Among these arrays, 2 to 127 samples were genotyped on at least two different arrays with pairwise concordance rates all >99.18%. A set of 47 000 variants genotyped on all arrays and meeting the following four criteria: common , independent , high quality , were used to assess duplicate samples included on multiple arrays and also to confirm the reported pedigree structure. Family structures were altered as needed, and genotypes were checked for Mendelian inconsistencies using PedCheck33 with the revised family structure. Genotype inconsistencies were set to missing. The same set of 47 000 variants was also employed to calculate principal components using Eigenstrat34 and 1000 Genomes . Based on the first two PCs, each individual was then assigned a race classification . To maximize the value of the multiplex family recruitment strategy of COGA, family-based analyses were performed. Families were assigned a family-based race, according to the majority of individual based race in that family. All samples were imputed to 1000 Genomes using the cosmopolitan reference panel using SHAPEIT235 then Minimac336 within each array. Only variants with non-A/T or C/G alleles, missing rates <5%, MAF >3%, and HWE Pvalues >.0001 were used for imputation. Imputed variants with R2 < .30 were excluded, and genotype probabilities were converted to genotypes if probabilities ≥.90. PedCheck33 was used again to detect and clean Mendelian in consistences for imputed variants. All genotyped and imputed variants with missing rates <25%, MAF ≥1% and HWE P-values >1E−6 were included in analyses. 8 021 023 and 6 832 792 genotyped and imputed variants passed QC and were included in COGA EA and trans-ancestral meta-analysis, respectively.
Discovery GWAS were focused on the EA sub-sample and a transancestral meta-analysis of GWAS summary statistics from the COGA AA and EA sub-samples . Even though a GWAS was conducted in the AA sub-sample,cannabis growing system results were only used in the trans-ancestral meta-analysis. Due to the strict definition of AD controls, the individual AA sub-sample was too small for use as a discovery sample . For binary traits, association analysis was performed using a generalized estimating equation framework to control for relatedness with each family treated as a cluster. For the criterion count measure, a linear mixed effects model was fit to continuously distributed data with family relationship adjusted through a kinship matrix. The R package GWAF was used to test both models. Birth cohort was a stronger predictor of AD than was age , and hence was selected along with sex, GWAS array indicator, and the first four ancestral PCs as covariates in the model. In GWS regions, conditional analyses were performed by including the most significant variant in the region as a covariate to evaluate whether a single locus explained the association signal. The transancestral meta-analysis was performed using inversevariance weighting in METAL.As implemented in METAL, genomic control, which was estimated by comparing the median test statistics to those expected by chance alone, was applied to the GWAS of COGA AA and COGA EA. For the trans-ancestral meta-analysis , genomic control was applied to the standard errors of the effect sizes. All genomic control estimations were implemented in METAL. Only GWS variants were evaluated in replication samples. As we tested seven individual criteria for the tertiaryanalyses, a matrix of the phenotypic correlations between these criteria in the EA participants was spectrally decomposed using matSpD resulting in three effectively independent tests and thus a revised GWS P-value threshold of 1.67E−8 was used for the tertiary analyses.
The COGA Prospective Sample includes offspring aged 12 to 34 years from COGA families, and was designed to assess multiple domains , at 2-year intervals.Neurophysiological analyses of reward-related theta ERO data from the most recent assessments were carried out in a subsample of 825 COGA AA and 1726 COGA EA young adults . A monetary gambling task was implemented as detailed elsewhere.46 Briefly, individuals bet 50¢ or 10¢ in each of 172 trials, with one of four possible outcomes: lose 50¢, lose 10¢, gain 50¢ or gain 10¢, with equal number of loss and gain trials . Evoked theta ERO power during monetary loss and gain feedback were measured and differential reward processing was derived at frontal, central and parietal regions . Linear regression was applied to test the associations between the top variants and theta ERO power after adjusting for sex, age and first three PCs. We did not examine rs1229984 in ADH1B in either the COGA Prospective Sample or the DNS due to its well-known role in the alcohol metabolizing process. For the remaining four GWS loci , three brain regions were tested; therefore, after multiple testing correction, the significance threshold was P ≤ .0042 . Further details on data acquisition and processing are given in Data S1.Table 1 and Table 2 summarize the samples used in discovery and replication analyses. There were 7418 EA and 3175 AA individuals, respectively. In total, there were 18 586 individuals evaluated for DSM-IV AD in both discovery and replication samples, with 7482 AD cases and 6169 controls. As shown in Table S1, the primary, secondary and tertiary phenotypes were highly correlated with each other in both EAs and AAs, with DSM-IV AD and DSM-IV AD criterion count having the highest correlations with each individual criterion in both AA and EA sub-samples . As shown in Table S2, the item response analysis demonstrated that all criteria loaded well on a single underlying AD factor.
