The draft genome of the pear (Pyrus bretschneideri) using a combination of BAC-by-BAC and next-generation sequencing is reported. A 512.0-Mb sequence corresponding to 97.1% of the estimated genome size of this highly heterozygous species is assembled with 1943 coverage. High-density genetic maps comprising 2005 SNP markers anchored 75.5% of the sequence to all 17 chromosomes. The pear genome encodes 42,812 protein-coding genes, and of these,~28.5% encode multiple isoforms. Repetitive sequences of 271.9 Mb in length, accounting for 53.1% of the pear genome, are identified. Simulation of eudicots to the ancestor of Rosaceae has reconstructed nine ancestral chromosomes. Pear and apple diverged from each other~5.4-21.5 million years ago, and a recent whole-genome duplication (WGD) event must have occurred 30-45 MYA prior to their divergence, but following divergence from strawberry. When compared with the apple genome sequence, size differences between the apple and pear genomes are confirmed mainly due to the presence of repetitive sequences predominantly contributed by transposable elements (TEs), while genic regions are similar in both species. Genes critical for self-incompatibility, lignified stone cells (a unique feature of pear fruit), sorbitol metabolism, and volatile compounds of fruit have also been identified. Multiple candidate SFB genes appear as tandem repeats in the S-locus region of pear; while lignin synthesis-related gene family expansion and highly expressed gene families of HCT, C39H, and CCOMT contribute to high accumulation of both G-lignin and S-lignin. Moreover, alpha-linolenic acid metabolism is a key pathway for aroma in pear fruit.

Wolfgang Maret received his diploma in chemistry (1977) and his Ph.D. in natural sciences (1980) from the Saarland University, Saarbruecken, Germany. The mentor for his Ph.D. thesis on metal substitution in liver alcohol dehydrogenase was Prof. Michael Zeppezauer. During his postdoctoral years in Prof. Marvin W. Makinen's laboratory at the University of Chicago (1980-1982), he continued his education in mechanistic enzymology and spectroscopy. He joined Prof. Bert L. Vallee's Center for Biochemical and Biophysical Sciences and Medicine at Harvard Medical School as an assistant professor in 1986 and began to work on the chemical and biochemical mechanisms of cellular zinc homeostasis. Since 2003, Dr. Maret has been an associate professor in the Division of Human Nutrition (Department of Preventive Medicine and Community Health) at the University of Texas Medical Branch in Galveston, TX. His work focuses on molecular mechanisms of cellular metal homeostasis, sulfur redox chemistry, structure and function of metalloenzymes, and functions of micronutrients in chronic and degenerative diseases.

Polycystic ovary syndrome (PCOS) is a common metabolic disorder in women. To identify causative genes, we conducted a genome-wide association study (GWAS) of PCOS in Han Chinese. The discovery set included 744 PCOS cases and 895 controls; subsequent replications involved two independent cohorts (2,840 PCOS cases and 5,012 controls from northern Han Chinese; 498 cases and 780 controls from southern and central Han Chinese). We identified strong evidence of associations between PCOS and three loci: 2p16.3 (rs13405728; combined P-value by meta-analysis P(meta) = 7.55 × 10⁻²¹, odds ratio (OR) 0.71); 2p21 (rs13429458, P(meta) = 1.73 × 10⁻²³, OR 0.67); and 9q33.3 (rs2479106, P(meta) = 8.12 × 10⁻¹⁹, OR 1.34). These findings provide new insight into the pathogenesis of PCOS. Follow-up studies of the candidate genes in these regions are recommended.

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1077-y) contains supplementary material, which is available to authorized users.