Plasmodium falciparum infections. New amplicon sequencing and data analysis protocols are needed for genotyping in epidemiological studies and drug efficacy trials of P. falciparum.Amplicon deep sequencing permits sensitive detection of minority clones and improves discriminatory power for genotyping multi-clone csp and novel marker cpmp was conducted in duplicate on mixtures of parasite culture strains and 37 field samples. A protocol allowing to multiplex up to 384 samples in a single sequencing run was applied. Software “HaplotypR” was developed for data analysis.Targeted sequencing of molecular marker Cpmp was highly diverse (He = 0.96) in contrast to csp (He = 0.57). Minority clones were robustly detected if their frequency was >1%. False haplotype calls owing to sequencing errors were observed below that threshold.msp2, highly multiplexed amplicon sequencing displayed greater sensitivity in detecting minority clones.To reliably detect haplotypes at very low frequencies, experiments are best performed in duplicate and should aim for coverage of >10′000 reads/amplicon. When compared to length polymorphic marker The online version of this article (10.1186/s12864-017-4260-y) contains supplementary material, which is available to authorized users. Plasmodium species. To better understand the epidemiology and infection dynamics of malaria, individual parasite clones are tracked over time to measure the acquisition, elimination and persistence of individual clones in a human host. The incidence of new clones per host serves as surrogate measure for the exposure of an individual and for the transmission intensity in a population [msp1, msp2) [In infection biology of malaria as well as of many other pathogens, detection of minority clones is a crucial task. In areas of high malaria transmission, most infected hosts harbour multiple clones of the same pulation . Identifpulation , 3. For 1, msp2) , 5.P. falciparum infections [Despite their wide use in many malaria research laboratories, length polymorphic markers have important limitations. For example, microsatellite typing suffers from frequent occurrence of stutter peaks, possibly resulting from polymerase slippage on stretches of simple tandem repeats. A cut-off requirement for a minimal peak height (e.g. 33% of the predominant peak ) is requfections .P. falciparum and P. vivax by amplicon deep sequencing: (i) Sequencing of the classical length polymorphic genotyping markers, such as P. falciparum msp1 and msp2 [P. falciparum circumsporozoite protein (csp) or P. vivax msp1 [Deep sequencing of short amplicons has the potential to overcome some of the shortfalls of length polymorphic genotyping markers, in particular the influence of fragment length of a marker on the detectability of minority clones. Earlier studies used two different approaches for genotyping of and msp2 . Alternavax msp1 –11. The vax msp1 . This lavax msp1 . Thus, gIn previous studies, amplicon deep sequencing was performed on two platforms, 454/Roche or Ion Torrent. In the past these technologies have produced longer sequences than the 37 bp reads obtained by the Illumina sequencing platform. Now Illumina MiSeq generates reads of up to 600 bp length . Sequencing error rates of 454/Roche and Ion Torrent technologies were high, owing to insertion and deletion (indel) errors occurring predominantly in homopolymeric regions –16. IlluP. falciparum by amplicon sequencing. The primary aim was to thoroughly assess the analytical sensitivity and specificity of amplicon sequencing in detecting minority clones. In epidemiological studies involving hundreds of samples sequencing costs per sample are crucial. Therefore we designed a highly multiplexed protocol, allowing sequencing of up to 384 barcoded P. falciparum amplicons in a single Illumina MiSeq run. Because multiple concurrent P. falciparum clones may differ greatly in density, sequencing analysis strategies need to identify alleles of very low abundance. To distinguish true minority clones from sequencing errors, quality checks were designed based on replicates of samples and integrated into the sequence analysis pipeline. The newly created data analysis software package was validated using experimental mixtures of P. falciparum in vitro culture strains, and tested on field samples.The present report outlines a strategy and protocols for identifying highly diverse markers for SNP-based genotyping of P. falciparum csp gene (PF3D7_0304600) and gene PF3D7_0104100, annotated in the malaria sequence database PlasmoDB as “conserved Plasmodium membrane protein” (cpmp). Results from these two markers were compared with classical length polymorphic genotyping using the highly diverse marker msp2. Sizes of msp2 fragments amplified for genotyping range from 180 to 515 bp in PNG using published primers  and was prioritized as candidate for a highly diverse amplicon sequencing marker. He in the worldwide dataset was 0.93 for cpmp compared to 0.86 for csp  and 731 for csp . The discrepancy in median sequence coverage was deliberate and resulted from our pooling strategy to underrepresent csp amplicons to prevent their predominance in the sequencing library due to this amplicon’s shorter length . The mismatch rate increased towards the end of sequence reads, up to 11% for forward reads and 54% for reverse reads  were sequenced  and were thus excluded.Defined mixtures of cpmp and up to 1:500 for csp Cut-off settings for SNP calling were defined by a population-based approach. A SNP was required to be dominant  in ≥2 samples. A single dominant occurrence of a SNP is likely caused by amplification or sequencing error.(ii)Cut-off settings for haplotype calling required a haplotype to be supported by ≥3 reads in ≥2 samples (including independent replicates of the same sample). Per haplotype a minimum of 3 reads are needed to distinguish SNPs from sequencing errors, because a consensus sequence cannot be determined from 2 disparate reads alone. Random sequencing and amplification errors would unlikely lead repeatedly to a particular haplotype.(iii)Cut-off settings for calling minority clones required that a minority clone would represent at least 0.1% of all reads of a sample, which corresponds to a detection limit for minority clones of 1:1000. For the current project, the cut-off was justified by the results obtained from artificial mixtures of culture strains, which defined the technical limit of detection for a minority clone. This parameter may be set to more stringent values.Cut-off values for the analysis of sequencing data were defined to support removal of background noise caused by sequencing and amplification errors. The following values represent minimal stringency and can be adjusted to higher stringency to increase specificity in the HaplotypR pipeline:csp the minority clone fell below the cut-off even in the 1:1500 ratio . For marker io Table . No fals:3000. FoThe above criteria were validated on reads from culture strains and primer sequences. The background sequencing error rate at each individual nucleotide position was measured to distinguish sequencing and amplification errors from true SNPs. Mismatch rates of up to 22% was measured in primer sequences  for fragment sizing. Msp2 genotyping was reproducible and consistent between different laboratories  of samples harboured multiple clones . Compared to msp2 the discriminatory power of csp was substantially lower with only 4 csp haplotypes found in 37 samples, He of 0.57 and mean MOI of 1.54. Concordance between csp and msp2 MOI was poor .All samples were genotyped for length polymorphic marker Csp and cpmp haplotypes obtained from 37 field samples were compared between replicates to investigate reproducibility of the molecular and bioinformatic analyses. For both replicates of the field samples the default cut-off criteria for haplotype calling (≥3 reads and minority clone detection limit of 1:1000) were applied. Concordance between replicates was very good with Cohen’s Kappa 0.84  for cpmp and 0.91  for csp. Comparison of replicates permitted to investigate the amount of false haplotype calls. True haplotypes should be detected in both replicates, unless the sequence depth is not sufficient for detecting a minority clone in one of the replicates. Cpmp minority clones that had a frequency > 1.0% of all reads were consistently detected with ≥3 reads in both replicates . No chimeric csp reads were detected in the field samples . The strategy applied for down-selecting highly diverse regions in the genome suggested cpmp as the top candidate. Cpmp is most abundantly expressed in sporozoite stages [cpmp revealed a genetic diversity similar to the length polymorphic region of the widely-used marker msp2. 45 SNPs were observed in the 37 field samples of this study, leading to the designation of 27 haplotypes for marker cpmp. With increasing number of field samples processed, additional rare SNPs and even more haplotypes are likely to be found. The diversity of cpmp was high also in the global MalariaGEN dataset (He = 0.93); its resolution as genotyping marker in other geographic regions remains to be shown. In contrast, marker csp, analysed in parallel to cpmp and also used in earlier studies, showed a limited diversity with only 4 haplotypes detected in 37 field samples. Earlier studies reported similar low diversity for csp in regions of Asia Pacific [csp is not suited to serve as a single genotyping marker in PNG. However, the global diversity of csp according to the MalariaGEN dataset seems to be high (He = 0.86), and high diversity has also been observed in African isolates [Amplicon sequencing showed an excellent resolution when using the novel genotyping marker e stages , but thee stages . In this Pacific . Thus, cisolates .Implementing amplicon sequencing required parallel development of a bioinformatics pipeline. A known problem in sequence analysis is the robust detection of minority clones from a background of experimentally induced artefacts. We addressed this problem with the design of HaplotypR, a software package dedicated to stepwise analyse of sequence reads for samples containing multiple clones. The HaplotypR pipeline can be divided into three steps: In the first step, this pipeline de-multiplexes and clusters raw sequence reads to clusters of related sequences, so called “representative haplotypes”. This step employs Swarm2 software, which expands pools of amplicons  by iteratively joining other pools of amplicons that are separated by a defined number of mismatches  , 23. ThiP. falciparum culture strain 3D7 and from controlled mixtures of strains HB3 and 3D7. On those control samples the impact of amplification and sequencing errors could be assessed. An increased frequency of sequence mismatches relative to the 3D7 reference sequence of up to 22% was observed at a few specific genomic locations including the sequences of amplified primers. To differentiate these sequencing errors from true genotypes of rare minority clones, we defined a SNP calling cut-off where a genotype was required to be dominant  in at least 2 samples. This cut-off is critical to distinguish true positive genotypes that are rare in the population from sequencing errors.Validation of HaplotypR was made possible by reads from serial dilutions of To prevent reporting of false haplotypes, HaplotypR pipeline applies