Comprehensive Gene-Based Association Study of a Chromosome 20 Linked Region Implicates Novel Risk Loci for Depressive Symptoms in Psychotic Illness

Background Prior genomewide scans of schizophrenia support evidence of linkage to regions of chromosome 20. However, association analyses have yet to provide support for any etiologically relevant variants. Methods We analyzed 2988 LD-tagging single nucleotide polymorphisms (SNPs) in 327 genes on chromosome 20, to test for association with schizophrenia in 270 Irish high-density families (ISHDSF, N = 270 families, 1408 subjects). These SNPs were genotyped using an Illumina iSelect genotyping array which employs the Infinium assay. Given a previous report of novel linkage with chromosome 20p using latent classes of psychotic illness in this sample, association analysis was also conducted for each of five factor-derived scores based on the Operational Criteria Checklist for Psychotic Illness (delusions, hallucinations, mania, depression, and negative symptoms). Tests of association were conducted using the PDTPHASE and QPDTPHASE packages of UNPHASED. Empirical estimates of gene-wise significance were obtained by adaptive permutation of a) the smallest observed P-value and b) the threshold-truncated product of P-values for each locus. Results While no single variant was significant after LD-corrected Bonferroni-correction, our gene-dropping analyses identified loci which exceeded empirical significance criteria for both gene-based tests. Namely, R3HDML and C20orf39 are significantly associated with depressive symptoms of schizophrenia (PempP-value and truncated-product methods, respectively. Conclusions Using a gene-based approach to family-based association, R3HDML and C20orf39 were found to be significantly associated with clinical dimensions of schizophrenia. These findings demonstrate the efficacy of gene-based analysis and support previous evidence that chromosome 20 may harbor schizophrenia susceptibility or modifier loci

Analyst information precision and small earnings surprises

This study proposes and tests an alternative to the extant earnings management explanation for zero and small positive earnings surprises (i.e., analyst forecast errors). We argue that analysts’ ability to strategically induce slight pessimism in earnings forecasts varies with the precision of their information. Accordingly, we predict that the probability that a firm reports a small positive instead of a small negative earnings surprise is negatively related to earnings forecast uncertainty, and we present evidence consistent with this prediction. Our findings have important implications for the earnings management interpretation of the asymmetry around zero in the frequency distribution of earnings surprises. We demonstrate how empirically controlling for earnings forecast uncertainty can materially change inferences in studies that employ the incidence of zero and small positive earnings surprises to categorize firms as suspected of managing earnings