Whole-genome re-sequencing for the identification of high contribution susceptibility gene variants in patients with type 2 diabetes
- Authors:
- Published online on: March 18, 2016 https://doi.org/10.3892/mmr.2016.5014
- Pages: 3735-3746
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Copyright: © Sun et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Type 2 diabetes (T2D) is a complex, multifactorial disorder characterized by chronic hyperglycemia due to the interplay of multiple genetic variants and several environmental factors. As a result of aging populations, and the increasing prevalence of obesity and physical inactivity, the number of patients with T2D has markedly increased worldwide (1). The disease is considered a polygenic disorder, in which each genetic variant confers a partial and additive effect. Only 5–10% of T2D cases are due to single gene defects; these include maturity-onset diabetes of the young, insulin resistance syndromes, mitochondrial diabetes and neonatal diabetes (2). Examining T2D susceptibility genes may be useful for the prediction, prevention and early treatment of the disease.
Following previous genome-wide association studies (GWAS), the number of replicated common genetic variants associated with T2D has rapidly increased (3–9). In addition, >40 T2D-associated genetic loci have been identified, however, these loci have been revealed primarily on the basis of investigations of European individuals (10). The identified genomes only explain a small proportion of the estimated heritability of T2D, suggesting that additional genetic factors remain to be identified. One limitation of GWAS is the large number of hypotheses and the high economic cost of these investigations (11). Several studies have addressed the feasibility and effectiveness of pooling-based GWAS, with considerable savings in time and cost (11–13). Additionally, whole-genome sequencing across multiple samples in a population provides an unprecedented opportunity for comprehensively characterizing the polymorphic variants in the population (14).
Although the genetic contribution to T2D is well recognized, there are now at least 19 loci containing genes, which are known to increase the risk of T2D, including PPARG, KCNJ11, KCNQ1, CDKAL1, CDKN2A-2B, CDC123-CAMK1D, MTNR1B, TCF7L2, TCF2 (HNF1B), HHEX-KIF11-IDE, JAZF1, IGF2BP2, SLC30A8, THADA, ADAMTS9, WFS1, FTO, NOTCH2 and TSPAN8 (2). To date, the current set of 66 established susceptibility loci, identified primarily through large-scale GWAS (2,8,15–22), encompasses, at most, 10% of the familial aggregation of the disease. Of the currently established susceptibility loci, nine of the loci are contained in the 19 loci-containing genes. In the present study, the genomes of 100 Chinese patients with T2D and 100 non-diabetic Chinese individuals were examined using high throughput genome-wide re-sequencing and DNA pooling with Illumina HiSeq 2000 (Illumina, San Diego, CA, USA). The aim of the present study was to determine the rates of susceptibility genes in T2D in the Chinese population.
Materials and methods
Study populations
The present study was performed between August 2012 and the end of June 2013 at the 181st Hospital of People's Liberation Army, (Guilin, China). A total of 200 Chinese individuals were recruited, of which half were diagnosed with T2D. All participants with T2D were unrelated, and their disease was defined by World Health Organization criteria (23). The healthy individuals had a fasting plasma glucose <5.6 mmol/l, a 2-h oral glucose tolerance test-based plasma glucose <7.8 mmol/l, a body mass index <27.5 kg/m2 and blood pressure <140/90 mmHg, with no antihypertensive treatment. The clinical and biochemical characteristics of the 200 individuals are presented in Table I.
Ethics
The present study was approved by the Medical and Health Research Ethics Committee of the 181st Hospital of the People's Liberation Army (Guilin, China). All participants provided informed consent for the use of their biological samples for genetic investigation.
Experimental procedure
Peripheral blood samples from the 200 volunteers were collected for genomic DNA extraction. DNA preparation followed the manufacturer's protocol (Illumina). Genomic DNA was extracted and then randomly fragmented. Following electrophoresis, DNA fragments of desired length (90 bp) were gel purified using QIAquick PCR Purification kit (Qiagen GmbH, Hilden, Germany). Adapter ligation and DNA cluster preparation were performed as part of Solexa sequencing by Beijing Genomics Institute (Shenzhen, China) (24–26).