two types of cut-offs: firstly, a cut-off for singleton exclusion, whereby a SNP or haplotype needed to be supported by more than one sample. It is unlikely that these cut-offs would remove true haplotypes, except if the sample size was very small. In this case, it is recommended to amplify and sequence samples in duplicate, as in this study. A true haplotype is expected to be present in both replicates and thus will not get excluded. Secondly, a cut-off for haplotype coverage was defined requiring that a haplotype is supported by a user-defined number of sequence reads. This flexible cut-off can be selected for each marker. The coverage cut-off removes false or weakly supported haplotypes and thus improves specificity. On the other hand, the ability to detect minority clones (i.e. sensitivity) will be limited by a cut-off based on coverage. Sequence reads from a minority clone were detected in all ratios up to 1:3000 in the mixtures of strains HB3 and 3D7. However, due to high background noise, false haplotypes with a frequency of up to 0.01% were also detected, making the definition of a cut-off to remove background noise obligatory. Applying these default cut-offs in HaplotypR decreased minority clone detectability from 1:3000 to 1:1000.In an other publication a parasite density specific cut-off was applied in addition to a default cut-off .The potential of amplicon sequencing for genotyping samples of very low parasitaemia was assessed in serial dilutions of strain 3D7. Sequence reads were retrieved from samples of a parasitaemia as low as 5 parasites/μl, however coverage was below 100 reads for the lowest level of parasitaemia. To reliably genotype samples spanning a wide range of parasitaemias, similar sequence coverage  for all samples is needed. The inexpensive strategy used to adjust amplicon concentrations of individual samples to equal levels prior to pooling for highly multiplexed sequencing still resulted in fluctuation in the sequence coverage, but a commercial DNA normalisation kit may improve equimolar pooling of samples , 26.All samples in this study were sequenced in 2 replicates. This was done to assess the reproducibility of amplicon sequencing method of genotyping very low abundant minority clones, and to investigate the effect of nested PCR cycle number on artefacts. Analysing replicates of field samples revealed that haplotypes with a frequency of >1% were consistently detected in both replicates. In contrast, haplotypes with a frequency of <1% were frequently detected only in a single replicate. If minority clones of <1% frequency are to be reliably detected, amplifying and sequencing two or more replicates for each sample would be essential to call true haplotypes.To detect minority clones with high sensitivity and specificity, samples need to be sequenced at high coverage and in replicates. As sensitivity may be adjusted by sequence coverage, choices have to be made in a trade-off between sequencing costs and sensitivity. The specific genotyping application can guide this choice. For example, in large scale field studies with many samples, a high degree of multiplexing of samples at moderate sequence coverage may be chosen to keep sequencing cost low. Furthermore, a less sensitive approach without performing replicates may be sufficient when detection of very rare minority clones is less of an issue. Another important application of genotyping of malaria parasites is the example of “recrudescence typing” during in vivo drug efficacy trials. To distinguish a new infection from one present as a minority clone prior to drug administration requires highest sensitivity and every clone must be reliably detected. In such cases a sequencing approach with less multiplexing is desired to achieve high coverage and maximal detection of minority clones.cpmp and 731 reads for csp owing to a high number of samples and of markers sequenced in parallel. A total of 352 samples were multiplexed in a single sequence run. Samples simultaneously processed but not included in the present analysis served for an unrelated research question. According to our protocol for PCR-based sequencing library preparation, costs per sample for Amp-Seq were twice that of msp2-CE genotyping [The power of high sequencing coverage was shown for example in a study assessing the subclonal diversity in carcinomas . MinoritPlasmodium parasites, but also widely applied in other fields, e.g. to study diversity of other pathogens, diversity in eco-systems or sequence alteration caused by CRISPER/Cas9 [Targeted amplicon sequencing is not only used for investigating genetic diversity of PER/Cas9 , 28–31. PER/Cas9 , 30. DatPER/Cas9 , 22, 24 PER/Cas9 , 28.cpmp was 1490 and for csp 731). This permitted detection of minority clones at a frequency of >0.3% of the total parasite load. To robustly detect minority clones at 0.1% frequency, a coverage of 10′000 reads is recommended. In addition, experiments should be performed in duplicate. The need to detect such low-abundance clones depends on the specific research question, which should guide experimental decision on number of samples and multiplexed amplicons as well as on the desired sequence depth.Short amplicon sequencing has the advantage that no multi-locus haplotype reconstruction is needed, as all SNPs are linked by a single paired-end read. This allows the reliable analysis of samples of very high MOI, a prerequisite for genotyping in areas of high malaria endemicity. An additional strength of this method is that previously undescribed or newly evolving haplotypes can be captured without any adjustment of the typing methodology or the HaplotypeR pipeline. The main limiting factor for the detection of minority clones was the sequence depth per sample. The sequence coverage in the present study was in the order of 1000 reads , were screened to identify highly diverse markers for SNP-based genotyping [P. falciparum genomes were divided in 200 bp windows and He was calculated for each window as follows: n is the number of clones and pi the frequency of allele i. Annotated genes (PlasmoDB v11.0) that overlapped with windows of high heterozygosity were selected for further evaluation. Genes belonging to gene families, such as var, rifin, stevor and surf families, were excluded from the list, as well as genes with high heterozygosity that is usually caused by length polymorphism  [e values for amplicons are shown in Additional file Primers for marker er.html) . Primer The sequencing library was generated by 3 rounds of PCR with KAPA HiFi HotStart ReadyMix PCR Kit as described earlier . A firstPCR products were purified with NucleoMag beads. The expected fragment size of the sequencing library was confirmed by Agilent 2200 Tapestation System. DNA concentration of the sequencing library was quantified by Qubit Fluorometer (Thermo Fisher Scientific). Sequencing was performed on an Illumina MiSeq platform in paired-end mode using Illumina MiSeq reagent kit v2 (500-cycles) together with a Enterobacteria phage PhiX control .phiX174 genome (Accession: J02482.1) for assessing the quality of the sequencing run and calculating sequencing error per nucleotide position. Reads were then de-multiplexed to separate individual samples and different genotyping markers   to the pers Fig. . Primer P. falciparum reference sequences of strains 3D7 and HB3 . Rates of mismatches to primer and reference sequences were calculated for each individual sample at each nucleotide position. A SNP was defined as a nucleotide position with a > 50% mismatch rate in the sequence reads from at least two independent samples.For analysis of control samples, fused reads were mapped to the corresponding primers and For prediction of haplotypes, fused reads were clustered individually per sample with Swarm2 software (parameters: boundary = 3 and fastidious mode) , 23. Thehttps://github.com/lerch-a/HaplotypR.git).The full analysis pipeline, named HaplotypR, was implemented as R package and is illustrated in Figure Detectability of minority clones was estimated by bootstrapping from the reads of the control samples with defined HB3-3D7 strain ratios. Reads were randomly sampled with replacement until the required coverage was reached. These resampled set of reads were processed in the same manner as the original samples using HaplotypR. For resampling only sequence files from HB3-3D7 mixtures were used that had a coverage of >3000 reads.Additional file 1:Supporting information. Supplementary text, figures and tables. (DOCX 2111 kb)Additional file 2:e windows of whole P. falciparum genome. (XLSX 96 kb)List of 200 bp HAdditional file 3:List of haplotype calls. (XLSX 67 kb)


Osteoporosis individual susceptibility is determined by the interaction of multiple genetic variants and environmental factors. The aim of this study was to conduct SNP-SNP interaction analyses in candidate genes influencing heel quantitative ultrasound (QUS) parameter in early adulthood to identify novel insights into the mechanism of disease.The study population included 575 healthy subjects . To assess bone mass QUS was performed to determine Broadband ultrasound attenuation . A total of 32 SNPs mapping to loci that have been characterized as genetic markers for QUS and/or BMD parameters were selected as genetic markers in this study. The association of all possible SNP pairs with QUS was assessed by linear regression and a SNP-SNP interaction was defined as a significant departure from additive effects.ESR1 and LRP5 genes respectively, revealed the lowest p value after adjusting for confounding factors  = 14.289 . In addition, our model reported others such as TMEM135-WNT16  = 9.101 , ESR1-DKK1  = 13.641  or OPG-LRP5  = 8.724 . However, none of the detected interactions remain significant considering the Bonferroni significance threshold for multiple testing (p<0.0001).The pairwise SNP-SNP analysis showed multiple interactions. The interaction comprising SNPs rs9340799 and rs3736228 that map in the ESR1 and LRP5 genes, that did not reach statistical significance. Although our results do not support a relevant genetic contribution of SNP-SNP epistatic interactions to QUS in young adults, further studies in larger independent populations would be necessary to support these preliminary findings.Our analysis of SNP-SNP interaction in candidate genes of QUS in Caucasian young adults reveal several interactions, especially between  Osteoporosis represents an important public health problem worldwide that triggers near 9 million fractures annually . Its preAn important determinant of osteoporosis risk later in life is the peak bone mass (PBM) . EnhanciRecently, it has been demonstrated that QUS predicts fracture independently of BMD , 10. QUSTo identify the genetic factors underlying osteoporosis susceptibility, extensive genetic studies have been conducted reporting association of several genetic variants with different bone phenotypes –18. HoweWNT16  [RSPO3 (rs774121) [LRP5 (rs3736228) [WTN16, RSPO3 and LRP5 are genetic factors that determine bone mineralization in young adults [The independent association between single nucleotide polymorphisms (SNPs) in 2707466) , 28, 29,s774121)  and LRP53736228)  genes ang adults .Taking into consideration all this evidence, and bearing in mind that it is likely that interactions of common variants at different loci