The receiver has superior sensitivity (2.36–3.87 V/MPa) to those of a commercial fibre-optic hydrophone in the low megahertz frequency range (0.51–5.4 MHz). The receiver also has a wide −6 dB acceptance angle (54 degrees at 1.1 MHz and 13 degrees at 5.4 MHz) and the ability to detect subharmonic and higher harmonic MB emissions in phantoms. The overall acoustic performance of this low-cost receiver indicates its suitability for the eventual use within an array for MB monitoring and mapping in preclinical studies.The real-time monitoring of spectral characteristics of microbubble (MB) acoustic emissions permits the prediction of increases in blood–brain barrier (BBB) permeability and of tissue damage in MB-mediated focused ultrasound (FUS) brain therapy. Single-element passive cavitation detectors provide limited spatial information regarding MB activity, greatly affecting the performance of acoustic control. However, an array of receivers can be used to spatially map cavitation events and thus improve treatment control. The spectral content of the acoustic emissions provides additional information that can be correlated with the bio-effects, and wideband receivers can thus provide the most complete spectral information. Here, we develop a miniature polyvinylidene fluoride (PVDF thickness = 110 μm, active area = 1.2 mm Microbubble-mediated focused ultrasound (FUS) has been exploited as a method for brain therapy, including targeted delivery of various agents ,5,6,7,8,n + 1) f0, n = 1, 2, 3…). With increasing pressure, an MB will expand and compress in the stable cavitation (SC) regime. If pressure passes a threshold, subharmonic ((1/(n + 1)) f0, n = 1, 2, 3…) and ultraharmonic (((2n + 1)/2) f0, n = 1, 2, 3…) emissions can be observed. With further increase in pressure amplitude, the MB enters the inertial cavitation (IC) regime. The oscillatory behavior becomes more violent until the MB collapses, resulting in broadband emissions. These spectral characteristics allow types of cavitation to be distinguished [One method of directly controlling FUS-induced BBBM is through monitoring MB emissions. MBs are preformed lipid- or albumin-encapsulated gas bubbles commonly used as a contrast agent for ultrasound imaging . MB actinguished .The changing spectral characteristics of the MB emissions with increasing pressure correlate well with FUS-induced BBBM bioeffects. The IC behaviour of the MBs has been shown to be associated with vascular damage, in addition to changes in BBB permeability. In particular, the presence of broadband emission, a signature of IC, has been correlated with erythrocytes extravasation  and vascBroadband emission is a signature of IC and associated with unwanted bioeffects. Thus, safe BBBM requires remaining below the IC threshold ,27. AcouThe strength of harmonic emissions has also been proposed as a measure to define safe treatment ,24,26. AThe aforementioned controller algorithms either rely on the detection of sub- and/or ultraharmonics, or thresholding pressure using harmonic emissions. The bandwidths of the receiver used in those approaches were, for the most part, not sufficiently wide to detect multiple signatures. Therefore, combining the two approaches to calibrate and modulate BBBM pressure using multiple characteristic emissions remains challenging.Another drawback of the single-receiver design is spatial specificity. Studies have been performed using a set of three to four hydrophones to localize cavitation activity in tissue  and throTo map MB activity for preclinical studies, a high frequency receiver array with small aperture broadband elements to reduce directivity is needed to provide high spatial resolution at all characteristic frequencies. Commercially available calibrated hydrophones, such as fibre-optic and needle hydrophones, are either expensive and/or bulky and thus do not satisfy the specific needs of monitoring ultrasound brain therapy. In this paper, we present a small aperture and a broadband receiver operating in the low megahertz (MHz) range with minimal fabrication expenses. Sensitivity, minimum detectable pressure, and directivity were characterized with free-field acoustic measurements. The acoustic characteristics were compared to those of commercial fibre-optic and needle hydrophones, and an in-house lead zirconate titanate (PZT) receiver previously used in vivo studies. Benchtop bubble experiments were conducted to examine the receiver’s ability to detect MB activity compared to that of the PZT receiver.A miniature receiver was fabricated using 110 μm polyvinylidene fluoride (PVDF) film  with nickel-copper electrodes , having an active area of 1.1 mm by 1.1 mm. PVDF film was selected for its well-known broadband characteristics over common lead zirconate titanate (PZT) material. The active layer was backed with 1.35 mm thick conductive silver epoxy  mixed with a 10% volume ratio of tungsten powder  to improve bandwidth and increase sensitivity in low MHz range . Conduct®, Epoxy Technology, Billerica, MA, United States of America) was used to secure ground connections. Alternatively, the use of a male-to-male adapter  enables rapid characterization by simply plugging the receiver into an MMCX female connecter. The finished element was then coated with approximately 10 μm of parylene C for electrical insulation and corrosion prevention. An optional housing can be made using a standard MMCX connector with one end of male pin-plug type, as shown in A small preamplifier with unity gain was constructed and enclosed within 3D printed acrylic housing to improve the signal-to-noise ratio (SNR). The operational amplifier selected  has a −3 dB unity gain bandwidth of 190 MHz. The preamplifier was attached to a customized printed circuit board (PCB). A MMCX female jack connector  was soldered onto the PCB for ease of individual receiver characterization. With the modularized