Andrew Schork | bioRxiv Science

bioRxiv2016

Williams Syndrome-Specific Neuroarchitectural Profile and Its Associations with Cognitive Features

Williams Syndrome (WS), a rare genetic disorders caused by hemizyous deletion of ~26 genes on the chromosome 7, has unique cognitive features and neuroanatomic abnormalities. Limited in statistical power due to its rareness had led to inconsistent in many direct comparisons using structural magnetic resonance imaging (MRI), and their associations with cognitive features of WS are not clear. Here, we used a novel approach to derive a WS specific neuroarchitectural profile and tested its association with cognitive features of WS. Using a WS adult cohort (n = 43), we trained a logistic elastic-net model to extract a sparse representation of WS specific neuroarchitectural profile. The predictive performances are robust within the training cohort (leave one out cross-validation AUC = 1.0) and generalized well in an independent teenager WS cohort (n = 60, AUC = 1.0). The WS specific neuroarchitectural profile includes multiple MRI measurements in the orbitofrontal cortex, superior parietal cortex, Sylvian fissures, and basal ganglia, whereas its variations reflect the underlying size of hemizygous deletion, and mediated the disease impact on the cognitive features of WS. In this study, we demonstrate the robustness of the derived WS specific neuroarchitectural profile, suggesting the joint developmental abnormalities in the cortical-subcortical circuitry cause the unique features of WS cognition.

Neuroscience↗

bioRxiv2015

Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics

Genome-wide Association Studies (GWAS) result in millions of summary statistics (\"z-scores\") for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities that does not require raw genotype data, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype - the proportion of SNPs (after uniform pruning, so that large LD blocks are not over-represented) likely to be in strong LD with causal/mechanistically associated SNPs - and predicting the proportion of chip heritability explainable by genome-wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N=82,315) and additionally, for purposes of illustration, putamen volume (N=12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We estimate the degree to which effect sizes are over-estimated when based on linear-regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 106 and 105. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

Genetics↗