Introduction

The neutrophil is the most abundant granulocyte in human blood and is a central component of the innate immune system. Its granules are loaded with proteins with potent anti-microbial properties1 and the most abundant among these are the three highly similar human neutrophil peptides (HNP1-3) which locate to azurophilic (or primary) granules where they comprise 30–50% of granule protein and 5–7% of total cellular protein2. During phagocytosis of a target microorganism, the primary granules fuse with phagocytic vacuoles in which they generate high concentrations of these alpha defensins1. Defensin hexamers then form pores in the pathogen’s cell membrane leading to permeabilization and cell death3,4.

In the human genome, the nucleotide sequence for the HNP proteins is found in the alpha defensin gene cluster on chromosome 8 (Fig. 1). DEFA1-3 (HNP1-3) and DEFA4 are expressed in neutrophils whilst DEFA5 and DEFA6 are expressed in Paneth cells. HNP1 and HNP3 proteins differ from each other at a single amino acid whilst no gene has been discovered for the shorter HNP2. It is therefore thought that HNP2 is a proteolytic product of one or both of HNP1 and HNP35. DEFA1 and DEFA3 have long been known to be copy number variant6 whilst DEFA4 is only subject to deletion (and not duplication) in the samples from the 1000 genome (G1K) project (Fig. 1). DEFA1 has been in a multi-copy array for 25 million years (whilst DEFA3 is thought to be a human specific variant) and both genes differ in number and location within the array5. Thus, the two genes are viewed as variant repeats in gene array haplotypes and the gene locus is designated as DEFA1A3 by the HUGO Gene Nomenclature Committee.

Figure 1
figure 1

Overview of the defensin-α locus and HTS DEFA1/DEFA3 ratio estimation. Genes in black and pseudogenes in grey. WG: whole genome sequencing. GenomeStrip CNV regions are the regions defined in Handsaker et al.8 which do not capture the true nature of the underlying DEFA1A3 repeat.

Full size image

Most European individuals have 5 or more DEFA1A3 copies per diploid genome7. On a general basis and as one would expect, it has been shown that individuals with high copy number of a gene, express it at a higher level than those with lower copy number8. Although this correlation was not observed for DEFA1A3 in a sample of 111 normal individuals5, it is thought that the innate immune system’s need to rapidly produce large quantities of DEFA1A3 peptides in response to infection probably confers a substantial fitness advantage to individuals with multiple copies of the gene and drives positive selection of gene duplications.

Gene copy number variation (CNV) is a well-known cause of rare genomic disorders, such as several recurrent microdeletions and microduplications that are associated with various neuropsychiatric phenotypes9,10. In those cases, the disease mechanism is usually related to the loss or gain of a single copy of the relevant gene(s). Variation in gene copy number can also contribute to more common human diseases such as psoriasis11 and asthma8,12. Further the relationship between copy number and pathology can be complex, as in the case of the complement C4 gene, where low copy number is associated with susceptibility to systemic lupus erythematosus, whilst high copy number is associated with schizophrenia13,14. Some gene-based CNVs show particularly large variation within and between populations, such as the amylase gene AMY1, which may be present in 2 to 18 copies per diploid genome15,16. In the specific case of DEFA1A3, low alpha-defensin gene copy number has been shown to increase the risk of IgA nephropathy and renal dysfunction17, whilst increased α-defensins levels have been shown to be a marker for schizophrenia susceptibility18 and to be associated with susceptibility to severe sepsis19,20.

Interestingly, this type of genetic variation is often overlooked due to the technical challenge of accurate copy number measurement and this has been proposed as one of the possible causes of the missing heritability paradox of many common disorders21. Due to the high similarity between DEFA1 and DEFA3, specific measurement of their gene copy number is non-trivial and several different approaches for DEFA1A3 CNV analysis have been published5,7,22.

In the present study, we set out to refine existing methods for the measurement of DEFA1 and DEFA3 gene copy number - one based on high throughput sequencing (HTS) and one based on quantitative polymerase chain reaction (qPCR). We then used these methods to generate a comprehensive and accurate overview of DEFA1A3 copy number variation (including the A1/A3 ratio) in a wide range of human populations. Interestingly, our detailed study of this specific locus also led us to interesting general observations with regards to the estimation and the extent of copy number variation in the human genome.

