Copy number variation of the beta-defensin genes in Europeans: no supporting evidence for association with lung function, chronic obstructive pulmonary disease or asthma

Lung function measures are heritable, predict mortality and are relevant in diagnosis of chronic obstructive pulmonary disease (COPD). COPD and asthma are diseases of the airways with major public health impacts and each have a heritable component. Genome-wide association studies of SNPs have revealed novel genetic associations with both diseases but only account for a small proportion of the heritability. Complex copy number variation may account for some of the missing heritability. A well-characterised genomic region of complex copy number variation contains beta-defensin genes (DEFB103, DEFB104 and DEFB4), which have a role in the innate immune response. Previous studies have implicated these and related genes as being associated with asthma or COPD. We hypothesised that copy number variation of these genes may play a role in lung function in the general population and in COPD and asthma risk. We undertook copy number typing of this locus in 1149 adult and 689 children using a paralogue ratio test and investigated association with COPD, asthma and lung function. Replication of findings was assessed in a larger independent sample of COPD cases and smoking controls. We found evidence for an association of beta-defensin copy number with COPD in the adult cohort (OR = 1.4, 95%CI:1.02–1.92, P = 0.039) but this finding, and findings from a previous study, were not replicated in a larger follow-up sample(OR = 0.89, 95%CI:0.72–1.07, P = 0.217). No robust evidence of association with asthma in children was observed. We found no evidence for association between beta-defensin copy number and lung function in the general populations. Our findings suggest that previous reports of association of beta-defensin copy number with COPD should be viewed with caution. Suboptimal measurement of copy number can lead to spurious associations. Further beta-defensin copy number measurement in larger sample sizes of COPD cases and children with asthma are needed

Meiotic Recombination Hotspots in Humans - Dynamics and Controlling Factors

Meiotic gene conversion has a major impact on genome diversity. Both crossovers and non-exchange conversions cluster into distinct recombination-active regions that we call hotspots. Hotspot analysis in humans has focused on the description of crossover profiles and only few hotspots had been tested for crossover and non-exchange gene conversion. Therefore, very little was known about the frequency and distribution of non-exchange conversions and how well they correlate with crossing over. Six extremely active recombination hotspots were analysed by using small pool PCR approaches on sperm DNA to detect both types of recombinant molecules. Non-exchange conversions were detectable and arose at high frequencies (0.01-0.47%) per sperm, in addition to crossovers (0.2-0.70%). Conversion tracts were short and their distribution defined a steep conversion gradient, centred at the peak of crossover activity and probably marking the zone of recombination initiation. It was also observed that non-exchange gene conversion and crossover frequencies were positively correlated, not just between men at the same hotspot but equally when compared across several hotspots. On average, non-exchange gene conversions spanning a marker close to the centre of the hotspot occurred at 50% of the crossover frequency. Hotspot regulation in cis had been described previously to results in different initiation efficiencies between interacting haplotypes. Preferential initiation on a more active haplotype, in turn leads to the overtransmission of alleles from the less active haplotype. Additional hotspots that showed a phenomenon of biased gene conversion were described in this study, with crossovers and non-exchange gene conversions influenced to the same degree. More unusually, biased gene conversion specifically affecting non-exchange events was also observed at two hotspots. Here single SNP heterozygosities appear responsible for triggering the bias in cis. Crossovers were not affected, which may provide evidence for distinct crossover and non-crossover pathways operating at human hotspots. Inter-individual differences in recombination frequencies between men at a given hotspot established PRDM9 as major trans-regulator of hotspot activity. PRDM9 regulation was characterised at two hotspots activated by specific sets of PRDM9 variants. At both hotspots a sequence motif, proposed to be the PRDM9 binding site in vitro, was not found to be responsible for hotspot activation as had been predicted previously. Curiously, better motif matches were not correlated with higher crossover frequencies, and PRDM9 can in fact activate hotspot without the proposed binding motif

Evolution of the recombination regulator PRDM9 in minke whales.

