Somatic mutation load of estrogen receptor-positive breast tumors predicts overall survival: an analysis of genome sequence data.

Breast cancer is one of the most commonly diagnosed cancers in women. While there are several effective therapies for breast cancer and important single gene prognostic/predictive markers, more than 40,000 women die from this disease every year. The increasing availability of large-scale genomic datasets provides opportunities for identifying factors that influence breast cancer survival in smaller, well-defined subsets. The purpose of this study was to investigate the genomic landscape of various breast cancer subtypes and its potential associations with clinical outcomes. We used statistical analysis of sequence data generated by the Cancer Genome Atlas initiative including somatic mutation load (SML) analysis, Kaplan-Meier survival curves, gene mutational frequency, and mutational enrichment evaluation to study the genomic landscape of breast cancer. We show that ER(+), but not ER(-), tumors with high SML associate with poor overall survival (HR = 2.02). Further, these high mutation load tumors are enriched for coincident mutations in both DNA damage repair and ER signature genes. While it is known that somatic mutations in specific genes affect breast cancer survival, this study is the first to identify that SML may constitute an important global signature for a subset of ER(+) tumors prone to high mortality. Moreover, although somatic mutations in individual DNA damage genes affect clinical outcome, our results indicate that coincident mutations in DNA damage response and signature ER genes may prove more informative for ER(+) breast cancer survival. Next generation sequencing may prove an essential tool for identifying pathways underlying poor outcomes and for tailoring therapeutic strategies

DnaSP v5: A software for comprehensive analysis of DNA polymorphism data

Podeu consultar el programari a: http://hdl.handle.net/2445/53451DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser

Use of partial least squares regression to impute SNP genotypes in Italian Cattle breeds

Background The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. Methods Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. Results In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. Conclusions Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available

Attitudes of parents and teachers toward the integration of severely and profoundly handicapped students.

This study investigated the attitudes of teachers and parents toward the integration of severely and profoundly handicapped students. A five group, single observation study was conducted to determine if teachers and parents with varying amounts of contact with handicapped people would have significantly different attitudes toward the handicapped population. The parents chosen for this study had children who attended one of two schools within a middle sized city in the Midwest. The teacher chosen were assigned to these two facilities. One facility was integrated and had severely and profoundly handicapped students enrolled. The second facility chosen for this study had special education students, however, their handicaps were not obvious and for the purposes of this study was considered a nonintegrated facility. The sample included 33 regular education teachers and 126 parents. The Attitudes Toward Disabled Persons Scale-Form A was the instrument utilized to assess the attitudes of the different groups. In addition, a demographic information sheet was also developed and used to collect data. One hundred ninety five surveys were sent out with a return rate of 82% (N 159). All returned surveys were utilized due to the small sample size of each group. Analysis of variance was the statistical method used to analyze the data. Results indicated significantly more positive attitudes in the teachers assigned to the nonintegrated facility than the teachers assigned to the integrated facility

Linkage Disequilibrium-Based Quality Control for Large-Scale Genetic Studies

Quality control (QC) is a critical step in large-scale studies of genetic variation. While, on average, high-throughput single nucleotide polymorphism (SNP) genotyping assays are now very accurate, the errors that remain tend to cluster into a small percentage of “problem” SNPs, which exhibit unusually high error rates. Because most large-scale studies of genetic variation are searching for phenomena that are rare (e.g., SNPs associated with a phenotype), even this small percentage of problem SNPs can cause important practical problems. Here we describe and illustrate how patterns of linkage disequilibrium (LD) can be used to improve QC in large-scale, population-based studies. This approach has the advantage over existing filters (e.g., HWE or call rate) that it can actually reduce genotyping error rates by automatically correcting some genotyping errors. Applying this LD-based QC procedure to data from The International HapMap Project, we identify over 1,500 SNPs that likely have high error rates in the CHB and JPT samples and estimate corrected genotypes. Our method is implemented in the software package fastPHASE, available from the Stephens Lab website (http://stephenslab.uchicago.edu/software.html).</p

Complex patterns of local adaptation in teosinte

Populations of widely distributed species often encounter and adapt to specific environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome-wide genotype data, multiple sampled populations, and a good understanding of population structure. We have used environmental and high-density genotype data to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found that altitude, dispersal events and admixture among subspecies formed a complex hierarchical genetic structure within teosinte. Patterns of linkage disequilibrium revealed four mega-base scale inversions that segregated among populations and had altitudinal clines. Based on patterns of differentiation and correlation with environmental variation, inversions and nongenic regions play an important role in local adaptation of teosinte. Further, we note that strongly differentiated individual populations can bias the identification of adaptive loci. The role of inversions in local adaptation has been predicted by theory and requires attention as genome-wide data become available for additional plant species. These results also suggest a potentially important role for noncoding variation, especially in large plant genomes in which the gene space represents a fraction of the entire genome