Results

Estimating DEFA1 and DEFA3 copy number using HTS data

We accessed whole genome sequencing data for 2,504 samples of different ethnicities (AFR 661, AMR 347, EAS 504, EUR 503, SAS 489) from the high-coverage phase 3 of the 1000 Genomes (G1K) project and downloaded all reads that mapped to the α-defensin locus (Fig. 1). We estimated DEFA1A3 copy number with GenomeSTRiP and used the allelic ratio for reads covering the sequence difference between DEFA1 and DEFA3 to calculate the gene copy numbers (CN) per diploid genome in each sample. The mean CN across all super-populations is 5.7 for DEFA1 and 1.4 for DEFA3 and all super-populations have means approximately equal to these numbers (Table 1). However, these mean statistics conceal important differences in DEFA1A3 CN distribution between populations, especially regarding DEFA3.

Table 1 DEFA1 and DEFA3 copy number distribution statistics by super-population.
Full size table

First, and most strikingly, individual HG02554 of African-Caribbean ethnicity carries 26 copies of DEFA1A3 (15 DEFA1 and 11 DEFA3, Fig. 2A). Second, the African super-population differs substantially from the other super-populations in that approximately a third of African samples have genotypes with no DEFA3 copies (Table 1 and Fig. 2B), but the samples with the highest DEFA3 copy numbers are also African (Figs. 2A and S2B). We investigated whether the samples with high DEFA3 CN may be due to unreliable estimates of the DEFA3 fraction, but this does not appear to be the case (Fig. S3). We also investigated whether the high DEFA3 CN in Africans may be limited to specific populations, but we found that all 7 African populations have individuals with high DEFA3 CN (Fig. S4). Third, the Asian super-population has high numbers of samples with more than 10 copies of DEFA1 (Figs. 2 and S2A). These are primarily Japanese samples (Fig. S5).

Figure 2
figure 2

HTS copy number estimates by super-population. (A) DEFA1 and DEFA3 copy number for all samples (light grey lines delineate genotypes with total CN of 5, 10, and 15). (B) 2D densities for each super-population. For both (A,B): AFR African (black), EUR European (blue), EAS East Asian (gold), SAS South Asian (green), AMR ad mixed American (red).

Full size image

We can also use the genotypes to make inferences about haplotype structure. We observe many samples with pure DEFA1 genotypes with CN ranging from 3 to 16 implying that pure DEFA1 haplotypes as short as 1 CN and as long as 8 CN exist. We do not observe any samples with a pure DEFA3 genotype which suggests that pure DEFA3 haplotypes are either very rare or non-existent (Fig. 2).

Visualization and characterization of the DEFA1A3 haplotypes

We produced a fully independent validation of our HTS-based DEFA1A3 CN estimates by performing FiberFISH analysis of four selected samples with high DEFA1A3 CN values. The fluorescent probes that hybridise to different parts of the alpha-defensin locus (Fig. 1) disclose the haplotype structure in detail (Fig. 3). The genotype of individual HG02554 is highly surprising because its exceptional DEFA1A3 CN of 26 is not the result of a duplication of the whole repeat array, but rather consists of the extremes of haplotype size with one huge haplotype of 24 CN and one small haplotype of 2 CN (Fig. 3, row 1). It is also evident that genotypes with 15 and 16 DEFA1A3 CN consist of different combinations of haplotypes: 8 + 8, 9 + 7, and 11 + 4 (Fig. 1, rows 2 to 4).

Figure 3
figure 3

Haplotypes by FiberFISH annotated with DEFA1A3 copy number. Probe colours as defined in Fig. 1: DEFA1A3 gene (red), DEFT1P pseudogene (green), left flank (red & blue), right flank (blue only). For HG02554, qPCR estimates match FiberFISH estimates exactly as this FiberFISH sample was used for calibration/scaling of the qPCR estimates.