Bioinformatics analysis
The bioinformatics analysis used the sequencing data (raw data) generated from the Illumina HiSeq 2000. First, the adapter sequence in the raw data was removed, and low quality reads, which contained too many unknown bases (N) or low quality bases were discarded. This step produced 'clean data'. Secondly, Burrows-Wheeler Aligner (BWA) (27) was used to align the reads to the reference sequence. The alignment information was stored in BAM format files, which were further processed during subsequent steps, including fixing mate-pair information, adding read group information and marking duplicate reads caused by polymerase chain reaction (PCR). Following these processes, the final BAM files were ready for variant calling. Single nucleotide polymorphisms (SNPs) were detected using SOAPsnp (28), small insertion/deletions (InDels) were detected using SAMtools (29)/GATK (30,31), copy number variants (CNVs) were detected using CNVnator (32,33), single nucleotide variants (SNVs) were detected using Varscan (34) and somatic InDels were detected using GATK. Structure variants (SVs) and somatic CNVs were identified using BreakDancer (35)/CREST/SeekSV (self-method) and a self-method based on the SegSeq (36) algorithm, respectively. Virus integration sites were identified using a self-method based on unmapped reads. The procedure also included purity estimation. Subsequently, filters were applied to obtain variant results of higher confidence and, based on which subsequent advanced analysis could be performed, ANNOVAR (37) was used to annotate the variant results. Quality control was required at each stage of analysis to ensure clean data, alignment and variants. SIFT (38) was used to assess the likely phenotypic effect of identified missense mutations. PolyPhen-2 (39) analysis was performed to calculate the probability of an identified mutation as deleterious for disease pathogenesis.
Sequencing quality control
The raw reads, which contained the adapter sequence, a high content of unknown bases and low quality reads were removed prior to data analysis. The filtering steps were as follows: i) Removal of adapter reads. An adapter read was defined as a read that included the adapter bases, which were removed from the raw FASTQ data; ii) removal of low-quality reads. If more than half of the bases in a read were low-quality, defined as a base quality ≤5, the read was treated as a low-quality read and removed from the raw FASTQ data; iii) removal of reads in which unknown bases were >10%. The 'clean reads' were then used for downstream bioinformatics analysis. Finally statistical analysis was performed for data interpretation. The quality of the clean data is shown in Figs. 1 and 2.
Alignment quality control
The human genome build37 (hg19) was used as the reference genome in the present study. The whole-genome size of hg19 is 3,137,161,264 bp, whereas the effective size is 2,861,327,131 bp, following exclusion of the N bases, random regions, hap regions, and chromosome Un and chromosome M in the reference sequence. BWA was used to align the reference genome sequence for sequencing reads. Picard (broadinstitute.github.io/picard) was used to mark duplicated reads, which were redundant information produced by PCR.
Results
Quality control of sequencing data
To minimize the likelihood of systematic bias during sampling, two paired-end libraries with insert sizes of 500 bp were prepared for all samples and were subjected to whole-genome sequencing. Each library comprised four lanes, resulting in at least 30-fold haploid coverage for each sample. The raw image files were processed using the Illumina pipeline for base calling; default parameters and the sequences of each individual were generated as 90-bp-paired-end reads. A total of 144.3 GB raw sequence data were generated in a sequencing depth of ~30-fold. As shown in Table II, comparison was performed between the raw and clean data, which were detected using whole-genome re-sequencing.
Alignment of quality control data
The resulting calculated T2D genome consensus sequence covered 99.88% of the hg19 sequence. At a depth of 10-fold, the assembled consensus covered 97.98% of the reference genome using two paired-end reads. Thus, increased sequencing depth provided only a marginal increase in genome coverage. The alignment quality control results are shown in Table III. The distribution of per-base sequencing depth and cumulative depth distribution in the target regions are presented in Fig. 3. The data approximately followed a Poisson distribution, which showed that the exome-capturing target region was evenly sampled.
