Convergence of Y Chromosome STR Haplotypes from Different SNP Haplogroups Compromises Accuracy of Haplogroup Prediction

The paternally inherited Y chromosome has been widely used in forensics for personal identification, in anthropology and population genetics to understand origin and migration of human populations, and also in medical and clinical studies （Wang and Li, 2013; Wang et al., 2014）. There are two kinds of extremely useful markers in Y chromosome, single nucle- otide polymorphism （SNP） and short tandem repeats （STRs）. With a very low mutation rate on the order of 3.0 x 10-8 mutations/nucleotide/generation （Xue et al., 2009）, SNP markers have been used in constructing a robust phylogeny tree linking all the Y chromosome lineages from world pop- ulations （Karafet et al., 2008）. Those lineages determined by the pattern of SNPs are called haplogroups. That is to say, we have to genotype an appropriate number of SNPs in order to assign a given Y chromosome to a haplogroup. Compared with SNPs, the mutation rates of STR markers are about four to five orders of magnitude higher （Gusmgo et al., 2005; Ballantyne et al., 2010）. Typing STR has advantages of saving time and cost compared with typing SNPs in phylogenetic assignment of a Y chromosome （Wang et al., 2010）. A set of STR values for an individual is called a haplotype. Because of the disparity in mutation rates between SNP and STR, one SNP haplogroup could actually comprise many STR haplotypes （Wang et al., 2010）. It is most interesting that STR variability is clustered more by haplogroups than by populations （Bosch et al., 1999; Behar et al., 2004）, which indicates that STR haplotypes could be used to infer the haplogroup information of a given Y chromosome. There has been increasing interest in this cost- effective strategy for predicting the haplogroup from a given STR haplotype when SNP data are unavailable. For instance, Vadim Urasin＇s YPredictor （http：//predictor.ydna.ru/）, Whit Atheys＇ haplogroup predictor （http：//www.hprg.com/hapest5/） （Athey, 2005, 2006）, and haplogroup classifier of Arizona University （Schlecht et al., 2008） have been widely employed in previous studies for haplogroup prediction （Larmuseau et al., 2010; Bembea et al., 2011; Larmuseau et al., 2012; Tarlykov et al., 2013）.

Nuclear proteome profile of C57BL/6J mouse liver

The liver proteome can serve as a reference to better understand both disease mechanisms and possible therapeutics,since the liver is an important organ in the body that performs a large number of tasks.Here we identify the organelle proteome of C57BL/6J mouse liver nuclei as a promising strategy to enrich low abundance proteins,in the sense that analysis of whole liver cells is rather complex for current techniques and may not be suitable for proteins with low abundance.Evaluation of nucleus integrity and purity was performed to demonstrate the effectiveness of the optimized isolation procedure.The extracted nuclear proteins were identified by 2-DE MS analyses,and a total of 748 proteins were identified.Bioinformatic analyses were performed to demonstrate the physicochemical properties,cellular locations and functions of the proteins.

Multiple functional linear model for association analysis of RNA-seq with imaging

Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at the genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimensions （2DFPCA） for representing imaging data and develop a multiple functional linear model （MFLM） in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA- seq data of ovarian cancer and kidney renal clear cell carcinoma （KIRC） studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoforms of gene expressions.

Follicle stimulating hormone controls granulosa cell glutamine synthesis to regulate ovulation

Polycystic ovary syndrome(PCOS)is the leading cause of anovulatory infertility.Inadequate understanding of the ovulation drivers hinders PCOS intervention.Herein,we report that follicle stimulating hormone(FSH)controls follicular fluid(FF)glutamine levels to determine ovulation.Murine ovulation starts from FF-exposing granulosa cell(GC)apoptosis.FF glutamine,which decreases in pre-ovulation porcine FF,elevates in PCOS patients FF.High-glutamine chow to elevate FF glutamine inhibits mouse GC apoptosis and induces hormonal,metabolic,and morphologic PCOS traits.Mechanistically,follicle-development-driving FSH promotes GC glutamine synthesis to elevate FF glutamine,which maintain follicle wall integrity by inhibiting GC apoptosis through inactivating ASK1-JNK apoptotic pathway.FSH and glutamine inhibit the rapture of cultured murine follicles.Glutamine removal or ASK1-JNK pathway activation with metformin or AT-101 reversed PCOS traits in PCOS models that are induced with either glutamine or EsR1-KO.These suggest that glutamine,FSH,and ASK1-JNK pathway are targetable to alleviate PCOS.

An improved trust region method for unconstrained optimization

In this paper,we propose an improved trust region method for solving unconstrained optimization problems.Different with traditional trust region methods,our algorithm does not resolve the subproblem within the trust region centered at the current iteration point,but within an improved one centered at some point located in the direction of the negative gradient,while the current iteration point is on the boundary set.We prove the global convergence properties of the new improved trust region algorithm and give the computational results which demonstrate the effectiveness of our algorithm.