Full size image

The HTS data can also be used to shed a broader light on the issue of whole locus duplication by plotting DEFA1A3 CN vs DEFT1P CN for all 2504 samples (Fig. S6). We observe that DEFA1A3 CN = DEFT1P CN + 2, i.e. that there are always two partial repeats (lacking DEFT1P) in each genome (Fig. 1). A higher discrepancy would be indicative of more than 2 partial repeats per genome suggesting a duplication of the locus. There is no tendency for samples with high DEFA1A3 CN to have a greater difference between DEFA1A3 CN and DEFT1P CN suggesting that, as for HG02554, high CN genotypes are the result of DEFA1A3 haplotypes with a single high CN defensin locus rather than duplication of the whole locus.

Independent verification of HTS copy number estimates by qPCR

The FiberFISH method provides exact quantification of copy number and reveals the haplotypes, but it is an expensive and time-consuming method that cannot be easily applied to many samples and it does not provide an estimate of the DEFA1/DEFA3 CN ratio. We therefore analysed 69 G1K samples to check the quality of our HTS CN estimates with an orthogonal method (Fig. 4). We generally found a very high correlation between our HTS estimate and the qPCR estimate with all correlations exceeding 96% and the vast majority of estimates deviating from each other by less than 1 CN. The variance of the estimates did not increase as CN increased. Finally, we also checked the consistency of qPCR with itself by comparing DEFA1A3 CN with the sum of the two separate DEFA1 CN and DEFA3 CN assays (Fig. 4D). We found a Pearson correlation of 96.9% and a slight tendency for DEFA1A3 to generate lower CN estimates than DEFA1 + DEFA3. This slight bias is most likely due to the qPCR scaling factors not being completely exact. We conclude that our HTS CN estimates are accurate in estimating both the total DEFA1A3 CN and the DEFA1/A3 CN ratio and thus that they provide an accurate picture of DEFA1A3 genotype variation across populations.

Figure 4
figure 4

Concordance of HTS and qPCR copy number genotyping methods. (A). DEFA1, (B). DEFA3, (C). DEFA1A3, (D). DEFA1A3 vs DEFA1 + DEFA3 (both by qPCR). ρ: Pearson correlation coefficient. Solid line: y = x. Thin lines: y = x + 1 and y = x-1.

Full size image

In the exploratory phase of this work, we tested the qPCR amplifications on the Polymorphism Discovery Resource panel of 24 used by Linzmaier and Ganz22, but found that our qPCR DEFA1A3 CN estimates were substantially and consistently lower than those reported by them (an average discrepancy of 46%) despite the fact that we are using their original primers (Table S5). Given that we find high concordance between our qPCR and HTS estimates (Fig. 4) as well as high concordance between DEFA1A3 and DEFA1 + DEFA3 (both by qPCR), we conclude that the Ganz and Linzmaier primers can be used to provide good estimates of DEFA1 and DEFA3 CN, but that the copy numbers reported in their paper are inaccurate22. This is probably due to the fact that they do not apply a scaling factor to compensate for the differences in efficiency between target and endogenous control amplifications.

The DEFA1A3 locus in archaic humans

The HTS data from archaic humans clearly shows that the DEFA1A3 locus was copy number variant in the Denisova, Altai and Vindija samples (Fig. S8). Further, there is clear evidence of the presence of DEFA3 variation in the Altai and Vindija samples, but we do not observe the DEFA3 variation in the Denisova sample (Fig. S9). However, since this is a single sample, it is not possible to conclude whether the DEFA3 variation was present in Denisova population or not.

