Evaluation of Five Candidate Genes from GWAS for Association with Oligozoospermia in a Han Chinese Population.
PLoS One. 2013;8(11):e80374
Authors: Xu M, Qin Y, Qu J, Lu C, Wang Y, Wu W, Song L, Wang S, Chen F, Shen H, Sha J, Hu Z, Xia Y, Wang X
BACKGROUND: Oligozoospermia is one of the severe forms of idiopathic male infertility. However, its pathology is largely unknown, and few genetic factors have been defined. Our previous genome-wide association study (GWAS) has identified four risk loci for non-obstructive azoospermia (NOA).
OBJECTIVE: To investigate the potentially functional genetic variants (including not only common variants, but also less-common and rare variants) of these loci on spermatogenic impairment, especially oligozoospermia.
DESIGN SETTING AND PARTICIPANTS: A total of 784 individuals with oligozoospermia and 592 healthy controls were recruited to this study from March 2004 and January 2011.
MEASUREMENTS: We conducted a two-stage study to explore the association between oligozoospermia and new makers near NOA risk loci. In the first stage, we used next generation sequencing (NGS) in 96 oligozoospermia cases and 96 healthy controls to screen oligozoospermia-susceptible genetic variants. Next, we validated these variants in a large cohort containing 688 cases and 496 controls by SNPscan for high-throughput Single Nucleotide Polymorphism (SNP) genotyping.
RESULTS AND LIMITATIONS: Totally, we observed seven oligozoospermia associated variants (rs3791185 and rs2232015 in PRMT6, rs146039840 and rs11046992 in Sox5, rs1129332 in PEX10, rs3197744 in SIRPA, rs1048055 in SIRPG) in the first stage. In the validation stage, rs3197744 in SIRPA and rs11046992 in Sox5 were associated with increased risk of oligozoospermia with an odds ratio (OR) of 4.62 (P = 0.005, 95%CI 1.58-13.4) and 1.82 (P = 0.005, 95%CI 1.01-1.64), respectively. Further investigation in larger populations and functional characterizations are needed to validate our findings.
CONCLUSIONS: Our study provides evidence of independent oligozoospermia risk alleles driven by variants in the potentially functional regions of genes discovered by GWAS. Our findings suggest that integrating sequence data with large-scale genotyping will serve as an effective strategy for discovering risk alleles in the future.
PMID: 24303009 [PubMed - in process]
Imputation-based assessment of next generation rare exome variant arrays.
Pac Symp Biocomput. 2014;19:241-52
Authors: Martin AR, Tse G, Bustamante CD, Kenny EE
A striking finding from recent large-scale sequencing efforts is that the vast majority of variants in the human genome are rare and found within single populations or lineages. These observations hold important implications for the design of the next round of disease variant discovery efforts-if genetic variants that influence disease risk follow the same trend, then we expect to see population-specific disease associations that require large sample sizes for detection. To address this challenge, and due to the still prohibitive cost of sequencing large cohorts, researchers have developed a new generation of low-cost genotyping arrays that assay rare variation previously identified from large exome sequencing studies. Genotyping approaches rely not only on directly observing variants, but also on phasing and imputation methods that use publicly available reference panels to infer unobserved variants in a study cohort. Rare variant exome arrays are intentionally enriched for variants likely to be disease causing, and here we assay the ability of the first commercially available rare exome variant array (the Illumina Infinium HumanExome BeadChip) to also tag other potentially damaging variants not molecularly assayed. Using full sequence data from chromosome 22 from the phase I 1000 Genomes Project, we evaluate three methods for imputation (BEAGLE, MaCH-Admix, and SHAPEIT2/IMPUTE2) with the rare exome variant array under varied study panel sizes, reference panel sizes, and LD structures via population differences. We find that imputation is more accurate across both the genome and exome for common variant arrays than the next generation array for all allele frequencies, including rare alleles. We also find that imputation is the least accurate in African populations, and accuracy is substantially improved for rare variants when the same population is included in the reference panel. Depending on the goals of GWAS researchers, our results will aid budget decisions by helping determine whether money is best spent sequencing the genomes of smaller sample sizes, genotyping larger sample sizes with rare and/or common variant arrays and imputing SNPs, or some combination of the two.
PMID: 24297551 [PubMed - in process]
Gene-environment interaction effects on lung function- a genome-wide association study within the Framingham heart study.
Environ Health. 2013 Dec 1;12(1):101
Authors: Liao SY, Lin X, Christiani DC
BACKGROUND: Previous studies in occupational exposure and lung function have focused only on the main effect of occupational exposure or genetics on lung function. Some disease-susceptible genes may be missed due to their low marginal effects, despite potential involvement in the disease process through interactions with the environment. Through comprehensive genome-wide gene-environment interaction studies, we can uncover these susceptibility genes. Our objective in this study was to explore gene by occupational exposure interaction effects on lung function using both the individual SNPs approach and the genetic network approach.
METHODS: The study population comprised the Offspring Cohort and the Third Generation from the Framingham Heart Study. We used forced expiratory volume in one second (FEV1) and ratio of FEV1 to forced vital capacity (FVC) as outcomes. Occupational exposures were classified using a population-specific job exposure matrix. We performed genome-wide gene-environment interaction analysis, using the Affymetrix 550 K mapping array for genotyping. A linear regression-based generalized estimating equation was applied to account for within-family relatedness. Network analysis was conducted using results from single-nucleotide polymorphism (SNP)-level analyses and from gene expression study results.
RESULTS: There were 4,785 participants in total. SNP-level analysis and network analysis identified SNP rs9931086 (Pinteraction =1.16 x 10-7) in gene SLC38A8, which may significantly modify the effects of occupational exposure on FEV1. Genes identified from the network analysis included CTLA-4, HDAC, and PPAR-alpha.
CONCLUSIONS: Our study implies that SNP rs9931086 in SLC38A8 and genes CTLA-4, HDAC, and PPAR-alpha, which are related to inflammatory processes, may modify the effect of occupational exposure on lung function.
PMID: 24289273 [PubMed - as supplied by publisher]
Development of a genotyping microarray for studying the role of gene-environment interactions in risk for lung cancer.
J Biomol Tech. 2013 Dec;24(4):198-217
Authors: Baldwin DA, Sarnowski CP, Reddy SA, Blair IA, Clapper M, Lazarus P, Li M, Muscat JE, Penning TM, Vachani A, Whitehead AS
A microarray (LungCaGxE), based on Illumina BeadChip technology, was developed for high-resolution genotyping of genes that are candidates for involvement in environmentally driven aspects of lung cancer oncogenesis and/or tumor growth. The iterative array design process illustrates techniques for managing large panels of candidate genes and optimizing marker selection, aided by a new bioinformatics pipeline component, Tagger Batch Assistant. The LungCaGxE platform targets 298 genes and the proximal genetic regions in which they are located, using ∼13,000 DNA single nucleotide polymorphisms (SNPs), which include haplotype linkage markers with a minimum allele frequency of 1% and additional specifically targeted SNPs, for which published reports have indicated functional consequences or associations with lung cancer or other smoking-related diseases. The overall assay conversion rate was 98.9%; 99.0% of markers with a minimum Illumina design score of 0.6 successfully generated allele calls using genomic DNA from a study population of 1873 lung-cancer patients and controls.
PMID: 24294113 [PubMed - in process]