SNP analysis
The genotype with the highest probability at a given locus was identified for each individual sequencing sample, and the consensus sequence of the sample was assembled and saved in CNS format. Using the consensus sequence, the polymorphic loci between the identified genotype and the reference were filtered and highlighted; this constitutes the high confident SNP dataset. Following the identification of the SNPs, ANNOVAR was used to perform annotation and classification.
The results revealed that 99.88% of the T2D SNPs were present in dbSNP, and there were 3,010 novel SNPs (Table IV). A total of 485 SNPs were screened, for which the SIFT score was <0.05, and the PolyPhen score was >0.85. These features suggested the pathological nature of the identified genetic variation. Of these 485 SNPs, 480 SNPs were found at exonic regions. The remaining SNPs were at exonic and splicing regions. All the SNPs were nonsynonymous genes. Compared with the 76 loci-containing genes causing an increased risk of T2D, 77 SNP loci were identified in 37 genes (Table V).
InDel analysis
To detect the InDels, the present study used pair-end reads for gap alignment using the mpileup program in SAM tools. Following identification of the InDels, ANNOVAR was used for annotation and classification. Of the 642,189 identified InDels, the percentage that overlapped the dbSNP InDels was 68.52% (Table VI). The length distribution of the InDels in the whole target region and CDS region were also plotted (Fig. 4). The length distribution of InDel in CDS region indicated that peaks are present at 3, 6 and 9 bp length, the InDels with this periodicity are non-framenshift InDels, they have relatively small effect on the genome comparing with frameshift InDels.
SV, CNV and SNV analyses
When aligning the paired-end reads, if a structure variation existed between the sequencing and the reference sequences, the requirements for pair-end alignment, also termed the PE map, may not be met; thus, these anomalous read pairs and soft clip reads were used in the present study to detect SVs. The resulting list of the SVs, that were detected at the whole-genome level are listed in Table VII.
The CNVs of each sample were detected using CNVnator. Following identification of the CNVs, ANNOVAR was used for annotation and classification (Table VIII). Varscan was used to identify specific SNVs by simultaneously comparing read counts, base quality and allele frequency between the healthy individuals and patients with type 2 diabetes. Following identification of the SNVs, ANNOVAR was again used for annotation and classification (Table IX and Figs. 5 and 6).
Analyses of somatic InDels, somatic CNVs and somatic SVs
In the sufficiently covered sites, the initial call was produced in the type 2 diabetes sample and then compared with the normal sample to detect evidence for the event. If there was no evidence to support the InDel event in the normal sample, the site was considered to be a putative somatic InDel. In total, there were 1,249 somatic InDels in 1,000 genome and dbSNP135, 688 in 1,000 genome specific, and 310 dbSNP135 specific. The results of the statistical analyses are provided in Table X. The dbSNP rate was 42.19%, and without heterozygous.
Somatic CNVs correspond to relatively large regions of the genome, which are either deleted and fewer than the normal number, or duplicated and more than the normal number, on certain chromosomes. The results of the somatic CNVs analyses are shown in Table XI, and the somatic CNV overview is plotted in Fig. 7.
In the sufficiently covered sites, the initial call was produced in the type 2 diabetes sample and then compared with the normal sample to detect evidence for the event. If there was no evidence to support the SV event in the normal sample, this event was considered to be a putative somatic SV. The results of the somatic SV statistical analyses are presented in Table XII.