Funder: Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V.BACKGROUND: PRDM9 is a key regulator of meiotic recombination in most metazoans, responsible for reshuffling parental genomes. During meiosis, the PRDM9 protein recognizes and binds specific target motifs via its array of C2H2 zinc-fingers encoded by a rapidly evolving minisatellite. The gene coding for PRDM9 is the only speciation gene identified in vertebrates to date and shows high variation, particularly in the DNA-recognizing positions of the zinc-finger array, within and between species. Across all vertebrate genomes studied for PRDM9 evolution, only one genome lacks variability between repeat types - that of the North Pacific minke whale. This study aims to understand the evolution and diversity of Prdm9 in minke whales, which display the most unusual genome reference allele of Prdm9 so far discovered in mammals. RESULTS: Minke whales possess all the features characteristic of PRDM9-directed recombination, including complete KRAB, SSXRD and SET domains and a rapidly evolving array of C2H2-type-Zincfingers (ZnF) with evidence of rapid evolution, particularly at DNA-recognizing positions that evolve under positive diversifying selection. Seventeen novel PRDM9 variants were identified within the Antarctic minke whale species, plus a single distinct PRDM9 variant in Common minke whales - shared across North Atlantic and North Pacific minke whale subspecies boundaries. CONCLUSION: The PRDM9 ZnF array evolves rapidly, in minke whales, with at least one DNA-recognizing position under positive selection. Extensive PRDM9 diversity is observed, particularly in the Antarctic in minke whales. Common minke whales shared a specific Prdm9 allele across subspecies boundaries, suggesting incomplete speciation by the mechanisms associated with PRDM9 hybrid sterility

Transmission Distortion Affecting Human Noncrossover but Not Crossover Recombination: A Hidden Source of

Meiotic recombination ensures the correct segregation of homologous chromosomes during gamete formation and contributes to DNA diversity through both large-scale reciprocal crossovers and very localised gene conversion events, also known as noncrossovers. Considerable progress has been made in understanding factors such as PRDM9 and SNP variants that influence the initiation of recombination at human hotspots but very little is known about factors acting downstream. To address this, we simultaneously analysed both types of recombinant molecule in sperm DNA at six highly active hotspots, and looked for disparity in the transmission of allelic variants indicative of any cis-acting influences. At two of the hotspots we identified a novel form of biased transmission that was exclusive to the noncrossover class of recombinant, and which presumably arises through differences between crossovers and noncrossovers in heteroduplex formation and biased mismatch repair. This form of biased gene conversion is not predicted to influence hotspot activity as previously noted for SNPs that affect recombination initiation, but does constitute a powerful and previously undetected source of recombination-driven meiotic drive that by extrapolation may affect thousands of recombination hotspots throughout the human genome. Intriguingly, at both of the hotspots described here, this drive favours strong (G/C) over weak (A/T) base pairs as might be predicted from the well-established correlations between high GC content and recombination activity in mammalian genomes

Transmission distortion affecting human noncrossover but not crossover recombination : a hidden source of meiotic drive

Meiotic recombination ensures the correct segregation of homologous chromosomes during gamete formation and contributes to DNA diversity through both large-scale reciprocal crossovers and very localised gene conversion events, also known as noncrossovers. Considerable progress has been made in understanding factors such as PRDM9 and SNP variants that influence the initiation of recombination at human hotspots but very little is known about factors acting downstream. To address this, we simultaneously analysed both types of recombinant molecule in sperm DNA at six highly active hotspots, and looked for disparity in the transmission of allelic variants indicative of any cis-acting influences. At two of the hotspots we identified a novel form of biased transmission that was exclusive to the noncrossover class of recombinant, and which presumably arises through differences between crossovers and noncrossovers in heteroduplex formation and biased mismatch repair. This form of biased gene conversion is not predicted to influence hotspot activity as previously noted for SNPs that affect recombination initiation, but does constitute a powerful and previously undetected source of recombination-driven meiotic drive that by extrapolation may affect thousands of recombination hotspots throughout the human genome. Intriguingly, at both of the hotspots described here, this drive favours strong (G/C) over weak (A/T) base pairs as might be predicted from the well-established correlations between high GC content and recombination activity in mammalian genomes

Transmission at SNPs showing biased gene conversion in NCOs at hotspots F and K.

