Virus-derived variation in diverse human genomes

Acquisition of genetic material from viruses by their hosts can generate inter-host structural genome variation. We developed computational tools enabling us to study virus-derived structural variants (SVs) in population-scale whole genome sequencing (WGS) datasets and applied them to 3,332 humans. Although SVs had already been cataloged in these subjects, we found previously-overlooked virus-derived SVs. We detected non-germline SVs derived from squirrel monkey retrovirus (SMRV), human immunodeficiency virus 1 (HIV-1), and human T lymphotropic virus (HTLV-1); these variants are attributable to infection of the sequenced lymphoblastoid cell lines (LCLs) or their progenitor cells and may impact gene expression results and the biosafety of experiments using these cells. In addition, we detected new heritable SVs derived from human herpesvirus 6 (HHV-6) and human endogenous retrovirus-K (HERV-K). We report the first solo-direct repeat (DR) HHV-6 likely to reflect DR rearrangement of a known full-length endogenous HHV-6. We used linkage disequilibrium between single nucleotide variants (SNVs) and variants in reads that align to HERV-K, which often cannot be mapped uniquely using conventional short-read sequencing analysis methods, to locate previously-unknown polymorphic HERV-K loci. Some of these loci are tightly linked to trait-associated SNVs, some are in complex genome regions inaccessible by prior methods, and some contain novel HERV-K haplotypes likely derived from gene conversion from an unknown source or introgression. These tools and results broaden our perspective on the coevolution between viruses and humans, including ongoing virus-to-human gene transfer contributing to genetic variation between humans.     

Multivariate eQTL mapping uncovers functional variation on the X-chromosome associated with complex disease traits

Very few studies have investigated the associations between genetic polymorphisms and gene expression on the X-chromosome. This is a major bottleneck when conducting functional follow-up studies of trait-associated variants, as those identified in genome-wide association studies (GWAS). We used a multivariate approach to test the association between individual single nucleotide polymorphisms (SNPs) and exon expression levels measured in 356 Epstein–Barr virus-transformed lymphoblastoid cell lines (LCLs) from the Geuvadis RNA sequencing project to identify SNPs associated with variation in gene expression on the X-chromosome, which we refer to as eSNPs. At an FDR of 5 %, we discovered 548 independent [linkage disequilibrium (LD) r ² < 0.1] eSNPs on the X-chromosome. Of these, 35 were in LD (r ² > 0.8) with previously published disease- or trait-associated variants identified through GWAS. One of the strongest eSNPs identified was rs35975601, which was associated with F8A1 expression (p value = 3 × 10^?20) and was in LD with a type 1 diabetes risk variant. Additionally, we identified a number of genes for which eSNPs were in LD with multiple diseases or traits, including DNASE1L1 which was mapped to bilirubin levels, type 1 diabetes and schizophrenia. Our results also indicate that multivariate exon-level analysis provides a more powerful approach than univariate gene-level analysis, particularly when SNPs influence the expression of different exons with different magnitude and/or direction of effect. The associations identified in our study may provide new insights into the molecular process by which gene expression may contribute to trait variation or disease risk in humans.     

Analysis of copy number variants in the cattle genome

Copy number variation (CNV) is likely to be an important component of heritable variation in livestock. To characterise CNVs in cattle, we performed a genome wide survey to determine the number, location and gene content of these genomic features. A tiling oligonucleotide array with ~385,000 probes was used for comparative genomic hybridisation of both taurine and zebu cattle. Using a conservative set of calling criteria, a total of 51 CNV were detected that collectively spanned approximately half of one percent of the bovine genome. The size of the average CNV within each animal ranged from 213 kb up to 335 kb. Half of the CNV were detected in a single animal only, whilst the remainder was independently identified in multiple individuals. Analysis was performed to determine the gene content for each CNV region. This revealed that the majority of CNV (82%) spanned at least one gene, with a number of CNV containing genes which are known to control aspects of phenotypic variation in cattle. Whilst additional studies are required to determine the impact of individual CNV, this study confirmed them as an important class of genomic variation in cattle.     