Discussion
In the present study, whole-genome re-sequencing was performed with DNA pooling to investigate T2D in Chinese individuals. In total, 1.44 GB of raw data were generated in a short period of time. Among the data obtained, 3,010 novel SNPs and 120,703 novel InDels were found. In addition, 5,590 SVs, 4,713 CNVs and 13,049 SNVs were identified. There was a significant difference between cases and controls in 1,884 somatic CNVs and 74 somatic SVs. These findings improve current understanding of the genetic basis of T2D and offer insight for future investigations.
Among the identified genes, only rs734312 in WFS1 (with a SIFT score 0.02 and a PolyPhen score of 0.99) suggested a pathologic nature. It was also found that, even in the same genes, the associated loci were different in the present study. Although >30 genetic susceptibility loci have been found in the comparison of 76 genes, the most frequently reported variants have small to moderate effects, and account for only a small proportion of the heritability of T2D, suggesting that the majority of inter-person genetic variation in this disease remains to be elucidated (20).
KCNQ1 (40), UBE2E2 and C2CD4A-C2CD4B (19) have been identified as T2D susceptibility genes in three GWA scans in Japanese individuals. The combined analyses identified GLIS3, PEPD, FITM2-R3HDML-HNF4A, KCNK16, MAEA, GCC1-PAX4, PSMD6 and ZFAND3 as T2D loci reaching genome-wide significance in East Asia (22). PTPRD and SRR have been identified as diabetes susceptibility loci in a study of a Han Chinese population (2). In the present study, the SNP loci in the UBE2E2, PSMD6, ZFAND3 and SRR genes were not found. The results of the present study suggested that, in different patient populations, different genes may confer risks for diabetes, which may lead to more complex study designs for investigating the molecular pathogenesis of T2D.
A simple, but important observation was that DNA pooling provides a highly effective approach for examining the genetic underpinnings of common familial diseases. DNA pooling has been confirmed to be an effective and efficient method to select candidate susceptibility loci for follow-up by individual genotyping (12,13,41). This indicates that the use of GWAS for a large number of cases and controls are technically and financially feasible. Additional findings of particular interest include the large-scale examination of possible genetic variants. The present study demonstrated novel, significant associations, including SNPs, CNVs, InDels and SNVs.
The present study indicated general recommendations, which are relevant to whole-genome re-sequencing using DNA pooling. The first recommendation is associated with the importance of careful quality control. In the present study, 144.3 GB of raw data were generated from the Illumina pipeline, which contained too many Ns or low quality bases. Small systematic differences can readily produce effects capable of obscuring true associations from being identified (42,43). The present study implemented extensive quality control checks to minimize differences in the clean data, alignment and called variants.
The sequencing method used in the present study also resulted in sequence redundancy reaching an average of 35.70-fold. Thus, the consensus sequence accuracy was higher and particularly suitable for calling heterozygous alleles. Whole-genome re-sequencing with DNA pooling technologies is high throughput technique, as one hundred million DNA fragments can be sequenced in parallel on one chip. The Illumina HiSeq 2000 platform from Illumina used in the present study can provide up to 55 GB of high-quality data per day. In this regard, it was possible to undertake comprehensive assessments of the variants within the regions of interest using this high-throughput and time efficient method.
Thirty years ago, James V. Neel (44) labeled T2D as 'the geneticist's nightmare', describing the identification of genetic factors in T2D as challenging. Numerous investigations on candidate genes for T2D have been published; however, the various approaches, including high-throughput gene scanning and gene and pedigree analysis have not been entirely successful in identifying robustly replicating T2D-susceptibility loci. Ultimately, with large samples and worldwide collaboration, novel risk factors for diabetes are likely be identified using whole-genome re-sequencing technology.
Acknowledgments
The authors would like to thank Professor Yong Dai (Department of Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong, China) and Professor Yueying Xiang (Department of Health Management Center, 181st Hospital, Guilin, Guangxi, China) for their helpful comments, and Dr Minglin Ou and Mr Xianliang Hou from the Nephrology Department of 181st Hospital and Guangxi Key Laboratory of Metabolic Diseases Research (Guilin, China) for their technical assistance.
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