are influencing QUS traits, the aim of this study was to identify SNP-SNP interactions between SNPs independently associated with QUS, that could contribute to QUS traits variation in early adulthood.A total of 575 Caucasian healthy individuals  composed the study population after giving written informed consent. Local ethics committees approved the study that was conducted following the Declaration of Helsinki. Were excluded from the study those individuals who reported therapy with hormonal contraceptives, diseases of metabolic or endocrine systems and history of bone disease.2). Physical activity (PA) was quantified using the short form and self-administered version of the International Physical Activity Questionnaire (IPAQ) [A body composition analyzer (TANITA BC-418MA) was used to estimate weight, fat mass and lean mass to the nearest 0,11 Kg. Height measurement was performed with a Harpenden stadiometer (Holtain 602VR®) to the nearest 0,1 cm. Body mass index (BMI) was calculated as weight divided by height squared (kg/me (IPAQ) . DietaryUltrasound measurements  were performed using the CUBA clinical ultrasound bone densitometer  at the right calcaneus to assess bone mass. This localization was selected on the basis of its accessibility and its high content of trabecular bone . To guarA total of 32 SNPs mapping to loci that have been characterized as genetic markers for QUS and/or BMD parameters were selected.DNA was extracted from saliva samples collected from all study participants with the 500 Collection Kit .Genotyping was carried out using a custom design TaqMan OpenArray . Genotyping plate containing a predesigned TaqMan genotyping assay for every polymorphism was conducted in the Genomic and Genotyping unit of GENYO centre . The amplification reaction was performed following the conditions recommended by the manufacturer. For detection of the fluorescence and interpretation of the genotyping results the QuantStudio 12 K Flex Real-Time PCR System (Applied Biosystems) was used.All the Taqman assays included in the array showed a genotyping call rate above 95%. The accuracy of the genotyping process was verified including in all plates negative controls and duplicate samples. We observed 100% reproducibility.For all analyses the genotype of each SNP was encoded as 0, 1, 2, where 0 and 2 denotes homozygotes with major allele and minor allele respectively and 1 codes heterozygotes.2 test was used to assess the Hardy-Weinberg equilibrium (HWE). First, to reduce redundancy between SNPs, only one SNP out of all SNPs in strong pairwise linkage disequilibrium (r2 > 0.90) was kept, leading to the final inclusion of 30 SNPs (rs2707466 and rs7988338 were excluded) : βinteraction = βobserved – βexpected with βexpected = βSNP1 + βSNP2, where βobserved is the observed effect of the concomitant carriage of SNP1 and SNP2 (versus no carriage) in a bivariate linear regression, βSNP1 is the effect (β coefficient) of SNP1 in the absence of SNP2, βSNP2 is the effect of SNP2 in the absence of SNP1 in the same bivariate regression framework (bivariate refers only to the number of genetic markers (SNPs), as non-genetic covariates have systematically been included for all analyses). The results were reported as βinteraction . To all associations, the highly conservative Bonferroni correction considering the number of tested SNPs was applied. The cut-off value for significance was set at p<0.0001 (0.05/435). The STATA software was used to perform statistical analyses .The Pearson’s goodness-of-fit χd) Table . All posThe characteristics of the 575 study subjects have been published previously . Table 1ESR1 and LRP5 genes respectively, revealed the lowest p value (0.001) after adjusting for sex, age, BMI, physical activity and calcium intake. This interaction amounted to a β (95% CI) = 14.289 . Interestingly, we found several interactions between different polymorphisms in these genes, further supporting the presence of epistasis between these two loci . However, none of the pairwise SNP-SNP tests reached the significant cut off p value considering the Bonferroni correction for multiple testing (p = 0.0001).The pairwise SNP-SNP interactions tested by linear regression showed multiple interactions Table . The intWNT16; rs3736228 and rs2306862 in LRP5; rs2982552 and rs3020331 in ESR1; rs2982552 and rs9340799 in ESR1).On the other hand, our analyses revealed SNP-SNP interactions within the same genes . Even though these data suggest that epistatic SNP-SNP interactions does not influence heel ultrasound measurements in young Caucasian adults, the possibility of a false negative due to a limited statistical power cannot be excluded.This study is the first to investigate SNP-SNP interactions with heel QUS trait. Our findings identified several SNP-SNP interactions, highlighting that observed between rs9340799 in ESR1 gene, which has been associated with different osteoporosis related traits in previous studies [ESR1 gene and although this intron may contain regulatory elements, the functional implication of this genetic variant remains unknown. In addition, low-density lipoprotein receptor-related protein 5 (LRP5) codifies a protein belonging to the Wnt canonical signal, which regulates osteoblast and osteocyte function [LRP5 is still unknown [Oestrogen action in bone are mediated mainly through ERα codified by the  studies . The pos studies . These b studies . The rs9function . Similar unknown , 38.ESR1 and LRP5 genes in bone physiology. Although ESR1 and LRP5 develop different roles in skeletal homeostasis, they could be interacting in bone cell biology through synergistic or antagonistic actions. It could be hypothesised that individuals carrying risk alleles at rs9340799 and rs3736228 SNPs have a lower expression of both ESR1 and LRP5, which could lead to an impaired bone mass. As we have analysed a cohort including only young adults, our findings also may suggest that the interaction between rs9340799 and rs3736228 SNPs might be implicated in bone mass accrual. Although previous studies have reported the individual contribution of these polymorphisms to osteoporosis-related phenotypes in early adulthood [The results of this study could be suggestive of a well-regulated cross-talk between dulthood , 38–41, LRP5 and ESR1, our model suggested others such as TMEM135-WNT16, ESR1-DKK1 or OPG-LRP5. Interestingly, all of these genes belong to well characterize pathways implicated in the complex mechanisms that regulate bone formation. Thus, a novel line of research would be very interesting to elucidate how they may be interacting at the molecular level.Besides the interaction between WTN16, LPR5 and ESR1. Thus, beyond the individual effect of SNPs in a gene, epistatic SNP-SNP interactions in the same gene occur and could be a potential factor contributing to the unexplained heritability of bone mass acquisition.On the other hand, SNP-SNP interactions between variants that were not in LD within the same gene were observed. That was the case of genes p value threshold for multiple testing (p<0.0001). Thus, a limitation of the present study could be the population size included that would not reach enough statistical power to detect epistatic SNP-SNP interactions with weak effect. Therefore, in order to support our preliminary results, further studies including larger population size and meta-analysis are needed. On the other hand, the current approach considered only two-locus interactions and therefore more complex interactions between three or more genetic markers are unknown. Again, larger studies are required to confirm our findings and to elucidate mechanisms by which the genetic interaction between these genes influences quantitative bone phenotypes in early adulthood.On the other hand, it could be hypothesised that the effects of SNP-SNP interactions would be weak and therefore, a very large population size would be needed to detect associations surpassing the ESR1 and LRP5 genes, that did not reach statistical significance. Although our results do not support a relevant genetic contribution of SNP-SNP epistatic interactions to QUS in young adults, further studies in larger independent populations would be necessary to support these preliminary findings.Our analysis of SNP-SNP interaction in candidate genes of QUS in Caucasian young adults reveal several interactions, especially between


With the development of SNP chips, SNP information provides an efficient approach to further disentangle different patterns of genomic variances and covariances across the genome for traits of interest. Due to the interaction between genotype and environment as well as possible differences in genetic background, it is reasonable to treat the performances of a biological trait in different populations as different but genetic correlated traits. In the present study, we performed an investigation on the patterns of region-specific genomic variances, covariances and correlations between Chinese and Nordic Holstein populations for three milk production traits.Variances and covariances between Chinese and Nordic Holstein populations were estimated for genomic regions at three different levels of genome region  using a novel multi-trait random regression model which uses latent variables to model heterogeneous variance and covariance. In the scenario of the whole genome as one region, the genomic variances, covariances and correlations obtained from the new multi-trait Bayesian method were comparable to those obtained from a multi-trait GBLUP for all the three milk production traits. In the scenario of each chromosome as one region, BTA 14 and BTA 5 accounted for very large genomic variance, covariance and correlation for milk yield and fat yield, whereas no specific chromosome showed very large genomic variance, covariance and correlation for protein yield. In the scenario of every 100 SNP as one region, most regions explained <0.50% of genomic variance and covariance for milk yield and fat yield, and explained <0.30% for protein yield, while some regions could present large variance and covariance. Although overall correlations between two populations for the three traits were positive and high, a few regions still showed weakly positive or highly negative genomic correlations for milk yield and fat yield.The new multi-trait Bayesian method using latent variables to model heterogeneous variance and covariance could work well for estimating the genomic variances and covariances for all genome regions simultaneously. Those estimated genomic parameters could be useful to improve the genomic prediction accuracy for Chinese and Nordic Holstein populations using a joint reference data in the future.The online version of this article (doi:10.1186/s12863-017-0491-9) contains supplementary material, which is available to authorized users. The Chinese Holstein has been formed in the process that the imported Holstein bulls from Europe and North America were crossbred with local yellow cattle, and the crossbred cows were continuously back-crossed with imported Holstein bulls , 2. TherBecause of different production systems between China and Nordic countries, some genes may show significant different effects for the same trait between Chinese and Nordic Holstein populations, i.e., genotype by environment interactions. The different genetic effects on the same