The challenges of genome-wide interaction studies: Lessons to learn from the analysis of HDL blood levels

Genome-wide association studies (GWAS) have revealed 74 single nucleotide polymorphisms (SNPs) associated with high-density lipoprotein cholesterol (HDL) blood levels. This study is, to our knowledge, the first genome-wide interaction study (GWIS) to identify SNP6SNP interactions associated with HDL levels. We performed a GWIS in the Rotterdam Study (RS) cohort I (RS-I) using the GLIDE tool which leverages the massively parallel computing power of Graphics Processing Units (GPUs) to perform linear regression on all genome-wide pairs of SNPs. By performing a meta-analysis together with Rotterdam Study cohorts II and III (RS-II and RS-III), we were able to filter 181 interaction terms with a p-value, 1 · 1028 that replicated in the two independent cohorts. We were not able to replicate any of these interaction term in the AGES, ARIC, CHS, ERF, FHS and NFBC-66 cohorts (Ntotal = 30, 011) when adjusting for multiple testing. Our GWIS resulted in the consistent finding of a possible interaction between rs774801 in ARMC8 (ENSG00000114098) and rs12442098 in SPATA8 (ENSG00000185594) being associated with HDL levels. However, p-values do not reach the preset Bonferroni correction of the p-values. Our study suggest that even for highly genetically determined traits such as HDL the sample sizes needed to detect SNP6SNP interactions are large and the 2-step filtering approaches do not yield a solution. Here we present our analysis plan and our reservations concerning GWIS

GWAS for executive function and processing speed suggests involvement of the CADM2 gene

To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of executive functioning and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5429-32 070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1311-21860 subjects from 20 independent cohorts. A significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery P-value=3.12 × 10(-8)) and in the joint discovery and replication meta-analysis (P-value=3.28 × 10(-9) after adjustment for age, gender and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (P-value=4 × 10(-4)). The protein encoded by CADM2 is involved in glutamate signaling (P-value=7.22 × 10(-15)), gamma-aminobutyric acid (GABA) transport (P-value=1.36 × 10(-11)) and neuron cell-cell adhesion (P-value=1.48 × 10(-13)). Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed.Molecular Psychiatry advance online publication, 14 April 2015; doi:10.1038/mp.2015.37

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

Genetic Differences in the Immediate Transcriptome Response to Stress Predict Risk-Related Brain Function and Psychiatric Disorders

Depression risk is exacerbated by genetic factors and stress exposure; however, the biological mechanisms through which these factors interact to confer depression risk are poorly understood. One putative biological mechanism implicates variability in the ability of cortisol, released in response to stress, to trigger a cascade of adaptive genomic and non-genomic processes through glucocorticoid receptor (GR) activation. Here, we demonstrate that common genetic variants in long-range enhancer elements modulate the immediate transcriptional response to GR activation in human blood cells. These functional genetic variants increase risk for depression and co-heritable psychiatric disorders. Moreover, these risk variants are associated with inappropriate amygdala reactivity, a transdiagnostic psychiatric endophenotype and an important stress hormone response trigger. Network modeling and animal experiments suggest that these genetic differences in GR-induced transcriptional activation may mediate the risk for depression and other psychiatric disorders by altering a network of functionally related stress-sensitive genes in blood and brain

Prevalence of congenital malformations observed in neonates in Shariati Hospital (1381-1383)

Background: Congenital malformations are one of the most important problems in pediatrics. The estimation of the prevalence of malformations and some probable determinants were the purpose of this study. Methods: In this retrospective study, all of the newborns that were born during three years (2002-4) were included. Hospital files of 3840 newborns were studied retrospectively and the data were collected in checklist. Finding: 118 cases had at least a major or minor malformation. Over all the prevalence of malformations was 3.1%. Male newborns showed a higher prevalence of malformations than females but with no statistical significance. The skeletal system had the highest rate of malformations, while the genitourinary system and the head and neck deformities were in the second and third position. There were no significant relations between the prevalence of malformations and the maternal age, the height and weight of the newborns and the season of birth. Conclusion: The prevalence of malformations in this study was similar to previous studies

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs

Motivation: In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene–gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested

Effectiveness of t-PA in acute ischemic stroke: outcome relates to appropriateness.

OBJECTIVE: To examine whether the demonstrated efficacy of tissue-type plasminogen activator (t-PA) for acute ischemic stroke can be effective in a community setting. METHODS: Sixty-eight consecutive patients with acute ischemic stroke treated with IV t-PA within 3 hours of symptom onset by attending general neurologists in a busy teaching hospital. Outcome measures at 3 months were the National Institute of Health Stroke Scale (NIHSS), functional outcome (independence [modified Rankin score 0-2], dependence [modified Rankin score 3-5], and death), and symptomatic hemorrhage. Appropriately treated patients were defined by adherence to the National Institute of Neurological Disorders and Stroke (NINDS) guidelines. Effectiveness is expressed as the absolute risk reduction in which the baseline risk is assumed to be similar to that of the NINDS control group. RESULTS: Of 68 consecutively treated patients (with a mean baseline NIHSS score of 15 +/- 6), 26 (38%) made a full recovery and 39 (57%) made an independent recovery. The 11 patients who violated protocol had a lower probability of independence (p < 0.02) and full neurologic recovery (p < 0.02) and a higher probability of symptomatic hemorrhage (p < 0.05) and death (p < 0.01) compared with those of 57 patients treated according to NINDS guidelines. CONCLUSIONS: The use of t-PA for stroke in this community is effective with a number needed to treat of six. The risk of symptomatic hemorrhage is similar to that noted in randomized trials. Treating patients who violate protocol results in excess risk with no observable benefit

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year's time to complete the same task