Discussion

DEFA1A3 copy numbers – Inter-individual variation

Copy number variation has been investigated genome-wide on large numbers of samples by the Wellcome Trust Case Control Consortium23 and in phase 1 of the G1K project8. Interestingly, in the WTCCC study, the DEFA1A3 CNV was not scored (perhaps due to strict quality control criteria of CNV typing), whilst in the G1K study, the CN distribution for DEFA1A3 was estimated to have a mean of approximately 5 (Fig. S7) whereas we estimate it to be 7.1 in the phase 3 data. The reason for this discrepancy is not the difference between phase 1 and 3, but the fact that the automatic method used to identify the basic unit of the CNV incorrectly identifies the partial repeat as part of the basic unit and leaves the left-most part of the full-repeat out (Fig. 1). As a result, these measurements underestimate the true copy number by 2 copies. This specific issue with DEFA1A3 highlights the importance but also the challenge of correctly identifying the basic unit of a CNV when estimating the copy number of a gene or genomic region. Other CNVs may be affected by a similar issue, in which case, current levels of copy number variation may be underestimated.

Previous work, focused specifically on the CNV at the DEFA1A3 locus, correctly estimated the mean CN5,7 and identified the high fraction of African samples lacking DEFA324. However, the difference in distribution between populations and in particular the non-negligible frequency of DEFA1A3 CN genotypes with 12-16 in African and East Asian populations has not been previously reported (Fig. 2).

Sample HG02554 from an African Caribbean in Barbados with a genotype of 26 CN and haplotypes 24/2 CN (Fig. 3) is an interesting curiosity as it is to our knowledge the longest repeat of a protein coding gene in the human genome. The largest estimated CNV genotype detected genome-wide in the phase 1 of G1K is 15 CN8, implying haplotypes that are most likely shorter. Thus, the defensin-α locus and this specific sample demonstrate that the maximal range of copy number variation for protein coding genes in the human genome is substantially greater than previously thought.

Enhanced DEFA1 and DEFA3 copy number genotyping

A number of techniques have been developed for genotyping DEFA1A3 copy number. Two methods were published almost simultaneously in 2005. Linzmaier and Ganz proposed using primers that were specific to DEFA1 and DEFA3 in qPCR reactions (Fig. 1)22, while Aldred, Hollox and Armour used multiplex amplifiable probe hybridization (MAPH) combined with restriction variant digest ratio assays5. Following the release of the G1K phase 1 whole genome data, Armour proposed using read coverage to estimate CN25, but in its basic form this method does not permit distinction of A1 and A3.

Our results build on and enhance these existing methods. First, we show that Linzmaier and Ganz primers provide good estimates of DEFA1 and DEFA3 CN, but that this requires a scaling factor to be applied to compensate for the differences in efficiency between target and endogenous control amplifications. This qPCR method is simpler to perform than the MAPH-based method proposed by Aldred, particularly if one also requires the DEFA1/DEFA3 ratio. Interestingly, the higher mean DEFA1A3 CN obtained by Linzmaier relative to Aldred in European populations has been remarked upon7 and it was suggested that this may reflect limitations of real-time PCR typing for this locus. We find this to not be the case, as long as correct scaling is applied. This is an important conclusion with regards to the use of qPCR in measuring copy number variation. Second, we propose an enhancement to Armour’s HTS-based read coverage estimate, where re-mapping of defensin-α reads to a reference consisting of a single copy of the DEFA1A3 gene enables accurate estimation of the A1/A3 ratio from the reference and alternative allele depths (Fig. 4). Thus, high throughput methods that enable discrimination between A1 and A3 are now available for analysing the growing number of samples that are whole genome sequenced or for genotyping new samples at the DEFA1A3 locus specifically.

Limitations

The primary limitation of this work is that the qPCR method produces a non-integer CN estimate and we observe only mild clustering around integer copy numbers. Further, when we compare qPCR estimates to either HTS estimates or FiberFISH exact copy numbers, although we observe good concordance between methods with little bias (indicating that the methods are accurate), we see that the error margin is approximately 1 CN (Figs. 3 and 4). As a result, we choose to report CN as a continuous variable rather than attempt to estimate an integer copy number. Use of droplet digital PCR would probably have improved the precision of our estimates and made them more amenable to providing an integer CN estimate, but this technology involves higher expense and reduced throughput. Further, it should be noted that the precision of CNV determination at copy number higher than 6 is limited even for droplet digital PCR methods (figure 4.1 in http://www.bio-rad.com/webroot/web/pdf/lsr/literature/Bulletin_6407.pdf and Fig. 3 in8).