trait between two populations can be reflected by the patterns of genomic variances, covariances and correlations across genome, which can be detected by an analysis where a given biological trait in Chinese and Nordic Holstein populations is considered as two traits. On one hand, knowing these genomic parameters of different genome regions in two populations will give a better opportunity to understand genetic architectures of traits of interest, On the other hand, these genomic parameters can be used to improve the accuracy of genomic prediction for traits of interest in both populations when using a joint reference population.With the availability of SNP chips and genome sequencing, SNP information has offered a possibility to study genetic architecture of complex traits. Using SNP data, it is possible to disentangle the pattern of genomic variance and covariance across the whole genome. However, there are few literatures to report genomic variances, covariances and correlations for different genome regions by using SNP information. Recently, Janss  has propTherefore, the objective of the present study is to investigate region-specific genomic variances in Chinese and Nordic Holstein populations as well as region-specific genomic covariances and correlations between the two populations for three milk production traits, using the novel multi-trait Bayesian method.As shown in Table 2) in Table The proportion of additive genetic variance to phenotypic variance (hR2=0.97) with the length (in bp) of individual chromosome, while in the Nordic population, BTA 1, BTA 6, BTA 14, and BTA 20 explained larger proportions of genomic variance  than other BTAs.Figure R2 = 0.80) with the length of individual chromosome. However, for both MY and FY, similar to the pattern of genomic variance, BTA 14, BTA 5 and BTA 20 explained much larger proportions  than other chromosomes. As seen in Fig. As presented in Fig. Besides the scenario of each chromosome as one genome region, we also divided the whole genome into 430 regions with every adjacent 100 SNP as one genome region. The distribution of proportions of genomic variances explained by each region of 100 SNP for three traits is shown in Fig. Figure The present study for the first time reported the patterns of genomic variance, covariance and correlation between the Chinese and Nordic Holstein populations for three milk production traits. The patterns of those genomic parameters were investigated at different levels of genome regions, i.e., the whole genome as one genome region, each chromosome as one region and every 100 SNP as one region, using the novel MT-Bayesian rrBLUP model. The results showed that the MT-Bayesian rrBLUP worked well for estimating genomic parameters for different regions in the genome simultaneously. As expected, the results showed that different regions explained different amounts of genomic variance and covariance as well as different degrees of genomic correlation.Both MT-GBLUP and MT-Bayesian rrBLUP were used to estimate genomic variance, genomic covariance and residual variance between Chinese and Nordic Holstein populations for three milk production traits. The total genomic variance and covariance as well as genomic correlation estimated from MT-GBLUP were slightly higher than those from MT-Bayesian rrBLUP without dividing the genome into regions. Although MT-GBLUP and MT-rrBLUP without dividing regions were equivalent, the two models used different algorithms. The MT-GBLUP used AI-REML algorithm while MT-rrBLUP used MCMC algorithm. The two different algorithms might lead to slight difference in estimates of variance and covariances, especially when the distribution of the estimates was deviated from a normal distribution. In fact, the difference was very small, compared with the size of the standard errors. MT-Bayesian rrBLUP had the advantage to estimate region-specific variance and covariance for all regions simultaneously. In principle, MT-GBLUP also can estimate region-specific variance and covariance for all regions simultaneously by taking each region as one component in the model, but it has a very high computational demand, and is difficult to reach convergence due to too many components in the model. Another approach to use MT-GBLUP to estimate region-specific variance and covariance is to take a particular region as one component and the others as one component in the model, and thus obtain variance and covariance for one region in the model in one run. But this is still a time-consuming approach. For example for MY, although the computation time for MT-GBLUP is much shorter than MT-rrBLUP (3 h vs 42 h), the time for MT-GBLUP with every 100 SNP as one region is much longer than MT-rrBLUP with every 100 SNP as one region (10 h* region number vs 42 h). Thus it is difficult to use MT-GBLUP to estimate regional specific variance and covariance with every 100 SNP as one region because of very time consuming. It was observed that with the number of genome regions increased, total genomic variance and covariance derived from each region decreased (Table diacylg-lycerolacyltransferase 1 (DGAT1) on BTA 14 has a very large effect and segregates in the Holstein populations [DGAT1 plays an important role on these three traits  showed much higher genomic variance, covariance and correlation than other regions for MY and FY  gene, presented much higher genomic variance, genomic covariance and genomic correlation for MY in this region than other regions on BTA 20  gene on BTA 20 has a large effect on MY in Holstein populations [Besides BTA 14, BTA 5 and BTA 20 also showed a relative high genomic covariance and correlation which speculated that some QTL regions had the same effects one those two chromosomes in both populations. Among all 100 SNP regions on BTA 5, two consecutive regions  on BTA 19 for MY and 10 regions on different chromosomes for FY showing highly negative correlations . The way to partition the genomic variance into chromosomes has also been reported by Jensen et al. . For theUsing a weighted G matrix in a single-trait GBLUP model to improve the accuracy of genomic prediction has already been reported, in which the estimated SNP variances are used as a weighting factor for building a trait-specific G matrix , 17. LikThis study revealed the patterns of genomic variance, covariance and correlation for genomic regions across the whole genome between Chinese and Nordic Holstein populations for three milk production traits. BTA14 and BTA 5 showed very large genomic variance, covariance and correlation for MY and FY than other chromosomes, whereas no specific chromosomes showed very large genomic variance, covariance and correlation for PY. In scenario of every 100 SNP as one genome region, most regions explained <0.50% of genomic variance and covariance for MY and FY, and most regions explained <0.30% for PY. A few regions showed highly negative genomic correlations, e.g., one important region on BTA8 for FY. These regions can be considered as candidate regions accounting for interaction between genotype and production environment for MY and FY. The pattern of genomic variance, covariance and correlation across genomic regions could be useful for improving multi-population (or multi-breed) genomic prediction.In this study, both Chinese and Nordic Holstein cattle were genotyped with the Illumina BovineSNP50 BeadChip . For each population, we firstly removed SNP with unknown positions and SNP on Bostaurus (BTA) X, and then imputed missing genotypes using the software Beagle . After thttp://www.landbrugsinfo.dk/Kvaeg/Avl/Sider/principles.pdf) obtained from Nordic Genetic Evaluation. Thus, the DRP had different scales in two populations.The Chinese Holstein population consisted of 237 genotyped progeny-test bulls and 6076 genotyped cows, and the Nordic population included 5244 genotyped progeny-test bulls. Three milk production traits, i.e., milk yield (MY), fat yield (FY) and protein yield (PY), were analyzed. In the analysis, deregressed proofs (DRP) were used as phenotypes for all three traits. The DRP of Chinese Holstein bulls and cows were derived from the estimated breeding values (EBV) in original scale obtained from Dairy Association of China, and the DRP of Nordic Holstein bulls were derived from the EBV in standardized scale  for the population i ; ui was the overall mean; ei was the random residual and was uncorrelated between two populations. It was assumed that Di was a diagonal matrix with weights of the residual variance [W was a matrix with SNP genotype covariates. The elements in W were -2pk for genotype A1A1, A1A2 and A2A2, where pk is the frequency of minor allele A2 at SNP k; ai was a vector of SNP effects for the population i. SNP effects across populations were correlated by using the following hierarchical models for SNP effects:Similar to the single trait ridge regression BLUP (rrBLUP) model which assumes constant variance for all SNP, the logical assumption in the multi-trait rrBLUP (MT-rrBLUP) is to assume constant variance and covariance for all SNP. The MT-Bayesian rrBLUP model (it was referred to the new multi-trait Bayesian method above because it performed using the Bayesian Markov chain Monte Carlo(MCMC)) was described as follows :1\documevariance ; W was ari * s contributed to the covariance between SNP effects across populations; the “residual SNP effect” αi* was taken as uncorrelated across populations. The modeled variance and covariance for each SNP were worked out as a1, a2) = cov = r1 ∗ r2. Thus, the total genomic variance for the population i was The prior distributions were assumed as yi, ui, and ei were the same as model 1; ngroup was the total number of genome regions; Wj was a matrix with SNP genotype covariates for the region j; aij was a vector of SNP effects for the region j in the population i. For the region j, SNP effects across populations were correlated and were formulated by the following hierarchical models:In order to calculate genomic variances, covariances and correlations for different genome regions, multi-trait Bayesian model proposed by Janss  was exteThe prior distributions were assumed asaj1, aj2) = r1 * r2 + rj1 * rj2. Therefore, the total genomic variance for the region j in the population i was pik was the frequency of minor allele at marker locus k in the population i. The proportion of genomic variance and covariance explained by each region were calculated as Vg1j,2j/∑j = 1ngroupVg1j,2j, respectively.Thus, the genomic variance for each SNP in region j in population i  was worked out as www.bayz.biz). The Markov chains were run for 50,000 cycles of Gibbs sampling, and the first 20,000 cycles were discarded as burning in. After this, every 20th sample of the remaining 30,000 cycles was saved for the posterior analysis, and then median value was considered as the estimate of each unknown parameter. The posterior standard deviations of estimated genetic parameters across 30,000 cycles were denoted as standard error.The analysis of MT-Bayesian rrBLUP model was carried out using the BAYZ software  was used to compare with the MT-Bayesian rrBLUP model with regard to overall variance and covariance. The MT-GBLUP analysis was carried out using average information restricted maximum likelihood (AI-REML) algorithm implemented in the DMU package . The modVanRaden ; e1 andvariance .