畜禽生产性状主效基因定位技术与方法研究进展

鉴定畜禽重要生产性状形成的关键遗传因子,揭示其产生的分子遗传机制,对动物新品种的培育及特色遗传资源的开发利用具有重要意义。高通量分析技术诞生前,标记数量性状位点(quantitative trait loci,QTL)连锁分析和候选基因分析法作为鉴定畜禽生产性状QTL和主效基因主要方法,但由于其技术局限性,性状因果基因鉴定的效率并不高。人类基因组计划(human genome project, HGP)开启了以大数据为特征的生命科学研究的序幕,在HGP完成后的20余年间,快速发展的高通量分析技术使人们研究生物学问题的思路和方法发生了巨大改变。本综述就近年来被广泛用于畜禽重要性状的候选基因定位的方法与技术进行了较全面的综述,以期为同行提供参考。     

Recent genome duplications facilitate the phenotypic diversity of Hb repertoire in the Cyprinidae

Whole-genome duplications(WGDs) are an important contributor to phenotypic innovations in evolutionary history. The diversity of blood oxygen transport traits is the perfect reflection of physiological versatility for evolutionary success among vertebrates. In this study, the evolutionary changes of hemoglobin(Hb) repertoire driven by the recent genome duplications were detected in representative Cyprinidae fish, including eight diploid and four tetraploid species. Comparative genomic analysis revealed a substantial variation in both membership composition and intragenomic organization of Hb genes in these species.Phylogenetic reconstruction analyses were conducted to characterize the evolutionary history of these genes. Data were integrated with the expression profiles of the genes during ontogeny. Our results indicated that genome duplications facilitated the phenotypic diversity of the Hb gene family; each was associated with species-specific changes in gene content via gene loss and fusion after genome duplications. This led to repeated evolutionary transitions in the ontogenic regulation of Hb gene expression.Our results revealed that genome duplications helped to generate phenotypic changes in Cyprinidae Hb systems.     

Exploring copy number variation in the rabbit (Oryctolagus cuniculus) genome by array comparative genome hybridization

The European rabbit (Oryctolagus cuniculus) is relevant in a large spectrum of fields: it is a livestock, a pet, a biomedical model and a biotechnology tool, a wild resource and a pest. The sequencing of the rabbit genome has opened new perspectives to study this lagomorph at the genome level. We herein investigated for the first time the O. cuniculus genome by array comparative genome hybridization (aCGH) and established a first copy number variation (CNV) genome map in this species comprising 155 copy number variation regions (CNVRs; 95 gains, 59 losses, 1 with both gain and loss) covering ~0.3% of the OryCun2.0 version. About 50% of the 155 CNVRs identified spanned 139 different protein coding genes, 110 genes of which were annotated or partially annotated (including Major Histocompatibility Complex genes) with 277 different gene ontology terms. Many rabbit CNVRs might have a functional relevance that should be further investigated.     

Mutation bias is the driving force of codon usage in the Gallus gallus genome

Synonymous codons are used with different frequencies both among species and among genes within the same genome and are controlled by neutral processes (such as mutation and drift) as well as by selection. Up to now, a systematic examination of the codon usage for the chicken genome has not been performed. Here, we carried out a whole genome analysis of the chicken genome by the use of the relative synonymous codon usage (RSCU) method and identified 11 putative optimal codons, all of them ending with uracil (U), which is significantly departing from the pattern observed in other eukaryotes. Optimal codons in the chicken genome are most likely the ones corresponding to highly expressed transfer RNA (tRNAs) or tRNA gene copy numbers in the cell. Codon bias, measured as the frequency of optimal codons (Fop), is negatively correlated with the G + C content, recombination rate, but positively correlated with gene expression, protein length, gene length and intron length. The positive correlation between codon bias and protein, gene and intron length is quite different from other multi-cellular organism, as this trend has been only found in unicellular organisms. Our data displayed that regional G + C content explains a large proportion of the variance of codon bias in chicken. Stepwise selection model analyses indicate that G + C content of coding sequence is the most important factor for codon bias. It appears that variation in the G + C content of CDSs accounts for over 60% of the variation of codon bias. This study suggests that both mutation bias and selection contribute to codon bias. However, mutation bias is the driving force of the codon usage in the Gallus gallus genome. Our data also provide evidence that the negative correlation between codon bias and recombination rates in G. gallus is determined mostly by recombination-dependent mutational patterns.     