design of the receiver assembly, the receiver and preamplifier can be fabricated and characterized separately in parallel for constructing an array. The circuit board geometry and housing can be customized according to the need and available space when designing an array.The receiver sensitivity was obtained by comparison with a standard National Physics Laboratory (NPL)-calibrated needle hydrophone. Briefly, the receiver (or hydrophone) was placed in an ultrasonic field and voltage output values were recorded at a given frequency. The same measurements were repeated for a calibrated hydrophone as the reference to calculate pressure. The receiver’s voltage-to-pressure sensitivity was then determined from the relationship between the recorded output voltage and pressure measured by the calibrated hydrophone . The experimental setup is illustrated in A function generator  was used to send 50 sine wave bursts at each of the aforementioned frequencies with a burst period of 10 ms to a 53 dB radiofrequency power amplifier . The output was used to drive the source transducer. Steady-state peak-to-peak received voltage values were acquired by taking the difference of maximum and minimum voltage output values for different driving voltages, using an oscilloscope . The oscilloscope was set to perform 16-waveform average for all measurements. The signals were then transferred to a computer for processing using MATLAB . The pressure at the focus was calculated by correlating voltage obtained from a needle hydrophone  to different input voltages and calibrated voltage-to-pressure sensitivities provided by NPL. Another commercial fibre-optic hydrophone  was also included for comparison. The calibration values of the fibre-optic hydrophone were provided with an estimated 13% standard error. The calibration values of the needle hydrophone were provided with standard errors measured at integer MHz frequencies. In the frequency range examined, the sensitivity of the needle hydrophone has an uncertainty of approximately 14.5%. The sensitivity was then determined from the ratio of output voltage to calculated pressure. The receiver sensitivity was corrected by preamplifier gain at each frequency. In short, the preamplifier gain was measured by taking the ratio of the output voltage and a known input voltage. A function generator was connected directly to the input of the preamplifier. The output of the preamplifier was connected to an oscilloscope. The output gain was then measured by taking the ratio of the output to the input voltage. Then, the receiver sensitivity was extracted by dividing the sensitivity values from preamplifier gain at each frequency. Spatial averaging artifacts were corrected by estimating the correction factor using the method described in . The senIt is important to quantify the minimum detectable pressure of the developed receiver for a comprehensive receiver characterization. The noise floor of a hydrophone assembly determines the minimum detectable pressure or the lower dynamic limit of the receiver, as defined by International Electrotechnical Commission Standard 62127—Part 3 Section A6.1. The minimum detectable pressure, or noise equivalent pressure, is determined by the noise level (in volts), detected by the hydrophone assembly over the bandwidth of interest. We compared the measured lower dynamic limit of the PVDF receiver with that of a fibre-optic hydrophone and a needle hydrophone at the same frequencies as Receiver directivity, or acceptance angle, was measured using a similar experimental setup, as described in the sensitivity characterization section above. The only difference is that the receiver was mounted on a custom designed arm concentrically mounted to a fine-tuning rotation stage . Voltage measurements were taken at 5-degree intervals, for ±90 degrees from the center axis. Directivity patterns at three frequencies  were examined.For comparison, theoretical directivity patterns using the unbaffled model , assuminv/v solution of Definity contrast agent  was gravity fed through a thin-wall polytetrafluoroethylene tube  at a flow rate of approximately 1.7 mL/min. A spherically focused therapeutic transducer  with a 20 mm center hole was used to sonicate the tube phantom, as shown in A benchtop experiment was performed to examine the feasibility of acoustic detection using the PVDF receiver and an in-house PZT receiver with an adapted experimental setup similar to that previously described in . A 0.05%For each of the five sonications, 10 ms bursts of sine waves at 1.78 MHz were sent at a pulse repetition frequency of 1 Hz with increasing focal pressure . To examine if the PVDF receiver can identify the threshold pressure, which introduces nonlinear acoustic emissions from microbubbles, as determined by the PZT receiver, both receivers were set to simultaneously acquire 11 ms of data for each 10 ms burst, at a sampling rate of 20 MHz, captured using an oscilloscope card . Detailed data analysis was conducted using MATLAB.The presence of characteristic frequencies of the subharmonic, second, and third harmonic of the transmit frequency (1.78 MHz) were evaluated by spectrum integration of a band ± 180 Hz at those frequencies. The threshold for subharmonic emission detection was defined as the sum of the arithmetic mean and three times the standard deviation of the subharmonic band at baseline. The baseline level was set by 10 consecutive acquisitions at initial pressure. The average pressure at which this threshold was reached for the PZT receiver was compared with the PVDF receiver.2) is demonstrated in The different receivers investigated in this paper are listed in The lower dynamic limit of the receiver assembly (maximum of 0.39 ± 0.04 kPa at 5.4 MHz) was about an order of magnitude superior to that of the fibre-optic hydrophone, and approximately half of that of the needle hydrophone