SNPs suggested a low hybridization rate, with 3.5% of wildcat individuals being categorized as F1, F2, or backcrosses to either parental taxon. We report that results based on a set of SNPs were more consistent than on a set of 14 microsatellite markers, showed higher accuracy to detect hybrids and their class in simulation analyses, and were less affected by underlying population structure. Our results strongly suggest that very high hybridization rates previously reported for Central Europe may be partly due to inadequate choice of markers and/or sampling design. Our study documents that an adequately selected SNP panel for hybrid detection may be used as an alternative to commonly applied microsatellite markers, including studies relying on noninvasively collected samples. In addition, our finding of overall low hybridization rates in Central European wildcats provides an example of successful wildlife coexistence in human‐dominated, fragmented landscapes.Hybridization between wild species and their domestic congeners is considered a major threat for wildlife conservation. Genetic integrity of the European wildcat, for instance, is a concern as they are outnumbered by domestic cats by several orders of magnitude throughout its range. We genotyped 1,071 individual wildcat samples obtained from hair traps and roadkills collected across the highly fragmented forests of western Central Europe, in Germany and Luxembourg, to assess domestic cat introgression in wildcats in human‐dominated landscapes. Analyses using a panel of 75 autosomal  First, domestic cats are virtually omnipresent throughout the wild species' range and even surpass it in numbers, presumably making encounters between the two species and introgression highly likely. In Germany, for example, the domestic cat population is estimated to be around 11.8 million individuals , and only a few studies are based on additional noninvasively collected samples  or, more recently, on genomewide SNPs  What is the rate of hybridization in wildcats in a highly fragmented, human‐dominated landscape such as Central Europe? (ii) What is the primary direction of hybridization and introgression between wild and domestic cats? and (iii) To what degree do different approaches  may affect the conclusions regarding introgression rate estimates and, if such is the case, which of these factors can explain diverging estimates of hybridization rates in Central European wildcats?22.1n = 27) in Hofheim am Taunus, Germany. Tissue samples were stored in 96% ethanol, and blood samples were preserved in EDTA. Hair samples were collected opportunistically with lure sticks  and hair samples .Samples included tissue, blood, and hair Table . Tissue 2.2DNA from roadkill samples was extracted using the Qiagen Blood and Tissue Kit  following the manufacturer's instructions. DNA from hair was obtained in a dedicated laboratory for samples with low DNA quantity using the Qiagen Investigator Kit with an additional 5‐min incubation step at the final elution step.KX161418‐KX161423), while 26 others had been observed previously  using primers LF4  were kept for further analyses and, in the case of the hair samples, consensus genotype construction. Individual assignment was conducted using an R custom script. Individuals' consensus genotypes with >75% amplification success and <20% ADO rates across all loci were kept for further analyses. A 100% amplification success indicates that all genotype replicates which were assigned to the same individual showed allele information at all 14 loci, whereas a dropout rate of zero indicates identical alleles across all genotype replicates belonging to one individual. Repeated detections of the same genotype were assumed to be the same individual and were removed from further analyses.Samples which yielded a single cat haplotype were then genotyped for 14 microsatellites using the polymerase chain reaction (PCR) in four multiplexes: (i) FCA8, FCA171, FCA571, and FCA124; (ii) FCA149, FCA170, FCA88, and FCA275; (iii) FCA364, FCA132, and FCA576; and (iv) FCA232, FCA347, FCA567 , were analyzed with a 96 genomewide SNP panel  on a 96.96 Dynamic Array Chip for Genotyping (Fluidigm), as developed by Nussberger, Wandeler, & Camenisch,  using a Bayesian clustering method and probabilistically assigns individuals to populations without using sampling location information. We performed ten runs for each value of K (from 1 to 10) using the admixture model with correlated allele frequencies  if the assignment value (qi) was ≥.75 to minimize the number of unassigned individuals. Individuals with assignment of qi)( < .75 to any cluster and qi). Individuals which could not be assigned to any of the groups ( < .75 to any cluster and qi)( < .19 to the domestic cat cluster) were considered to be potentially admixed between the eastern and western wildcat clusters. The assignment threshold was set lower than qi)( thresholds qi)( ≥ .75 and qi). We used Arlequin 3.5 . Haplotypes were assigned to wildcat or domestic cat when they were found in a minimum of 10 individuals, the majority of which assigned to either wildcat or domestic cat based on the individuals' genotypes, according to NewHybrids results based on the SNP dataset; if a haplotype was found in <10 individuals, the subspecies was not specified. A statistical parsimony network was obtained using TCS 1.21  and NewHybrids (qi). We identified 154 genotypes among the individuals that assigned to the western cluster and 130 genotypes among those that assigned to the central cluster. We then used each wildcat group of genotypes separately to generate different hybrid classes with 77 empirical genotypes selected from our sample of domestic cats. We generated 200 individuals of each of the following categories: parentals, F1s, F2s, and first and second backcrosses to either parental subspecies. For SNP data simulations, we used the same individuals as selected above, but SNP genotypes from the two wildcat populations  were merged, as no structure was detected in wildcats using the SNP markers. Then, simulated hybrid classes were generated as described above.In order to investigate the power of our markers to detect wildcat and domestic cat hybrid classes, we generated hybrid genotypes using Hybridlab . We graphically displayed the individual assignment probabilities for Structure qi), but only for 3The final dataset included 1,071 individuals which were successfully genotyped using 14 microsatellite markers and 589 individuals using 60 SNP markers. The roadkill dataset analyzed with microsatellites consisted of 536 individuals, with a mean amplification success of 95% and a mean ADO rate of 2%; in the case of the SNPs, the mean amplification success was also 95%. The hair trap dataset analyzed with microsatellites comprised 535 individuals, and the success rates resulted in a mean amplification success of 96% and a mean ADO rate of 10%.3.1n = 536 roadkill; n = 535 hair traps) indicated that the greatest likelihood was for K = 3 ( < .25 to the domestic cat cluster), but they could not be clearly assigned to the western or the central cluster (qi)( ≤ .75 to either), and 35 individuals were presumably admixed wildcat and domestic cat as they had similar probability of belonging to a wildcat or the domestic cat cluster.Structure analyses of all samples genotyped with microsatellite markers . The individuals above which assigned to either a wildcat or a domestic cat cluster (qi)( ≤ .75) based on microsatellite data, also assigned to a wildcat or domestic cat cluster, respectively, based on SNP data. In addition, we found six individuals presumably admixed between wildcat and domestic cat (qi)( ≤ .75 to any of the two clusters).For the roadkill dataset and the samples from the veterinary clinic genotyped using SNPs, STRUCTURE analyses indicated that the greatest likelihood was for two populations  were significant  or FST = 0.084 (roadkills) with microsatellites, and 0.066 (roadkills dataset) with SNPs. Allele frequency tables for the microsatellite and the SNP datasets are provided (Tables d Tables  and S5.3.2n = 80) were analyzed in both the western and central datasets. Individuals which were potentially admixed between the eastern and western wildcat cluster  were excluded. We used an assignment threshold of qi).NewHybrids analyses were conducted in two ways for microsatellite data: (i) all samples together and (ii) samples analyzed separately according to whether the samples belonged to the western or the central wildcat cluster as detected by Structure. In the latter analyses, individuals in the domestic cat cluster and individuals that were potentially admixed between wildcats and domestic cats were included in either the “western” or “central” analysis according to their geographical sampling location  resulted in 80% wildcats and 20% domestic cats in the western group  resulted in assignments of mean qi) were in agreement with the NewHybrids assignments. However, this agreement was higher for individuals from the central group  than from the western group (85% domestic cats and 90% wildcats). In the case of hybrid categories, four individuals identified by Structure as domestic cats were classified by NewHybrids as hybrids, and four individuals considered admixed by Structure were assigned to parental species. In the case of SNP‐based analyses, there was one disagreement only .Assignments using Structure and NewHybrids were mostly consistent Table . In the n = 4) or domestic cats (n = 5) and as not assigned (n = 4) based on microsatellites, resulting in the disagreement of 1% and 4%, respectively  and in some cases misclassified (qi).With NewHybrids, the artificially generated microsatellite wildcat genotypes were more reliable assigned to the correct parental category  than for the artificially generated domestic cats ( < .75), and a proportion of 40% of the backcrosses was assigned to each parental cluster , and almost all artificially generated F2 hybrids, backcrosses to wildcat, and backcrosses to domestic cat were assigned with high confidence (qi) , pure individuals , or were not assigned with confidence (qi) from six different regions in Germany , reported no evidence of hybridization using seven microsatellite loci and Structure analysis. The highest hybridization rates in Germany were reported in Hertwig et al.,  and backcrosses to both parental groups  when samples from the west and the central clusters (as detected by Structure) were analyzed together in NewHybrids. These F2 individuals were mostly assigned to pure wildcats when the western and central clusters  were analyzed separately or with SNP data . The software NewHybrids assumes any deviation from Hardy–Weinberg equilibrium as originating from hybridization if the allele frequencies of the two taxa prior to hybridization are not known , we analyzed them separately. These results indicated the presence of no hybrids in the western group and two in the central group, but left 60 individuals with assignment probabilities below qi). The lower differentiation of the western wildcat population and domestic cats was also observed in previous studies  of 6%–11% depending on the priors considered strongly contrasted with a high assignment rate of 0.99 when using the SNP data. The superiority in assignment can be explained by the selection process of the SNP panel, which was based on choosing loci showing greatest qi)( < .85). Microsatellites did not allow, however, to differentiate between hybrid classes, including backcrosses. In contrast, our simulated SNP genotypes were assigned with high certainty to parental and hybrid categories up to the second hybrid generation  appears somewhat surprising, given the fact that Germany and Luxembourg are densely populated areas with highly fragmented habitats for the species. Despite that wildcats are outnumbered by domestic cats by a factor of at least 10None declared.All authors conceptualized this study; A.T. and K.S. performed the molecular laboratory work and analyzed the data with V.M.F.; A.T. and K.S. wrote the first draft, with major contribution from V.M.F.; all authors contributed to the final version of the manuscript; C.N. acquired funding. All authors read and approved the current version of the article.https://doi.org/10.5061/dryad.fp954). DNA sequence data used for haplotype network have been submitted to GenBank and have accession numbers KX161418‐KX161423. Additional analyses are provided as supporting information.Sampling locations, all genetic raw data, and results from the software analyses as reported in this study, including mitochondrial sequences, genotypes from microsatellites, and SNPs, are available via Dryad ( Click here for additional data file.