Introgression and the fate of domesticated genes in a wild mammal population

When domesticated species are not reproductively isolated from their wild relatives, the opportunity arises for artificially selected variants to be re-introduced into the wild. However, the evolutionary consequences of introgression of domesticated genes back into the wild are poorly understood. By combining high-throughput genotyping with 25 years of long-term ecological field data, we describe the occurrence and consequences of admixture between a primitive sheep breed, the free-living Soay sheep of St Kilda, and more modern breeds. Utilizing data from a 50 K ovine SNP chip, together with forward simulations of demographic scenarios, we show that admixture occurred between Soay sheep and a more modern breed, consistent with historical accounts, approximately 150 years ago. Haplotype-sharing analyses with other breeds revealed that polymorphisms in coat colour and pattern in Soay sheep arose as a result of introgression of genetic variants favoured by artificial selection. Because the haplotypes carrying the causative mutations are known to be under natural selection in free-living Soay sheep, the admixture event created an opportunity to observe the outcome of a 'natural laboratory' experiment where ancestral and domesticated genes competed with each other. The haplotype carrying the domesticated light coat colour allele was favoured by natural selection, while the haplotype associated with the domesticated self coat pattern allele was associated with decreased survival. Therefore, we demonstrate that introgression of domesticated alleles into wild populations can provide a novel source of variation capable of generating rapid evolutionary changes

Scans for signatures of selection in Russian cattle breed genomes reveal new candidate genes for environmental adaptation and acclimation

Domestication and selective breeding has resulted in over 1000 extant cattle breeds. Many of these breeds do not excel in important traits but are adapted to local environments. These adaptations are a valuable source of genetic material for efforts to improve commercial breeds. As a step toward this goal we identified candidate regions to be under selection in genomes of nine Russian native cattle breeds adapted to survive in harsh climates. After comparing our data to other breeds of European and Asian origins we found known and novel candidate genes that could potentially be related to domestication, economically important traits and environmental adaptations in cattle. The Russian cattle breed genomes contained regions under putative selection with genes that may be related to adaptations to harsh environments (e.g., AQP5, RAD50, and RETREG1). We found genomic signatures of selective sweeps near key genes related to economically important traits, such as the milk production (e.g., DGAT1, ABCG2), growth (e.g., XKR4), and reproduction (e.g., CSF2). Our data point to candidate genes which should be included in future studies attempting to identify genes to improve the extant breeds and facilitate generation of commercial breeds that fit better into the environments of Russia and other countries with similar climates

HapTree: A Novel Bayesian Framework for Single Individual Polyplotyping Using NGS Data

As the more recent next-generation sequencing (NGS) technologies provide longer read sequences, the use of sequencing datasets for complete haplotype phasing is fast becoming a reality, allowing haplotype reconstruction of a single sequenced genome. Nearly all previous haplotype reconstruction studies have focused on diploid genomes and are rarely scalable to genomes with higher ploidy. Yet computational investigations into polyploid genomes carry great importance, impacting plant, yeast and fish genomics, as well as the studies of the evolution of modern-day eukaryotes and (epi)genetic interactions between copies of genes. In this paper, we describe a novel maximum-likelihood estimation framework, HapTree, for polyploid haplotype assembly of an individual genome using NGS read datasets. We evaluate the performance of HapTree on simulated polyploid sequencing read data modeled after Illumina sequencing technologies. For triploid and higher ploidy genomes, we demonstrate that HapTree substantially improves haplotype assembly accuracy and efficiency over the state-of-the-art; moreover, HapTree is the first scalable polyplotyping method for higher ploidy. As a proof of concept, we also test our method on real sequencing data from NA12878 (1000 Genomes Project) and evaluate the quality of assembled haplotypes with respect to trio-based diplotype annotation as the ground truth. The results indicate that HapTree significantly improves the switch accuracy within phased haplotype blocks as compared to existing haplotype assembly methods, while producing comparable minimum error correction (MEC) values. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.National Science Foundation (U.S.) (NSF/NIH BIGDATA Grant R01GM108348-01)National Science Foundation (U.S.) (Graduate Research Fellowship)Simons Foundatio

Somatic Mutations in Normal Tissues: Calm before the Storm

We explore the phenomenon of somatic mutations, including those in cancer driver genes, that are present in healthy, normal-appearing tissues and their potential implications for cancer development. We also examine the landscape of these somatic mutations, discuss the role of clonal cell competition and external factors like inflammation in enhancing the fitness of mutant clones, and conclude by considering how understanding these mutations will aid in prevention and/or interception of cancer