Integrative analysis of single nucleotide polymorphisms and gene expression efficiently distinguishes samples from closely related ethnic populations

Vertical integration methods for gene expression data analysis

Gene expression data have played an essential role in many biomedical studies. When the number of genes is large and sample size is limited, there is a 'lack of information' problem, leading to low-quality findings. To tackle this problem, both horizontal and vertical data integrations have been developed, where vertical integration methods collectively analyze data on gene expressions as well as their regulators (such as mutations, DNA methylation and miRNAs). In this article, we conduct a selective review of vertical data integration methods for gene expression data. The reviewed methods cover both marginal and joint analysis and supervised and unsupervised analysis. The main goal is to provide a sketch of the vertical data integration paradigm without digging into too many technical details. We also briefly discuss potential pitfalls, directions for future developments and application notes.

Genetic ancestry plays a central role in population pharmacogenomics

Recent studies have pointed out the essential role of genetic ancestry in population pharmacogenetics. In this study, we analyzed the whole-genome sequencing data from The 1000 Genomes Project (Phase 3) and the pharmacogenetic information from Drug Bank, PharmGKB, PharmaADME, and Biotransformation. Here we show that ancestry-informative markers are enriched in pharmacogenetic loci, suggesting that trans-ancestry differentiation must be carefully considered in population pharmacogenetics studies. Ancestry-informative pharmacogenetic loci are located in both protein-coding and non-protein-coding regions, illustrating that a whole-genome analysis is necessary for an unbiased examination over pharmacogenetic loci. Finally, those ancestry-informative pharmacogenetic loci that target multiple drugs are often a functional variant, which reflects their importance in biological functions and pathways. In summary, we develop an efficient algorithm for an ultrahigh-dimensional principal component analysis. We create genetic catalogs of ancestry-informative markers and genes. We explore pharmacogenetic patterns and establish a high-accuracy prediction panel of genetic ancestry. Moreover, we construct a genetic ancestry pharmacogenomic database Genetic Ancestry PhD (http://hcyang.stat.sinica.edu.tw/databases/genetic_ancestry_phd/).

Hsin-Chou Yang et al. examine population structure in several genomic databases and identify that pharmacogenetic loci are enriched for markers of genetic ancestry. Their results suggest that genetic ancestry must be carefully considered in population pharmacogenetics studies.

Clinical performance of DNA-based prenatal screening using single-nucleotide polymorphisms approach in Thai women with singleton pregnancy

Background

To review the performance of noninvasive prenatal screening (NIPS) using targeted single‐nucleotide polymorphisms (SNPs) approach in mixed‐risk Thai women.

Methods

Retrospective analysis of data for detection of trisomy 21 (T21), 18 (T18), 13 (T13), monosomy X (XO), other sex chromosome aneuploidies (SCA), and triploidy/vanishing twins (VT) from a single commercial laboratory.

Results

Mean (±SD) gestational age and maternal weight were 13.2 (±2.1) weeks and 125.7 (±22.4) pounds, respectively (n = 8,572). From 462/8,572 (5.4%) no‐calls; 1/462 (0.2%) was uninformative SNPs, and 1/462 chose amniocentesis. Redraw settled 323/460 (70%) samples with low fetal fraction (FF); and 8,434/8,572 (98.4%) were finally reportable, with 131 high risks (1.6%). The median (min‐max) FF of reportable (n = 8,434) and unreportable samples (n = 137) samples were 10.5% (2.6–37.9) and 3.8% (1–14.1), respectively (p < .05). Fetal karyotypes were available in 106/131 (80.9%) and 52/138 (37.7%) high risk and repeated no‐calls, respectively. The positive predictive values (PPVs) for T21 (n = 47), T18 (n = 15), T13 (n = 7), XO (n = 8), other SCA (n = 7), and triploidy/VT were 94%, 100%, 58.3%, 66.7%, 70%, and 57.1%, respectively. None of repeated no‐calls had aneuploidies.

