considering his size, this is something that could likely be coached into him. Waller also struggles to find his assignment while blocking and occasionally whiffs horribly. Revisiting the great Synjyn Days run from the Orange Bowl, I was struck by how badly Waller missed his assignment. If he blocks properly, Days has to make one less man miss.

When I saw this play on film, my first thought was "What are you doing Darren?" This is another coachable trait, but it means Waller will have a LOT to work on in his run blocking. Which isn't good because he also needs to work on.....

Tracking the Ball

Yet another thing that came up in the Orange Bowl was the fact that Waller noticeably slows down when tracking the ball overhead. By doing this, he inhibits his ability to go for the deep ball, which is one of his strengths. In order to reach his full potential as a deep threat, he'll need to fix this. It's another coachable trait, but these coachable traits are adding up, and increase the risk involved with drafting him. Waller does this most notably during the Orange Bowl, where he was unable to catch up to a deep pass from Justin Thomas despite getting behind the entire defense. He also slowed down before the defender grabbed him on his great deep touchdown from the previous video, but still made the catch.

YAC(Yards After Catch)

Despite his great touchdown from the Florida State game, Waller isn't very good at getting YAC. He lacks the short field quickness and fluid hips required to make defenders miss in the open field, and thus was never used on screen plays or reverses. There were only two screen plays during the entire Orange Bowl, and the both happened to be thrown to Michael Summers for this reason. This is the weakness Waller will be least able to correct, so he will have to be utilized in ways that are best suited to his strengths, just as he was while at Georgia Tech.

NFL Fits

Kansas City Chiefs

I'm sure every Chiefs fan is tired about hearing how their wide receivers didn't score a single touchdown in 2014. It's an incredible stat, and one I'm sure the front office will be trying to correct. Adding someone like Waller could take the pressure off of TE Travis Kelce and RB Jamaal Charles in the red zone, and could help to ease the loss of Dwayne Bowe. The Cheifs did sign Jeremy Maclin this offseason, but Waller could add size to their receiving corps and complement Maclin well.

Atlanta Falcons

This is an outcome many Tech fans would love to see, and it could definitely happen. The Falcons have not properly replaced Tony Gonzalez since his retirement, and Waller could be the guy. I could see him playing either tight end or wide receiver here. Julio Jones is a great receiver, but having another red zone threat would help take some of the pressure off. Roddy White isn't getting any younger either.

New Orleans Saints

With the shocking trade of Jimmy Graham to the Seahawks, Drew Brees needs some help. Graham was a touchdown machine his entire time in New Orleans and needs to be replaced. Marques Colston will be reaching the end of his career in the next few season as well. On top of that the Saints traded away Kenny Stills. Problem is, the Saints have more pressing needs on the defensive side of the ball that they will need to address in the early rounds. This may make Waller a prime target in the mid rounds, where he'll provide great value.

New England Patriots

This one would be interesting. Before Aaron Hernandez was arrested on murder charges, the Pats loved to run two tight end sets, and did so with great success. It's now more of a one many show starring Rob Gronkowski. The Patriots could really look at Waller or someone similar to bring back the capability they had before.

Cleveland Browns

The Browns have reportedly met with Waller, and this would be a great fit. They lost two big weapons this off season in Jordan Cameron and Josh Gordon, so they have a big need at WR. They signed Dwayne Bowe, but he is a temporary solution who could cover Waller's receiving duties while he develops. Waller could be either the deep threat or receiving TE the Browns need, so his versatility could be enticing.

Verdict

Drafting Waller may be the definition of gambling on a late-blooming prospect. He has all the tools to make it as a WR or TE at the next level, but really needs to be develop so he can shake some of his bad habits. Whoever drafts him will have to be patient with his development and use him to his strengths. Another physical freak WR from Georgia Tech, Waller and fellow prospect Deandre Smelter have an excellent shot to join the ranks of Demaryius Thomas and Calvin Johnson as big, physical receivers in the NFL. Wide Receiver U indeed.

Draft Projection: Rounds 4-5