In this work, we use the FiberFISH copy number to scale our qPCR estimates which may be considered to limit the applicability of the method. However, it should be noted that G1K samples sequenced to very high depth (200X) are publicly available (https://www.illumina.com/platinumgenomes.html) and also provide an accurate scaling factor for the qPCR measurements (unpublished data).

Evolution of the α-defensin gene locus

DEFA1 has been present in multiple copies since before the divergence of great apes from gibbons, but chimpanzee, bonobo, gorilla and orangutan all lack DEFA35. The whole genome sequence data from the Denisova, Altai and Vindija samples clearly show that these samples are copy number variant at the DEFA1A3 locus (Fig. S8) which was to be expected given the presence of the DEFA1A3 CNV in apes. Interestingly, the same data also show that the DEFA3 variation was present in the Altai and Vindija samples (Fig. S9). We used our enhanced HTS CN estimation to produce an overview of DEFA3 frequency across populations. We observe that genotypes lacking DEFA3 are common in African populations whereas they are markedly less frequent in other populations (Fig. 2). However, genotypes containing DEFA3 are not rare in African populations and the samples with very high DEFA3 CN are all African (note that the 24 CN haplotype in HG02554 consists of at least 8 DEFA3 copies). Further, African samples carrying DEFA3 are found in all African populations at both low and high CN (Fig. S4). This data does not permit us to draw a firm conclusion on the origin of the DEFA3 variation. However, in the context of the debate surrounding the geographic origins of modern humans26,27,28, it is intriguing to observe that the DEFA3 mutation is present in archaic human remains found outside Africa and that modern non-African genotypes mostly contain copies of DEFA3, whereas approximately one third of modern African genotypes lack a copy of DEFA3.

Materials and methods

Copy number estimation using whole genome sequencing data

We downloaded all whole genome sequencing reads mapping to the α-defensin locus (Fig. 1) for the 2504 high-coverage samples (AFR 661, AMR 347, EAS 504, EUR 503, SAS 489) from phase 3 of the 1000 genomes (G1K) project29 (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage). We also accessed the pre-computed GenomeSTRiP8 metadata for these samples on Google Storage (gs://mccarroll-gs-1000g/md). We estimated DEFA1A3 and DEFT1 copy number for each sample by running GenomeSTRiP (svtoolkit_2.00.1949.tar.gz) with the GSMASKMODE set to MXINC as we found that it was consistent with the results from the default mask mode, but gave more clustering around integer values (full details in the supplementary materials). These are our high-throughput sequencing copy number estimates (HTS CN).

In order to estimate DEFA3 copy numbers, we first extracted all reads from genomic regions containing DEFA1A3 and then mapped them to the fasta sequence of the partial repeat (8:6872895-6876338) using the MEM algorithm of BWA30. We performed joint variant calling using GATK’s HaplotypeCaller at the SNP site that distinguishes DEFA1 from DEFA331. This is position 6,873,603 in HG19 or position 709 in our reference sequence for the partial repeat. For each sample, we extracted the reference and alternative allele depths from the VCF file and used their ratio as an estimate of the relative copy numbers of DEFA1 and DEFA3 (Fig. 1).

Haplotype determination by FiberFISH

We used FiberFISH (fluorescence in-situ hybridisation) to obtain exact quantification of DEFA1A3 copy numbers (full details in supplementary materials). In brief, we produced a probe design (Figs. 1 and S1) and then generated the FiberFISH probe amplicons with the Qiagen LongRange PCR Kit (QIAGEN, Hilden, Germany) followed by shipment to Genomic Vision (Paris, France) for addition of fluorophores (Table S2). We selected four samples with high HTS CN and obtained their B lymphoblastoid cell lines from the NHGRI Repository at the Coriell Institute of Medical Research (HG02554, HG02067, GM19320, GM18533). We then generated cell plugs that were shipped to Genomic Vision (Paris, France) for molecular combing on vinyl-silane treated coverslips followed by fluorescent probe hybridisation and imaging.