P < 1.67 × 10−10). Of those, 9p24.3 represents a novel region influencing adult height, whereas the others have been highlighted in the previous GWAS on height based on analysis of individual SNPs. A replication analysis in 4080 Australians of European ancestry confirmed the significant CH-like association at 9p24.3 (P < 0.05). Together, the collapsed genotypes at these six loci explained 2.51% of the height variance (after adjusting for sex and age), compared with 3.23% explained by the 14 top-associated SNPs at 14 loci identified by traditional GWAS in the same data set (P < 5 × 10−8). Overall, our study empirically demonstrates that CH plays an important role in adult height and may explain a proportion of its “missing heritability”. Moreover, our findings raise promising expectations for other highly polygenic complex traits to explain missing heritability identifiable through CH-like associations.Adult height is the most widely genetically studied common trait in humans; however, the trait variance explainable by currently known height-associated single nucleotide polymorphisms (SNPs) identified from the previous genome-wide association studies (GWAS) is yet far from complete given the high heritability of this complex trait. To exam if compound heterozygotes (CH) may explain extra height variance, we conducted a genome-wide analysis to screen for CH in association with adult height in 10,631 Dutch Europeans enriched with extremely tall people, using our recently developed method implemented in the software package CollapsABEL. The analysis identified six regions , where multiple pairs of SNPs as CH showed genome-wide significant association with height (The online version of this article (doi:10.1007/s00439-017-1842-3) contains supplementary material, which is available to authorized users. Human adult height is highly influenced by genetic factors with an estimated heritability of up to 80%  in classical genetics refer to two different mutations at a particular gene, one on each chromosome—both typically rare and deleterious , including 770 additional Dutch Europeans of extremely tall stature . The top-associated SNPs at each locus were rs12485899 from 3p22.1 , rs9838625 from 3q23 , rs1265097 from 6p21.33 , rs2780226 from 6p21.31 , rs7741741 from 6q24.1 , rs4272 from 7q21.2 , rs6984782 from 8q12.1 , rs4931222 from 12p11.22 , rs1038196 from 12q14.3 , rs7159961 from 14q11.2 , rs1366870 from 15q11.2 , rs2279007 from 19p13.11 , rs6060355 from 20q11.22 , and rs10439884 from 21p11.1 . Among these, two loci (15q11.2 and 21p11.1) represent novel findings, but at both loci, the top-associated SNPs had relatively low minor allele frequency , while the remaining 12 loci have been previously identified by the GIANT Consortium via traditional GWAS , highlighting GRM4/HMGA1, GPR126, CDK6, HMGA2, MYO9B, and UQCC1 genes. The replication rate of the single SNPs was lower than expected from our power analysis, which showed a >90% probability of replicating all the 14 SNPs (see Online Resource 3). The lower replication rate may be explained by multiple factors. For example, allelic heterogeneity has been previously noted as a frequent feature underlying the genetic architecture of adult . The two novel loci which had very low MAF in QIMR (0.1–0.2%) are thus not available for replication. For the remaining 12 SNPs, 10 showed the same allele effect direction as in the RS and 6 showed nominally significant association with height (λ = 4.16) from the expected null of the conventional GWAS as the result of multiple testing from the sliding window approach implemented in CollapsABEL. This deviation is expected, because first, CollapsABEL only reports the most significant association signal per window, i.e., a problem of multiple testing of correlated SNPs, and second, a p-filter was applied to pre-remove SNPs with little or no marginal effect. Therefore, we conducted a permutation analysis to approximate the null empirically, i.e., repeating the full GCDH genome screening five times using permuted phenotype values, and considered the distribution of the resultant test statistics as an empirical null distribution . The top-associated pseudo-markers were collapsed from SNP pairs in these six regions, including rs9821337_rs6762826 from 3q23 , rs1466947_rs17070997 from 5q35.1 , rs1776897_rs2744977 from 6p21.31 , rs6904669_rs1960278 from 6p21.33 , rs7793983_rs17164894 from 7q21.2 , and rs514779_rs10962274 from 9p24.3 . One of the six loci, 9p24.3 . The GCDH employs a generalized linear modeling framework to test the association between a pseudo-marker and a phenotype with or without covariates, where the pseudo-marker is collapsed from two physically close SNPs, as described in more details in the “ion Fig. b. Althouion Fig. b. This GP = 0.026, β = −0.48) as well as three previously known markers were replicated at nominal significance, i.e., rs9821337_rs6762826 from 3q23 , rs1776897_rs2744977 from 6p21.31 , rs17688839_rs17164894 from 7q21.2 . All of these replicated β values showed the same directions as those in the Dutch Europeans  and the involvement of double (non-compound) heterozygotes (see Online Resource 7 for an illustration and double and compound heterozygosity) for the other three pseudo-markers (Online Resource 8).An empirical application of the GCDH approach (as implemented in CollapsABEL) to height identified novel CH-like interactions and markers that are likely not identifiable by the conventional GWAS. With only a small fraction of the sample size of the GIANT study  have all been previously identified via large-scale traditional single-SNP GWAS of the GIANT Consortium . This can be explained by the much stricter genome-wide significance threshold used in GCDH, i.e., the fewer false positives lead to higher reproducibility.Although our method provides a higher power to detect CH-like associations, it cannot pinpoint causal variants as does in the conventional GWAS, particularly when the associated SNPs are intergenic. Therefore, our method cannot distinguish between two variants that affect the same or different genes and the finding at DMRT2 and SMARCA2 requires further functional studies.Overall, the GCDH approach seems to be sitting at the saddle point of the two leading theories for “missing heritability”: it can detect CH-like interactions between pairs of SNPs often less frequent than what is detectable by the conventional GWAS. For future applications with larger sample sizes and adequate computing power in combination with whole genome-sequencing-based techniques, we expect GCDH to explain a considerably larger percentage of the variance, thus providing a partial explanation for “missing heritability” and new insights into genetics of human complex traits and diseases.The Rotterdam Study (RS) is a prospective population-based data set study of 14,926 participants’ aged 45 years and older, living in a suburb of Rotterdam, The Netherlands. Details of the study design and population have been described elsewhere  in 10,631 individuals. Height was measured using a stadiometer . We also included 770 extremely tall subject from the Dutch Tall cohort (DT), which used the following inclusion criteria: (1) standard deviation score (SDs) above 1.88 according to Dutch standards (http://www.tno.nl/groei), which corresponds to the 3% upper tail of the height distribution in Dutch adults, approximately >195 cm in men and >180 cm in women at age 30, after correcting for secular trend, and (2) Dutch European ancestry defined as being born to Dutch parents who themselves were born in The Netherlands.Whole blood DNA extraction, genotyping, quality controls, and 1000 Genome  mean GenCall score <0.7; (5) fail GenomeStudio filters as per Illumina QC SOPs; and (6) blacklists based on poor retest consistency and mapping issues, particularly gender-specific heterozygosity and call-rate filters for chromosome X. Genotyping of the 8672 was divided between first-generation Illumina chips (5960 of which 4300 were from 610 K-quad or (rarely) 660 K-quad chips; 1660 from 370 K or 317 K chips), and newer Omni and Core + Exome Illumina chips . Imputation was to the 1000 Genomes Phase 3 mixed-population panel via the University of Michigan’s Imputation Server in March–April 2015, as one run per chromosome for each of the two chip families above, in each case for the observed markers available post-QC across all batches for that chip family ; the two families merged post-imputation with a binary ‘run number’ covariate in the analysis to mitigate differences in imputation quality. The current study included 4080 unrelated QIMR subjects for replication of the associated SNPs.The QIMR study comprised 8672 individuals from predominantly population and family-based studies, around 4161 from nuclear families living in or near Brisbane, Australia, with twins aged around 12–14 years at recruitment for clinical studies, and the remainder from older twin families across Australia recruited through the Australian Twin Registry. Genotyping was a subset of an existing larger data set which used Illumina SNP array chips in large batches from two chip families. Per-batch QC removed SNPs with any of (1) MAF <1%; (2) call rate <95%; (3) HWE β values from GCDH analysis, we consider it as a validation of the CH model. In the absence of CH group, we consider it an indication of involvement of other genetic mechanisms.We conducted a genome-wide GCDH screening of adult height for finding CH-like association in the RS using CollapsABEL. Important parameters of CollapsABEL include the collapsing matrix, the window size, and the p-filter. The collapsing matrix is constructed according to the underlying genetic model, and defines the way that the two SNPs under investigation are collapsed. Assuming a joint recessive model for the minor alleles of two nearby SNPs, one may choose the default collapsing matrix, based on which the two SNPs is collapsed into a pseudo-marker consisting