Conclusion

SNP‐based NIPS has high PPVs for T21 and T18. Although the proprietary SNPs library is not population‐specific, uninformative SNPs are uncommon.

Establishment of two basal-like breast cancer cell lines with extremely low tumorigenicity from Taiwanese premenopausal women

The research of carcinogenetic mechanisms of breast cancer in different ethnic backgrounds is an interesting field, as clinical features of breast cancers vary among races. High premenopausal incidence is distinctive in East-Asian breast cancer. However, human cell lines derived from Asian primary breast tumor are rare. To provide alternative cell line models with a relevant genetic background, we aimed to establish breast cancer cell lines from Taiwanese patients of Han-Chinese ethnicity. Fresh tissue from mammary tumors were digested into organoids, plated and grown in basal serum-free medium of human mammary epithelial cells (HuMEC) with supplements. Cells were further enriched by positive selection with CD326 (epithelial cell adhesion molecule; EpCAM)-coated micro-magnetic beads. Two breast cancer cell lines derived from premenopausal women were successfully established by this method, and named Chang-Gung Breast Cancer 01 (CGBC 01) and 02 (CGBC 02). These two cell lines had a similar phenotype with weak expression of estrogen receptor (ER), progesterone receptor (PR), and without amplification of receptor tyrosine protein kinase erbB-2 (HER2/neu). Genome-wide Single Nucleotide Polymorphism (SNP) array showed multiple copy number alterations in both cell lines. Based on gene expression profiles, CGBC 01 and 02 were clustered into basal-like subtype with reference to the breast cancer cell line gene expression database. The tumorigenicity of both cell lines was extremely low in both anchorage-independence assay and transplantation into the mammary fat pads of nude mice. CGBC 01 and CGBC 02 are low tumorigenic breast cancer cell lines, established from Han-Chinese premenopausal breast cancer patients, which serve as in vitro models in studying the biological features of Asian breast cancer.

Genetic signatures of heroin addiction

Heroin addiction is a complex psychiatric disorder with a chronic course and a high relapse rate, which results from the interaction between genetic and environmental factors. Heroin addiction has a substantial heritability in its etiology; hence, identification of individuals with a high genetic propensity to heroin addiction may help prevent the occurrence and relapse of heroin addiction and its complications. The study aimed to identify a small set of genetic signatures that may reliably predict the individuals with a high genetic propensity to heroin addiction. We first measured the transcript level of 13 genes (RASA1, PRKCB, PDK1, JUN, CEBPG, CD74, CEBPB, AUTS2, ENO2, IMPDH2, HAT1, MBD1, and RGS3) in lymphoblastoid cell lines in a sample of 124 male heroin addicts and 124 male control subjects using real-time quantitative PCR. Seven genes (PRKCB, PDK1, JUN, CEBPG, CEBPB, ENO2, and HAT1) showed significant differential expression between the 2 groups. Further analysis using 3 statistical methods including logistic regression analysis, support vector machine learning analysis, and a computer software BIASLESS revealed that a set of 4 genes (JUN, CEBPB, PRKCB, ENO2, or CEBPG) could predict the diagnosis of heroin addiction with the accuracy rate around 85% in our dataset. Our findings support the idea that it is possible to identify genetic signatures of heroin addiction using a small set of expressed genes. However, the study can only be considered as a proof-of-concept study. As the establishment of lymphoblastoid cell line is a laborious and lengthy process, it would be more practical in clinical settings to identify genetic signatures for heroin addiction directly from peripheral blood cells in the future study.

Exploring transcriptomic diversity in muscle revealed that cellular signaling pathways mainly differentiate five Western porcine breeds

Background

Among transcriptomic studies, those comparing species or populations can increase our understanding of the impact of the evolutionary forces on the differentiation of populations. A particular situation is the one of short evolution time with breeds of a domesticated species that underwent strong selective pressures. In this study, the gene expression diversity across five pig breeds has been explored in muscle. Samples came from: 24 Duroc, 33 Landrace, 41 Large White dam line, 10 Large White sire line and 39 Piétrain. From these animals, 147 muscle samples obtained at slaughter were analyzed using the porcine Agilent 44 K v1 microarray.