<p>Men are numbered as in refs 5, 9 & 37. The combined number of molecules screened in the two orientations is shown for each man. Poisson-corrected numbers of recombinants are rounded up to the next integer; this Poisson-correction was modest, with observed CO numbers increased by a factor of 1.17 and 1.03 for hotspots F and K respectively, and NCO numbers being increased by a factor of 1.14 and 1.06 respectively. There is no evidence for heterogeneity between individuals in the strength of TD in NCOs in favour of F6.1G or K7.4C (hotspot F: <i>P</i> = 0.83, 2×10 contingency table, 9 d.f.; hotspot K: <i>P</i> = 0.24, 2×8 contingency table, 7 d.f.). Marginally significant variation between individuals was noted for COs at hotspot F (<i>P</i> = 0.047, 2×10 contingency table, 9 d.f.) but no significant variation for this class of recombinant amongst the men screened at hotspot K (<i>P</i> = 0.11, 2×8 contingency table, 7 d.f.).</p

No evidence for CO asymmetry.

<p>(A) Least-squares best-fit normal distribution of reciprocal A+B COs for all men combined at hotspot F (left) and hotspot K (right). (B) Transmission frequencies of SNP alleles into reciprocal A+B COs, with 95% CIs calculated by Bayesian analysis. Transmission of the strong allele (C or G) is shown for transition polymorphisms, and transmission of the purine allele is shown (A or G) for transversion polymorphisms. Data for the hotspot F region are derived from all 10 men analysed since they are all heterozygous at SNP F6.1 (left), whilst those for the hotspot K region are from the 8 men heterozygous for SNP K7.4 (right). The two alleles at each of the 20 markers in and around hotspot F show parity in transmission to COs as determined by two-tailed exact binomial tests (all <i>P</i> values>0.05, without Bonferroni correction). Of the 11 markers analysable in the hotspot K region, only the alleles at K8.8 showed deviation from 50% transmission (<i>P</i> = 0.029, two-tailed exact binomial). The disparity at this marker, which is located outside of the hotspot and informative in just 1 man, is not significant if a Bonferroni correction is applied (<i>P</i> = 0.319).</p

Transmission into NCOs at additional SNPs at hotspots F and K shows no conversion bias.

1<p><i>i.e.</i> excluding any NCO event that involves markers F6.1 or K7.4, including co-conversion events.</p

NCO to CO ratio at the driven SNP for hotspots F and K.

<p>NCO to CO ratio at the driven SNP for hotspots F and K.</p

CO and NCO distributions at recombination hotspots F and K.

<p>(A) Combined reciprocal cumulative CO distributions. SNP markers across the assay intervals are indicated by tick marks and local names on the top of the box plots (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004106#pgen.1004106.s009" target="_blank">Table S6</a> for dbSNP identifiers). Data points represent the observed cumulative proportion of COs pooled from reciprocal assays (A+B COs) at each informative marker for a given man. Different coloured symbols represent different men that are numbered as in refs 5 and 9. A total of 1028 COs were characterised from 10 men at hotspot F (mean CO frequency per sperm 0.81±0.41%) and 599 COs from 13 men at hotspot K (mean CO frequency per sperm 0.26±0.1%). A black line shows the best-fit cumulative CO distribution for each hotspot. (B) NCO gene conversion frequency per SNP, averaged over reciprocal assays (A+B NCOs). Mean conversion frequencies were determined by Poisson-approximation with 95% confidence intervals estimated by simulation. The grey shaded area marks what appears to be a background zone of presumably PCR mis-incorporation that results in false-positive single-SNP NCO artefacts that arise at a frequency of one per ∼15000 progenitor molecules tested. Hotspot centres, as defined by CO distributions, are indicated by red lines. Individual graphs showing the NCO gene conversion frequencies for each man in each orientation can be seen in Figures S1, S2, S3.</p