Bingshan Li

Transcriptome Analysis of Psoriasis in a Large Case–Control Sample: RNA-Seq Provides Insights into Disease Mechanisms

To increase our understanding of psoriasis, we utilized RNA-seq to assay the transcriptomes of lesional psoriatic and normal skin. We sequenced polyadenylated RNA-derived cDNAs from 92 psoriatic and 82 normal punch biopsies, generating an average of ~38 million single-end 80-bp reads per sample. Comparison of 42 samples examined by both RNA-seq and microarray revealed marked differences in sensitivity, with transcripts identified only by RNA-seq having much lower expression than those also identified by microarray. RNA-seq identified many more differentially expressed transcripts enriched in immune system processes. Weighted gene co-expression network analysis (WGCNA) revealed multiple modules of coordinately expressed epidermal differentiation genes, overlapping significantly with genes regulated by the long non-coding RNA TINCR, its target gene, staufen-1 (STAU1), the p63 target gene ZNF750, and its target KLF4. Other coordinately expressed modules were enriched for lymphoid and/or myeloid signature transcripts and genes induced by IL-17 in keratinocytes. Dermally-expressed genes were significantly down-regulated in psoriatic biopsies, most likely due to expansion of the epidermal compartment. These results demonstrate the power of WGCNA to elucidate gene regulatory circuits in psoriasis, and emphasize the influence of tissue architecture in both differential expression and co-expression analysis.

Large-scale genetic study in East Asians identifies six new loci associated with colorectal cancer risk

Known genetic loci explain only a small proportion of the familial relative risk of colorectal cancer (CRC). We conducted a genome-wide association study of CRC in East Asians with 14,963 cases and 31,945 controls and identified 6 new loci associated with CRC risk (P = 3.42 × 10−8 to 9.22 × 10−21) at 10q22.3, 10q25.2, 11q12.2, 12p13.31, 17p13.3 and 19q13.2. Two of these loci map to genes (TCF7L2 and TGFB1) with established roles in colorectal tumorigenesis. Four other loci are located in or near genes involved in transcriptional regulation (ZMIZ1), genome maintenance (FEN1), fatty acid metabolism (FADS1 and FADS2), cancer cell motility and metastasis (CD9), and cell growth and differentiation (NXN). We also found suggestive evidence for three additional loci associated with CRC risk near genome-wide significance at 8q24.11, 10q21.1 and 10q24.2. Furthermore, we replicated 22 previously reported CRC-associated loci. Our study provides insights into the genetic basis of CRC and suggests the involvement of new biological pathways.

Low-Pass DNA Sequencing of 1200 Sardinians Reconstructs European Y-Chromosome Phylogeny

Examining Y The evolution of human populations has long been studied with unique sequences from the nonrecombining, male-specific Y chromosome (see the Perspective by Cann). Poznik et al. (p. 562) examined 9.9 Mb of the Y chromosome from 69 men from nine globally divergent populations—identifying population and individual specific sequence variants that elucidate the evolution of the Y chromosome. Sequencing of maternally inherited mitochondrial DNA allowed comparison between the relative rates of evolution, which suggested that the coalescence, or origin, of the human Y chromosome and mitochondria both occurred approximately 120 thousand years ago. Francalacci et al. (p. 565) investigated the sequence divergence of 1204 Y chromosomes that were sampled within the isolated and genetically informative Sardinian population. The sequence analyses, along with archaeological records, were used to calibrate and increase the resolution of the human phylogenetic tree. Local human demographic history is inferred from in-depth DNA sequence analysis of Sardinian mens Y chromosomes. [Also see Perspective by Cann] Genetic variation within the male-specific portion of the Y chromosome (MSY) can clarify the origins of contemporary populations, but previous studies were hampered by partial genetic information. Population sequencing of 1204 Sardinian males identified 11,763 MSY single-nucleotide polymorphisms, 6751 of which have not previously been observed. We constructed a MSY phylogenetic tree containing all main haplogroups found in Europe, along with many Sardinian-specific lineage clusters within each haplogroup. The tree was calibrated with archaeological data from the initial expansion of the Sardinian population ~7700 years ago. The ages of nodes highlight different genetic strata in Sardinia and reveal the presumptive timing of coalescence with other human populations. We calculate a putative age for coalescence of ~180,000 to 200,000 years ago, which is consistent with previous mitochondrial DNA–based estimates.

Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin

BackgroundAlthough analysis pipelines have been developed to use RNA-seq to identify long non-coding RNAs (lncRNAs), inference of their biological and pathological relevance remains a challenge. As a result, most transcriptome studies of autoimmune disease have only assessed protein-coding transcripts.ResultsWe used RNA-seq data from 99 lesional psoriatic, 27 uninvolved psoriatic, and 90 normal skin biopsies, and applied computational approaches to identify and characterize expressed lncRNAs. We detect 2,942 previously annotated and 1,080 novel lncRNAs which are expected to be skin specific. Notably, over 40% of the novel lncRNAs are differentially expressed and the proportions of differentially expressed transcripts among protein-coding mRNAs and previously-annotated lncRNAs are lower in psoriasis lesions versus uninvolved or normal skin. We find that many lncRNAs, in particular those that are differentially expressed, are co-expressed with genes involved in immune related functions, and that novel lncRNAs are enriched for localization in the epidermal differentiation complex. We also identify distinct tissue-specific expression patterns and epigenetic profiles for novel lncRNAs, some of which are shown to be regulated by cytokine treatment in cultured human keratinocytes.ConclusionsTogether, our results implicate many lncRNAs in the immunopathogenesis of psoriasis, and our results provide a resource for lncRNA studies in other autoimmune diseases.

Genome-wide association analysis in East Asians identifies breast cancer susceptibility loci at 1q32.1, 5q14.3 and 15q26.1

In a three-stage genome-wide association study among East Asian women including 22,780 cases and 24,181 controls, we identified 3 genetic loci newly associated with breast cancer risk, including rs4951011 at 1q32.1 (in intron 2 of the ZC3H11A gene; P = 8.82 × 10−9), rs10474352 at 5q14.3 (near the ARRDC3 gene; P = 1.67 × 10−9) and rs2290203 at 15q26.1 (in intron 14 of the PRC1 gene; P = 4.25 × 10−8). We replicated these associations in 16,003 cases and 41,335 controls of European ancestry (P = 0.030, 0.004 and 0.010, respectively). Data from the ENCODE Project suggest that variants rs4951011 and rs10474352 might be located in an enhancer region and transcription factor binding sites, respectively. This study provides additional insights into the genetics and biology of breast cancer.

Recurrent Tissue-Specific mtDNA Mutations Are Common in Humans

Mitochondrial DNA (mtDNA) variation can affect phenotypic variation; therefore, knowing its distribution within and among individuals is of importance to understanding many human diseases. Intra-individual mtDNA variation (heteroplasmy) has been generally assumed to be random. We used massively parallel sequencing to assess heteroplasmy across ten tissues and demonstrate that in unrelated individuals there are tissue-specific, recurrent mutations. Certain tissues, notably kidney, liver and skeletal muscle, displayed the identical recurrent mutations that were undetectable in other tissues in the same individuals. Using RFLP analyses we validated one of the tissue-specific mutations in the two sequenced individuals and replicated the patterns in two additional individuals. These recurrent mutations all occur within or in very close proximity to sites that regulate mtDNA replication, strongly implying that these variations alter the replication dynamics of the mutated mtDNA genome. These recurrent variants are all independent of each other and do not occur in the mtDNA coding regions. The most parsimonious explanation of the data is that these frequently repeated mutations experience tissue-specific positive selection, probably through replication advantage.

A Likelihood-Based Framework for Variant Calling and De Novo Mutation Detection in Families

Family samples, which can be enriched for rare causal variants by focusing on families with multiple extreme individuals and which facilitate detection of de novo mutation events, provide an attractive resource for next-generation sequencing studies. Here, we describe, implement, and evaluate a likelihood-based framework for analysis of next generation sequence data in family samples. Our framework is able to identify variant sites accurately and to assign individual genotypes, and can handle de novo mutation events, increasing the sensitivity and specificity of variant calling and de novo mutation detection. Through simulations we show explicit modeling of family relationships is especially useful for analyses of low-frequency variants and that genotype accuracy increases with the number of individuals sequenced per family. Compared with the standard approach of ignoring relatedness, our methods identify and accurately genotype more variants, and have high specificity for detecting de novo mutation events. The improvement in accuracy using our methods over the standard approach is particularly pronounced for low-frequency variants. Furthermore the family-aware calling framework dramatically reduces Mendelian inconsistencies and is beneficial for family-based analysis. We hope our framework and software will facilitate continuing efforts to identify genetic factors underlying human diseases.