of two possible pseudo-genotypes . Alternative collapsing matrices may be of choice when different genetic models are assumed. In the present application of height, we assumed a joint additive effect of the minor alleles of two nearby SNPs and thus used an alternative collapsing matrix, based on which the two SNPs under investigation are collapsed into a pseudo-marker consisting of three possible pseudo-genotypes . The difference between compound and double heterozygotes is illustrated in Online Resource 7. It is important to note that compound and double heterozygotes are collapsed into the same pseudo-genotype and thus were not differentiated during the genome screening stage, but were differentiated in the subsequent analysis of the associated regions, where the haplotypes were inferred based on the expectation–maximization algorithm as implemented in the haplo.em function of the R package haplo.stats  and the maximal distance (d) between the index SNP and its pairing SNP. For each window, the first SNP is considered as the index SNP and all the remaining pairing SNPs satisfying both criteria are iteratively collapsed with the index SNP and all collapsed pseudo-markers are iteratively tested for association with the phenotype. Therefore, the maximal number of tests per window is k. The minimal P value within each window is assigned to the index SNP. In the current study of height, we used k = 300 and d = 500 kbp, and reported all pairs of SNPs with P values smaller than 1.67 × 10−10  from our GCDH genome screening, i.e., Bonferroni correction of the maximal possible number of tests per window.The genotype collapsing is done for all pairs of nearby SNPs within a sliding window which slides over the whole genome with one SNP per step. The size of the sliding window is defined by two criteria, the number of the maximal SNPs  and the resulting test statistics are displayed in QQ plots to illustrate the data-specific null distribution of our GCDH scan for adult height in the RS.R2 is consistent across RS and QIMR for all the SNPs tested.The conventional GWAS for adult height was conducted using PLINK v1.9 assuming an additive allele effect and considering sex, age, and four top genome PCs from the MDS analysis as covariates. Regional Manhattan plots were generated using Locuszoom  from the conventional GWAS and the six top-associated pseudo-markers (one per region) from the GCDH genome screen in 4080 unrelated QIMR subjects using linear regression adjusted for sex and age. Supplementary material 1 (XLSX 9 kb)Supplementary material 2 (XLSX 11 kb)Supplementary material 3 (TIFF 57 kb)Supplementary material 4 (XLSX 42 kb)Supplementary material 5 (DOCX 2170 kb)Supplementary material 6 (PDF 15 kb)Supplementary material 7 (DOCX 844 kb)Supplementary material 8 (XLSX 67 kb)Below is the link to the electronic supplementary material.


The designed unique architecture allows efficient use of pseudo‐capacitive Mn2O3 nanomaterials for charge storage with facilitated transport for both ions and electrons, rendering them high specific capacitance, good rate capability, and remarkable cycling performance. Among them, the manganese oxide is one of the most promising candidates due to its high specific capacitance, superior environmental compatibility and cost effectiveness.2 (Mn[IV]),[[qv: 5a]] Mn2O3 (Mn[III])[[qv: 5b]] or Mn3O4 (Mn[II/III]).[[qv: 5c]] Though manganese oxides have demonstrated huge potentials as supercapacitor electrodes, they still suffer from some drawbacks:[[qv: 2e]],[[qv: 2f]],[[qv: 4a]],Supercapacitors are intermediate devices between conventional batteries and dielectric capacitors that can be used in various electric systems.An emerging solution to overcome the above drawbacks is to utilize the unique structural characteristics of the hollow micro‐/nano‐structured materials.3O4,[[qv: 9a]] Fe2O3,[[qv: 9b]] SnO2[[qv: 10c]] and MFe2O4[[qv: 10a]] ) with elaborate control over the number of internal shells, shell spacing, and exterior shell structure. To date, however, the multi‐shelled hollow structured MnOx materials have not been used for supercapacitor applications mainly due to the difficulties in using the template methods to achieve the control of composition, crystalline phase and microstructure  of MnOx.[[qv: 2a]],],[[qv: 4a]]Recently, a general method utilizing the carbonaceous microspheres (CMSs) as sacrificial templates has been developed by our group for controllable synthesis of hierarchical multi‐shelled hollow microspheres with adjustable nanoparticle subunits and shell structures.[[qv: 8a]],2O3 hollow microspheres with controlled number of shells, shell thickness and porosity as the high performance supercapacitor electrode materials. The as‐synthesized triple‐shelled Mn2O3 hollow microspheres with thin porous shells show an extremely high specific capacitance up to 1651 F g–1 at a current density of 0.5 A g–1, and remarkable cycling stability with 92% retention after 2000 consecutive cycles. Besides, the rate capability is also impressive, showing a specific capacitance as high as 1422 F g–1 at a high current density of 10 A g–1. To our knowledge, the performance of Mn2O3‐based electrode materials for supercapacitor reported here is a new record to date.Herein, we report an effective approach to synthesize the multi‐shelled MnAs previously reported, the control over the crystalline phase and microstructure  can be achieved by tuning the metal ions adsorption amount and the depth embedded into the carbonaceous microsphere templates, as well as tuning the annealing conditions.[[qv: 8a]],Figure2 precursor solution at 3.35, 4.43 and 6.43, respectively, the single‐, double‐ and triple‐shelled hollow microspheres can be obtained in a high yield after the removal of the templates by heat treatment . It is well known that a decrease in pH leads to a decrease in the surface negative charge of CMSs, hence a decreased Mn cations adsorption onto the CMSs template.[[qv: 10c]] As shown in Figure S1, Supporting Information, the zeta potential of carbonaceous microsphere templates in aqueous solution is about –44.5 mV at the pH value of 6.78. With the solution pH values of 3.52, 4.80 and 6.23, the corresponding zeta potentials of CMSs are found to be –20.4, –29.9, and –42.0 mV, respectively, meaning a less electrostatic attraction between the negatively charged CMSs and the Mn cations. Moreover, with the addition of HCl aqueous solution (pH regulator), the coordination group of Mn2+ ions are changed from [Mn(H2O)6]2+ to [Mn(H2O)6‐xClx]2‐x , further decreasing the electrostatic attraction.2O3 hollow microspheres with certain number of shells can be obtained after heat treatment  instead of Mn2O3 hollow microspheres .In this communication, we report a modified control synthesis approach for fabrication of uniform single‐, double‐, triple‐shelled hollow microspheres with thin porous shells by precisely controlling the pH values of the precursor solutions with an appropriate concentration. As shown in 2O3 hollow microspheres can be obtained  into the aqueous infusion solution . According to the Arrhenius equation,[[qv: 9a]],2O3 hollow microspheres and nanoparticles were investigated by X‐ray diffraction (XRD). As shown in Figure 2O3 phase (JCPDS card no. 41–1442), with no additional peaks are detected.x that exist in the resulting products, such as MnO2, probably due to there not being enough driving force to step over the energy barrier to a much higher valence state. Further compositional information can be obtained by Raman spectra , EDX and corresponding elemental mapping . The average grain sizes of Mn2O3 nanoparticles and hollow microspheres with single‐, double‐, triple‐, and quadruple‐shells calculated by Scherrer Equation are 19.54, 30.35, 28.43, 27.12 and 29.87 nm respectively, consistent with the TEM (transmission electron microscopy) and SEM (scanning electron microscopy) observations shown in Figure 2O3 phase with high crystallinity, which is also proved by the high‐resolution TEM (HRTEM) image . Taking triple‐shelled Mn2O3 hollow microspheres as a representative example, two characteristic broad peaks encompassing the Mn (III) 2p3/2 at 641.8 eV and 2p1/2 at 653.3 eV are observed, with a spin‐energy separation of 11.5 eV, which is consistent with the reported data of Mn (III) 2p3/2 and Mn (III) 2p1/2 in pure Mn2O3 phase.The crystalline structures of as‐prepared Mn2O3 hollow microspheres as electrode materials for supercapacitors were evaluated by a three‐electrode electrochemical system.Figure2O3 hollow microspheres electrodes obtained from 6 m KOH electrolyte under scan rates ranged from 5 mV s–1 to 200 mV s–1 within the potential window of 0.0–0.45 V ). For comparison, the electrodes assembled with single‐, double‐, quadruple‐shelled Mn2O3 hollow microspheres and Mn2O3 NPs are also evaluated . It can be found that the CV curves of Mn2O3 are distorted from mirror image symmetry and a pair of redox peaks can be clearly observed, which can be ascribed to the redox reactions between the oxidation states of Mn(III) and Mn(IV). Therefore it can be known the Faradic capacitance plays the main role in the charge storage of Mn2O3 hollow structures. Moreover, all these CV curves preserve the obvious redox peaks of Mn2O3 even at a high scan rate of 200 mV s–1, indicating an ideal capacitive behavior and desirable fast charge/discharge property for power devices.The performance of the synthesized multi‐shelled MnI–V responses and small equivalent series resistance, ideal for capacitive device applications. The specific capacitance of the triple‐shelled Mn2O3 hollow microspheres electrode was found to decrease with increasing discharge current densities. It can be seen from Figure S9, Supporting Information, that the specific capacitance of triple‐shelled Mn2O3 hollow microspheres was 1651 F g–1 at a discharge current density of 0.5 A g–1 and still kept as high as 1422 F g–1 at a high discharge current density of 10 