Results

A total of 12,358 genes were identified as expressed in muscle after normalization and 1,703 genes were declared differential for at least one breed (FDR < 0.001). The functional analysis highlighted that gene expression diversity is mainly linked to cellular signaling pathways such as the PI3K (phosphoinositide 3-kinase) pathway. The PI3K pathway is known to be involved in the control of development of the skeletal muscle mass by affecting extracellular matrix - receptor interactions, regulation of actin cytoskeleton pathways and some metabolic functions. This study also highlighted 228 spots (171 unique genes) that differentiate the breeds from each other. A common subgroup of 15 genes selected by three statistical methods was able to differentiate Duroc, Large White and Piétrain breeds.

Conclusions

This study on transcriptomic differentiation across Western pig breeds highlighted a global picture: mainly signaling pathways were affected. This result is consistent with the selection objective of increasing muscle mass. These transcriptional changes may indicate selection pressure or simply breed differences which may be driven by human selection. Further work aiming at comparing genetic and transcriptomic diversities would further increase our understanding of the consequences of human impact on livestock species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2259-9) contains supplementary material, which is available to authorized users.

Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

BACKGROUND

Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL).

RESULTS

In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations.

CONCLUSIONS

In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.

Current and future directions of DNA in wildlife forensic science

Wildlife forensic science may not have attained the profile of human identification, yet the scale of criminal activity related to wildlife is extensive by any measure. Service delivery in the arena of wildlife forensic science is often ad hoc, unco-ordinated and unregulated, yet many of those currently dedicated to wildlife conservation and the protection of endangered species are striving to ensure that the highest standards are met. The genetic markers and software used to evaluate data in wildlife forensic science are more varied than those in human forensic identification and are rarely standardised between species. The time and resources required to characterise and validate each genetic maker is considerable and in some cases prohibitive. Further, issues are regularly encountered in the construction of allelic databases and allelic ladders; essential in human identification studies, but also applicable to wildlife criminal investigations. Accreditation and certification are essential in human identification and are currently being strived for in the forensic wildlife community. Examples are provided as to how best practice can be demonstrated in all areas of wildlife crime analysis and ensure that this field of forensic science gains and maintains the respect it deserves. This review is aimed at those conducting human identification to illustrate how research concepts in wildlife forensic science can be used in the criminal justice system, as well as describing the real importance of this type of forensic analysis.

VNN1 gene expression levels and the G-137T polymorphism are associated with HDL-C levels in Mexican prepubertal children

BACKGROUND

VNN1 gene expression levels and the G-137T polymorphism have been associated with high density lipoprotein cholesterol (HDL-C) levels in Mexican American adults. We aim to evaluate the contribution of VNN1 gene expression and the G-137T variant to HDL-C levels and other metabolic traits in Mexican prepubertal children.

METHODOLOGY/PRINCIPAL FINDINGS

VNN1 mRNA expression levels were quantified in peripheral blood leukocytes from 224 unrelated Mexican-Mestizo children aged 6-8 years (107 boys and 117 girls) and were genotyped for the G-137T variant (rs4897612). To account for population stratification, a panel of 10 ancestry informative markers was analyzed. After adjustment for admixture, the TT genotype was significantly associated with lower VNN1 mRNA expression levels (P = 2.9 × 10(-5)), decreased HDL-C levels (β = -6.19, P = 0.028) and with higher body mass index (BMI) z-score (β = 0.48, P = 0.024) in the total sample. In addition, VNN1 expression showed a positive correlation with HDL-C levels (r = 0.220; P = 0.017) and a negative correlation with BMI z-score (r = -0.225; P = 0.015) only in girls.

CONCLUSION/SIGNIFICANCE

Our data suggest that VNN1 gene expression and the G-137T variant are associated with HDL-C levels in Mexican children, particularly in prepubertal girls.