Genetic Polymorphisms in Pre-microRNA Genes as Prognostic Markers of Colorectal Cancer

Background: Cumulative data have shown that microRNAs (miRNA) are involved in the etiology and prognosis of colorectal cancer (CRC). Genetic polymorphisms in pre-miRNA genes may influence the biogenesis and functions of their host miRNAs. However, whether these polymorphisms are associated with CRC prognosis remains unknown. Methods: We analyzed the effects of seven single-nucleotide polymorphisms (SNP) in pre-miRNA genes on the prognosis of a Chinese population with 408 CRC patients with surgically-resected adenocarcinoma. Results: Two SNPs were identified to be significantly associated with recurrence-free survival and overall survival of the patients. The most significant SNP was rs6505162 in pre-miR-423. Compared with the homozygous wild-type genotype, the variant-containing genotypes of this SNP were significantly associated with both the overall survival (HR = 2.12, 95% CI = 1.34–3.34, P = 0.001) and the recurrence-free survival (HR = 1.59, 95% CI = 1.08–2.36, P = 0.019). Another SNP, rs4919510 in pre-miR-608, was also associated with altered recurrence-free survival (HR = 0.61, 95% CI = 0.41–0.92, P = 0.017). These effects were evident only in patients receiving chemotherapy but not in those without chemotherapy. In addition, the combined analysis of the two SNPs conferred a 2.84-fold (95% CI = 1.50–5.37, P = 0.001) increased risk of recurrence and/or death. Similarly, this effect was only prominent in those receiving chemotherapy (P < 0.001) but not in those without chemotherapy (P = 0.999). Conclusions: Our data suggest that genetic polymorphisms in pre-miRNA genes may impact CRC prognosis especially in patients receiving chemotherapy, a finding that warrants further independent validation. Impact: This is one of the first studies showing a prognostic role of pre-miRNA gene SNPs in CRC. Cancer Epidemiol Biomarkers Prev; 21(1); 217–27. ©2011 AACR.

Genotype calling and haplotyping in parent-offspring trios.

Emerging sequencing technologies allow common and rare variants to be systematically assayed across the human genome in many individuals. In order to improve variant detection and genotype calling, raw sequence data are typically examined across many individuals. Here, we describe a method for genotype calling in settings where sequence data are available for unrelated individuals and parent-offspring trios and show that modeling trio information can greatly increase the accuracy of inferred genotypes and haplotypes, especially on low to modest depth sequencing data. Our method considers both linkage disequilibrium (LD) patterns and the constraints imposed by family structure when assigning individual genotypes and haplotypes. Using simulations, we show that trios provide higher genotype calling accuracy across the frequency spectrum, both overall and at hard-to-call heterozygous sites. In addition, trios provide greatly improved phasing accuracy--improving the accuracy of downstream analyses (such as genotype imputation) that rely on phased haplotypes. To further evaluate our approach, we analyzed data on the first 508 individuals sequenced by the SardiNIA sequencing project. Our results show that our method reduces the genotyping error rate by 50% compared with analysis using existing methods that ignore family structure. We anticipate our method will facilitate genotype calling and haplotype inference for many ongoing sequencing projects.

RVTESTS: An Efficient and Comprehensive Tool for Rare Variant Association Analysis Using Sequence Data

Motivation: Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data. Availability and implementation: RVTESTS is available on Linux, MacOS and Windows. Source code and executable files can be obtained at https://github.com/zhanxw/rvtests Contact: zhanxw@gmail.com; goncalo@umich.edu; dajiang.liu@outlook.com Supplementary information: Supplementary data are available at Bioinformatics online.