Genomic analysis of the relationship between gene expression variation and DNA polymorphism in Drosophila simulans

Background

Understanding how DNA sequence polymorphism relates to variation in gene expression is essential to connecting genotypic differences with phenotypic differences among individuals. Addressing this question requires linking population genomic data with gene expression variation.

Results

Using whole genome expression data and recent light shotgun genome sequencing of six Drosophila simulans genotypes, we assessed the relationship between expression variation in males and females and nucleotide polymorphism across thousands of loci. By examining sequence polymorphism in gene features, such as untranslated regions and introns, we find that genes showing greater variation in gene expression between genotypes also have higher levels of sequence polymorphism in many gene features. Accordingly, X-linked genes, which have lower sequence polymorphism levels than autosomal genes, also show less expression variation than autosomal genes. We also find that sex-specifically expressed genes show higher local levels of polymorphism and divergence than both sex-biased and unbiased genes, and that they appear to have simpler regulatory regions.

Conclusion

The gene-feature-based analyses and the X-to-autosome comparisons suggest that sequence polymorphism in cis-acting elements is an important determinant of expression variation. However, this relationship varies among the different categories of sex-biased expression, and trans factors might contribute more to male-specific gene expression than cis effects. Our analysis of sex-specific gene expression also shows that female-specific genes have been overlooked in analyses that only point to male-biased genes as having unusual patterns of evolution and that studies of sexually dimorphic traits need to recognize that the relationship between genetic and expression variation at these traits is different from the genome as a whole.     

MicroRNA regulation and the variability of human cortical gene expression

Understanding the driving forces of gene expression variation within human populations will provide important insights into the molecular basis of human phenotypic variation. In the genome, the gene expression variability differs among genes, and at present, most research has focused on identifying the genetic variants responsible for the within population gene expression variation. However, little is known about whether microRNAs (miRNAs), which are small noncoding RNAs modulating expression of their target genes, could have impact on the variability of gene expression. Here we demonstrate that miRNAs likely lead to the difference of expression variability among genes. With the use of the genome-wide expression data in 193 human brain samples, we show that the increased variability of gene expression is concomitant with the increased number of the miRNA seeds interacting with the target genes, suggesting a direct influence of miRNA on gene expression variability. Compared with the non-miRNA-target genes, genes targeted by more than two miRNA seeds have increased expression variability, independent of the miRNA types. In addition, single-nucleotide polymorphisms (SNPs) located in the miRNA binding sites could further increase the gene expression variability of the target genes. We propose that miRNAs are one of the driving forces causing expression variability in the human genome.     

Computing evolutionary distinctiveness indices in large scale analysis

Background

A combined quantitative trait loci (QTL) and microarray-based approach is commonly used to find differentially expressed genes which are then identified based on the known function of a gene in the biological process governing the trait of interest. However, a low cutoff value in individual gene analyses may result in many genes with moderate but meaningful changes in expression being missed.

Results

We modified a gene set analysis to identify intersection sets with significantly affected expression for which the changes in the individual gene sets are less significant. The gene expression profiles in liver tissues of four strains of mice from publicly available microarray sources were analyzed to detect trait-associated pathways using information on the QTL regions of blood concentrations of high density lipoproteins (HDL) cholesterol and insulin-like growth factor 1 (IGF-1). Several metabolic pathways related to HDL levels, including lipid metabolism, ABC transporters and cytochrome P450 pathways were detected for HDL QTL regions. Most of the pathways identified for the IGF-1 phenotype were signal transduction pathways associated with biological processes for IGF-1's regulation.

Conclusion

We have developed a method of identifying pathways associated with a quantitative trait using information on QTL. Our approach provides insights into genotype-phenotype relations at the level of biological pathways which may help to elucidate the genetic architecture underlying variation in phenotypic traits.