Some criteria discriminated liability at the lower end of the liability distribution while others contributed at the higher end of the severity continuum .Regions on chromosomes 1, 2, 4, 8 and 15 reached GWS for primary, secondary and tertiary phenotypes in EA and EA + AA GWAS, respectively . All genomic controls are listed in Table S4. Primary phenotype : In EA, no GWS findings were identified. In the trans-ancestral meta-analysis , consistent with prior GWAS, rs1229984 in ADH1B was significantly associated with AD . In addition, a novel GWS locus was also identified on chromosome 1 in the EA + AA analysis. Both the EA and AA sub-samples contributed to the finding, with the same direction of effect. Conditional analyses confirmed that there were independent associations in the ADH1B region but not in the chromosome 1 region . Secondary phenotype : rs1229984 in ADH1B was associated at GWS levels in the EA and the EA + AA analysis. Tertiary phenotypes : In EA, rs1229984 was associated with Desire to cut drinking . Two novel regions were GWS for two individual DSM-IV criteria: rs188227250 on chromosome 8 for Drinking more than intended ; rs1912461 on chromosome 15 for Time spent drinking . For thetrans-ancestral analysis,hydroponic racks system rs1229984 was significantly associated with Desire to cut drinking and Tolerance . An additional GWS region on chromosome 2 was noted for Time spent drinking. The regions on chromosomes 2, 4 and 8 survived the more stringent correction for the seven criteria while the chromosome 15 variant was GWS but did not survive the additional correction for multiple testing of individual criteria . Conditional analyses demonstrated that there was only one association signal in the chromosome 15 region; however, the possibility of a second independent signal in the chromosome 8 region could not be ruled out .This large, family study of AA and EA individuals utilized a multi-pronged approach to dissect the genetic underpinnings of AD . In addition to the primary phenotype of DSM-IV diagnosis of AD, and severity as captured by the AD criterion count, it is, to our knowledge, the largest GWAS of each DSM-IV AD criterion. We detected five regions with variants meeting traditional GWS criteria, of which four were novel . Notably, the chromosome 8 signal was replicated in an independent dataset, as was the well-known association with rs1229984 in ADH1B. Even when excluding the larger effect size associated with rs1229984, PRS derived from the EA GWAS predicted 0.61% to 1.82% of the variation in AD in independent datasets, underscoring significant polygenicity underlying liability to the disorder. Analyses of two reward-related neural phenotypes also showed associations with two GWS variants. Consistent with several prior studies,6 rs1229984 in ADH1B was associated with DSM-IV AD. Although GWS was only noted in the trans-ancestral analysis, as shown in Table S7, rs1229984 was associated with the AD criterion count and criteria indexing physiological dependence and Desire to cut drinking at GWS levels, and with other AD criteria at nominal levels of significance.
Despite the robust relationship between this functional variant and AD, its relatively low minor allele frequency necessitates fairly large samples to detect a GWS effect for a binary trait, as was shown in a recent metaanalysis of DSM-IV AD.6 However, for DSM-IV AD criterion count, rs1229984 was GWS in both the EA and EA + AA analyses. Similar to another study,16 we found that while rs1229984 was associated with each individual criterion , the association was stronger with certain DSM-IV AD criteria. Consistent with Hart et al, Tolerance was strongly associated with rs1229984 . However, the additional GWS associations with Desire to cut drinking in our study differs from the prior study which used a sequential regression approach to identify Withdrawal and Drinking more than intended as additional criteria related to rs1229984 in EA, and Time spent drinking in AA. However, another study of 1130 individuals of Jewish descent reported associations between rs1229984 and both Tolerance and Desire to cut drinking.Across these studies, the most robust association signal for rs1229984 appears to arise from Tolerance, which is notably an index of excessive consumption and consistent with the role of ADH1B in other studies of nonproblem alcohol intake.Plausibly, the strong findings with Desire to cut drinking might also support this as epidemiological studies have shown this criterion to index liability to less severe AD , and therefore, serve as a marker of excessive drinking, rather than severe pathology and impairment.Differences in associations with other criteria could stem from the relative severity of individual criteria in each dataset or their relationship with excessive drinking. The GWS findings for the other loci are novel and have not been previously reported for AD or related phenotypes, although these regions have been linked to some neuropsychiatric diseases/traits. The region on chromosome 1 was previously linked to cerebrospinal fluid biomarker level,migraine,illegal substance dependence,and neuroticism.This region encompasses gene RABGAP1L, with many other genes nearby . RABGAP1L is broadly expressed in brain regions and showed association with cerebrospinal fluid biomarker levels and migraine.Other genes near this region seem interesting too, for example, KIAA0040, which is downstream of this region, was associated with alcohol dependence.The chromosome 2 region is in a gene desert and has been linked to cognitive test scores,attention-deficit/hyperactivity disorder symptom count,ADHD,current smoking and juvenile myoclonic epilepsy.The region on chromosome 8 has been linked to bipolar disorder.The only gene near the chromosome 8 region is FAM84B , however, this gene does not seem to be related to any neuropsychiatric diseases. The chromosome 15 region harbors some noncoding RNAs and was previously linked to the rate of cognitive decline,ADHD and major depression.Thus, despite our discovery of novel loci, much further study is needed to investigate the role of these variants in the etiology of AD and related traits. In our data, the chromosome 1 variant showed nominal association with multiple AD criteria and the criterion count, but none at GWS levels. However, a highly correlated variant was associated at GWS with a phenotype representing dependence on alcohol or illicit drugs in the same sample . It is possible that this variant is associated with overall liability to AD and dependence on other drugs but to a lesser extent with AD severity as indexed by a single continuous criterion count. Research has noted that mere summation does not capture the heterogeneity underlying AD severity, where constellations of criteria could result in meaningful individual differences.10 Prior latent class analyses aimed to parse out such groups of individuals with unique sets of criteria including in a subset of these data.9 However, assessment of the genomic underpinnings of such heterogeneous groups of individuals would require extremely large sample sizes. The chromosome 8 variant, rs188227250, was uniquely associated with Drinking more than intended .