A g–1, indicating a superior rate performance. Remarkably, compared with other Mn2O3‐based supercapacitor electrode reported up to date, the specific capacitance of the triple‐shelled Mn2O3 hollow spheres is a new record .To further evaluate the cell performance, the galvanostatic charge/discharge curves at various current densities were measured with a constant voltage window of 0.0–0.45 V.2O3 hollow microspheres can be well controlled to optimize the capacitive performance. Figure2O3 NPs and hollow microspheres with different shell numbers (1–4) to explore the effect of shell numbers on the resultant capacitances. It can be seen that the CV integration areas per weight of triple‐shelled Mn2O3 hollow microspheres, which indicates the charge stored in the supercapacitor, are much larger than that of Mn2O3 NPs, single‐, double‐ and quadruple‐shelled hollow microspheres. It seems that the maximum capacitance could only be obtained from a moderate shell number of Mn2O3 hollow microspheres with the porous thin shell. This is easy to be understood that as the maximal active sites and appropriate pore volume could only be obtained from the proper shell structures to facilitate the ions penetration and the process of redox reactions. The specific capacitance of the electrodes with different scan rates is given in Figure 2O3 hollow microspheres electrodes were much higher than that of the Mn2O3 NPs, and among them triple‐shelled Mn2O3 hollow microspheres achieved a maximum capacitance, the crest value of specific capacitance of 1640 F g–1 at the scan rate of 5 mV s–1 can be achieved with the triple‐shelled Mn2O3 hollow microspheres. The same trend can also be observed by comparison of galvanostatic charge/discharge curves of various samples at the same charge/discharge current density . The Brunauer–Emmett–Teller (BET) data  show that the triple‐shelled Mn2O3 possesses the highest specific surface area of 36.55 m2 g–1 and also the largest pore volume of ≈0.518 cm3 g–1 among other multi‐shelled hollow microspheres investigated . It can be deduced that a higher specific surface area and larger pore volume could guarantee an abundance of the active sites and the easy electrolyte penetration into the materials, resulting in an improved capacitive performance. It should be mentioned that the trend of the specific capacitance changes is consistent with the changes of specific surface areas and the pore volumes of multi‐shelled Mn2O3 hollow microspheres, supporting our standpoints. However, when comes to Mn2O3 NPs, it shows a much higher specific surface area and larger pore volume, yet a lower specific capacitance. It could be explained from two aspects: first, although Mn2O3 NPs possess a larger surface area, the actual accessible reactive surface area of hollow microspheres is much larger due to the special hierarchical structure and well‐preserved interface dispersibility in the electrode layers, while the Mn2O3 NPs could lose large portion of their reactive sites due to serious accumulation and crosslink. Additionally, the unique three‐dimensional hierarchical porous multi‐shelled hollow structure can ensure the ions from both outer and inner shell to be charged and transferred, resulting in superior capacitive and rate performance.Since the charge storage process of a supercapacitor is a surface reaction between the electrolyte ions and electrode materials,[[qv: 2b]] a detailed surface structure investigation is therefore needed to understand the mechanism behind the discrepant performance. From the TEM and SEM images we can clearly see that the shell thickness of multi‐shelled hollow microspheres is different and triple‐shelled Mnx often suffers from a limited long‐term stability because of the partial dissolution in the electrolyte during cycling, which restricts the commercial application of such low‐cost supercapacitor electrode materials.x hollow microspheres is able to noticeably meliorate the cycling performance. The cycling life test of Mn2O3 NPs and hollow microspheres were examined between 0 and 0.45 V in 6 m KOH aqueous electrolyte by consecutive charge/discharge cycles at a current density of 1 A g–1 , while Mn2O3 NPs could only retain a capacitance of 376.5 F g–1 with a 35.3% loss of its initial capacitance (581.7 F g–1). The better stability of the multi‐shelled Mn2O3 hollow microspheres can be ascribed to the superior shell structure with the shells supporting for each other, especially the exterior shell protecting the interior shells, which can undertake some mechanical deformation and swell in the redox processes and suppress the dissolution of Mn2O3 in electrolyte.Long cycling life is also an important requirement for supercapacitors. In this regards, MnO2O3 hollow microspheres as high‐performance supercapacitor electrodes and the effect of shell structures on the capacitive performance are carefully studied. The unique architecture of these designed nanostructures allows efficient use of pseudo‐capacitive Mn2O3 nanomaterials for charge storage due to the facilitated transport of both electrolyte ions and electrons, thus rendering them with a record high specific capacitance of 1651 F g–1, good rate capability and remarkable cycling performance. We believe that such earth‐abundant, low‐cost, environment‐friendly, and high‐performance electrode materials achieved through a scalable and controllable process, can offer great promise in large‐scale energy storage device applications.In summary, we have developed a scalable approach to fabricate multi‐shelled MnAs a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


There is a vast research literature on the antioxidant activity in food. In the case of foods seen as a natural mix of antioxidants, the highly cited paper by Floegel et al., produced an extensive correlation of the antioxidant capacities of 50 fruit, vegetable and beverages using the ABTS, DPPH and ORAC techniques with their total phenolic and flavanoid contents . It was Foods contain many antioxidants, some of which are more common than others. Flavonoids, for example, are often found in significant concentrations in most natural foods. Such individual antioxidant molecules react at different rates of reaction with free radicals and reactive oxidative species ROS) and, hence, have the potential to avoid the damaging effects of these reactive species on cellular components, particularly DNA. Antioxidant assays all purport to measure total antioxidant activity but yet different assays often do not correlate when measuring the same mixture of antioxidant molecules. Many assays are now informed by precise identification of the individual antioxidant molecules. In addition, there is much data now available on the reactivities of such molecules with specific free radicals and ROS. In principle, therefore, it is possible to reconcile the correlation (or lack of) of antioxidant mixtures. In this Special Issue, the original papers and review provide further insights into the correlation of characterisation, identification, chemical, biological and other assays of food-based antioxidant mixtures.In the study by Zeb , using wDe Taeye and Kankolongo, used online HPLC/TEAC and high resolution MS techniques to follow the heat-induced degradation of procyanidin, the major flavanol-3-ol trimer extracted from cocoa [Proestos and Varzakas extracted antioxidants from 8 aromatic plants found in Greece and measured their activities using the DPPH method . Catechi2O solutions. Other methods such as alkaline and acidic hydrolysis as well as enzymatic methods were also highlighted. Durazzo also drew attention to the antioxidant activities of non-extractable antioxidants such as tannins, phenolic acids and hydroxycinnamic acids which often lead to an underestimate of total antioxidant activity. Indeed, some studies were found to have higher antioxidant activities in the non-extractable residues. Equally significant was the effect of processing on antioxidant activities. In the review by Durazzo, many factors affecting the antioxidant activities of foods were considered . Thus, tDurazzo also reviewed the status of studies on the antioxidant capacities of the wide range of pure antioxidant compounds often used as a basis for understanding the overall antioxidant activities of extracts and highlighted studies in which a synergistic effect was observed whereby the overall activity was larger than predicted from studies on the separate standards.Consistent with the theme of this Special Issue, the contributed papers confirm the steadily-growing interest in food and antioxidants. These papers demonstrate not only the ever more sophisticated analytical techniques applied to identify individual antioxidant molecules as components of complex food extracts but also the drive to correlate overall antioxidant capacity of a food with the individual activities of the component antioxidant molecules.There are many antioxidant activity assays in general use, most of which can be classified as either electron transfer (ET) or hydrogen atom transfer (HAT) mechanisms with ABTS and DPPH the most well used. In all methods, a kinetic competition is set up between the oxidizing agent  and reducing agents . After a limited incubation, typically 6–10 min, the depletion of the oxidising agent is determined. The extent of depletion in the sample mixture can then be compared to a standard such as Trolox. The assumption is that at the end of the incubation period, the measurements can be used to determine the effectiveness of the food antioxidant(s) or components. However, this assumes a straightforward kinetic competition and it is likely that in some cases, this is not valid. There are very few kinetic studies on the reactions of the ABTS cation radical or the DPPH radical with pure food antioxidant components such as quercetin and catechin. Some such studies show that the reaction mechanisms are often complex and need further interpretation . For exa