Lack of association of common polymorphisms linked to empathic behavior with self-reported trait empathy in healthy volunteers.

BACKGROUND: In a previously specified sample of 421 healthy subjects, we found associations of a common oxytocin receptor (OXTR) polymorphism with self-reported trait empathy. In this study, we used this sample to explore polymorphisms in other genes which have been frequently linked to empathic behavior for associations with self-reported trait empathy: CD38 (CD38), involved in oxytocin secretion, the serotonin transporter (SLC6A4), the Catechol-O-Methyltransferase (COMT) and the corticotropin releasing hormone receptor 1 (CRHR1). METHODS: We genotyped our sample for the following common polymorphisms: rs3796863 in the CD38 gene, 5-HTTLPR in the SLC6A4 gene, rs4680 in the COMT gene and rs242924 in the CRHR1 gene. Dispositional empathy was tested using Davis' Interpersonal Reactivity Index (IRI). We used a Bonferroni corrected alpha level of p = 0.002 to adjust for multiple comparisons. RESULTS: None of the genotypes were associated with any of the IRI scales for the complete sample (n = 421) or for the sub-groups of male (n = 213) and female (n = 190) participants. Our sample of 421 participants achieved 95% power to detect effects greater than r = ±0.18. For smaller effects, however, false negatives could not be rejected with equal confidence as false positives. CONCLUSIONS: We conclude that an association between the four polymorphisms with trait empathy measured by the IRI may not be present. We propose that the associations that have been found in other studies can be largely explained by differences in empathy-related constructs and measurements.

ACGH detects distinct genomic alterations of primary intrahepatic cholangiocarcinomas and matched lymph node metastases and identifies a poor prognosis subclass.

Lymph node metastases (LNM) are an important prognostic factor for patients with intrahepatic cholangiocarcinoma, but underlying genetic alterations are poorly understood. Whole genome array comparative genomic hybridization (aCGH) was performed in 37 tumors and 14 matched LNM. Genomic analyses of tumors confirmed known and identified new (gains in 19q) copy number alterations (CNA). Tumors with LNM (N1) had more alterations and exclusive gains (3p, 4q, 5p, 13q) and losses (17p and 20p). LNM shared most alterations with their matched tumors (86%), but 79% acquired new isolated gains [12q14 (36%); 1p13, 2p23, 7p22, 7q11, 11q12, 13q13 and 14q12 (>20%)]. Unsupervised clustering revealed a poor prognosis subclass with increased alterations significantly associated to tumor differentiation and survival. TP53 and KRAS mutations occurred in 19% of tumors and 6% of metastases. Pathway analyses revealed association to cancer-associated pathways. Advanced tumor stage, microvascular/perineural invasion, and microscopic positive resection margin (R1) were significantly correlated to metastases, while N1-status, R1-resection, and poor tumor differentiation were significantly correlated to survival. ACGH identified clear differences between N0 (no LNM) and N1 tumors, while N1 tumors and matched LNM displayed high clonality with exclusive gains in the metastases. A novel subclass with increased CNAs and poor tumor differentiation was significantly correlated to survival.

A new workflow for whole-genome sequencing of single human cells.

Unbiased amplification of the whole-genome amplification (WGA) of single cells is crucial to study cancer evolution and genetic heterogeneity, but is challenging due to the high complexity of the human genome. Here, we present a new workflow combining an efficient adapter-linker PCR-based WGA method with second-generation sequencing. This approach allows comparison of single cells at base pair resolution. Amplification recovered up to 74% of the human genome. Copy-number variants and loss of heterozygosity detected in single cell genomes showed concordance of up to 99% to pooled genomic DNA. Allele frequencies of mutations could be determined accurately due to an allele dropout rate of only 2%, clearly demonstrating the low bias of our PCR-based WGA approach. Sequencing with paired-end reads allowed genome-wide analysis of structural variants. By direct comparison to other WGA methods, we further endorse its suitability to analyze genetic heterogeneity.