Copy number estimation by quantitative PCR (qPCR)

69 samples were selected for qPCR verification of the HTS-based CN estimates in such a way that all CN levels were well represented. This meant that almost all available samples with high HTS CN were selected whereas only a small fraction of available samples were selected at low CN (Table S3). DNA samples were obtained from the NHGRI Repository at the Coriell Institute of Medical Research (Camden, NJ, USA).

To determine the copy numbers of the DEFA1 and DEFA3 genes, we used the primer sequences proposed by Linzmeier and Ganz22. This involved using four sets of primers (Table S4). The DEFA1 and DEFA3 genes differ by one nucleotide only, so to discriminate between them, allele-specific primers were used. The last nucleotide of the forward (plus strand) primers covers the differing nucleotide, and the third to last nucleotide contains a mismatch to increase specificity of hybridization further (Fig. 1). The reverse primer was common for both DEFA1 and DEFA3. In addition to the allele-specific primers, a third primer set was also used, that amplifies both DEFA1 and DEFA3. The myeloperoxidase gene, MPO, was used as the endogenous control gene and the PCR reactions were run as described in the supplementary materials.

We also obtained the 24 DNA samples that were originally genotyped by Linzmaier and Ganz from the Coriell Institute of Medical Research (Polymorphism Discovery Resource panel of 24 - M24PDR) and performed qPCR as for the 69 G1K samples.

Theoretically, if the efficiencies of the DEFAx amplifications and the endogenous control are both 100%, we can use the standard qPCR normalisation methods32 to compute the relative amount of DEFAx and endogenous control \({2}^{({{\rm{C}}}_{{\rm{t}},{\rm{MPO}}}-{{\rm{C}}}_{{\rm{t}},{\rm{DEFA}}})}\) and this number can be scaled by 2 to obtain the number of DEFAx copies per diploid genome. For DEFA1A3, this approach is approximately correct. However, for DEFA1 and DEFA3, although we obtain estimates that are proportional to the true copy numbers, the absolute levels are incorrect. We therefore use the exact FiberFISH copy numbers for sample HG02554 to compute scaling factors for each of the primer sets using the following formula:

$${{\rm{FF}}}_{{\rm{DEFAx}}}^{{\rm{HG}}02554}/{2}^{({{\rm{C}}}_{{\rm{t}},{\rm{MPO}}}^{{\rm{HG}}02554}-{{\rm{C}}}_{{\rm{t}},{\rm{DEFAx}}}^{{\rm{HG}}02554})}$$

where the superscript indicates the sample and FF is the copy number determined by FiberFISH (we estimate \({{\rm{FF}}}_{{\rm{DEFA}}1}^{{\rm{HG}}02554}\) and \({{\rm{FF}}}_{{\rm{DEFA}}3}^{{\rm{HG}}02554}\) by using the HTS estimate of the relative copy numbers of DEFA1 and DEFA3). This resulted in the following scaling factors for the different primer pairs: DEFA1: 3.319, DEFA3: 6.551, DEFA1A3: 1.904. Note how the scaling factor for DEFA1A3 is close to 2 as it should be if the efficiency of the MPO and DEFA1A3 amplifications are both close to 100% whereas the scaling for DEFA1 and DEFA3 is much higher, indicating that the efficiency of the DEFA1 and DEFA3 amplifications is much lower than that of MPO (which was to be expected due to the mismatch at the third to last nucleotide – Fig. 1).

Neanderthal and Denisovan data

We obtained data on archaic humans by downloading the whole genome sequence data for the Denisova33 (http://cdna.eva.mpg.de/denisova/alignments/T_hg19_1000g.bam), Altai34 (http://cdna.eva.mpg.de/neandertal/altai/AltaiNeandertal/bam/AltaiNea.hg19_1000g.8.dq.bam), and Vindija35 samples (http://cdna.eva.mpg.de/neandertal/Vindija/bam/Vi33.19.chr8.indel_realn.bam) and inspected these in the IGV browser36 for presence of DEFA1A3 copy number variation and DEFA3 variation.