in the low MHz range . The devThe batch-wise construction method enabled reproducibility and quality control over a large number of low-cost ultrasonic receivers. For a batch of 27 receivers, the receivers have an average 3.9 ± 1.1 V/MPa at 1.1 MHz and 1.53 ± 0.36 V/MPa at 5.4 MHz.The results from the benchtop bubble monitoring experiment demonstrate the fabricated PVDF receiver is capable of detecting nonlinear signals from MB emissions. −5 and 3.0 × 10−4 for PVDF and PZT, respectively. In both cases, the area under FFT values were higher than the sum of baseline means and three standard deviations, satisfying the thresholding conditions.Greater second and third harmonic emissions were captured by the PVDF receiver than those of the PZT receiver. A more gradual increase in second harmonic emission strength was found in the signals captured by the PVDF receiver. At 0.5 MPa, the nonlinear second harmonic emission signal acquired by the PVDF receiver was more than two times the relative signal strength than that of the PZT receiver. The PVDF receiver was also capable of capturing emissions at the third harmonic. t-test found no statistical difference  between the averaged pressure threshold values of the two receivers.The averaged pressure threshold, as determined by either receiver, is shown in The fabrication protocol was optimized for mass-production. Receivers were built in a batch-wise fashion, allowing for reproducibility and quality control over 32 individual receivers (assuming a minimum of 50% yield). This fabrication protocol also lowers the cost of each receiver to under 4 USD, making it highly favorable for array construction. The variability in sensitivity is expected between receiver elements, just as it is commonly seen in other types of hydrophones. With more batches, a subset of receivers with less variability in sensitivity can be selected for array construction. In the context of passive cavitation detection, high sensitivity in the preclinically relevant low MHz range is essential for detecting sub- and/or ultraharmonic emissions. The enhanced sensitivity near 1 MHz, due to the use of the tungsten epoxy mix, is thus greatly favored. The gradual decrease in sensitivity at higher frequencies is likely due to the intrinsic resonant frequency of the 110 μm thick PVDF film at 10 MHz (λ/2 resonance). Using a thicker PVDF film as the active layer can be a way of increasing sensitivity by shifting resonance, though thicker PVDF films may not be readily available in the market. Nevertheless, care must be taken when choosing the appropriate film thickness as a significant drop in sensitivity is expected at frequencies higher than resonance.The tungsten epoxy backing layer in this design may change the sensitivity profile in the low frequency range compared to water or air-backed designs . It may The receiver sensitivity was not examined at frequencies higher than 5.4 MHz. This is intentionally omitted as high frequency signals are greatly attenuated by skull bone, even in the case of thin mouse skull . If a crThe well-known trade-off between receiver sensitivity and spatial resolution requires ultrasonic receivers to be designed according to their applications. Diminished sensitivity is expected when minimizing the sensitive area for receivers with higher spatial specificity. In the case of developing receivers for monitoring MB activities, the absolute sensitivity of receiver is as important as spatial resolution. Comparing to fibre-optic hydrophones, which are designed for high spatial resolution, the presented receiver is higher in sensitivity by an order of magnitude. On the other hand, the PVDF receiver is over 100 times smaller in the active area than the narrowband PZT receiver, thus providing much higher spatial resolution than if it was used in an array. In the case of a fibre-optic hydrophone, which has a higher sensitivity per unit area than any of the other receivers, it was not considered as a possible candidate in designing a receiver array due to the cost associated with scaling to an array of receivers. In other words, the PVDF receiver is a receiver capable of constructing an array specially designed for both sensitivity and spatial resolution in the use of monitoring ultrasound brain therapies.Comparing to the hydrophone design by Jiang et al. , the recThe upper limit of which this receiver can be used is not a concern for the application of acoustic monitoring of microbubbles in brains, where the pressure and frequency are limited to low MHz sonications at sub-MPa levels . In the Differences between experimental and theoretical directivity patterns may be due to the fabrication process and the relative position of the receiver aperture to the rotational axis. Since silver epoxy was manually applied along the edge of the active surface to establish electrical connection, the aperture is not expected to be a perfect square. This irregularity in aperture can change the directivity pattern, depending on the silver epoxy geometry. Secondly, the receiver was only rotated along one axis. Although the receiver was positioned to be perpendicular to the imaging transducer with the best effort, slight angular misalignment may cause the misalignment along the rotational axis. This could cause distortions in measured directivity pattern at low frequencies in which the normalized amplitude is expected to gradually decrease as the receiver moves away from the beam axis. The estimated effective receiver element radius using the unbaffled model could also be a source of theoretical and experimental discrepancy at low MHz range. As frequency increases, the model better predicts the experimental directivity pattern as the error in the radius estimation declines. Although it was reported that the rigid piston model outperforms the unbaffled model , the unbFrom the benchtop bubble experiments, the PVDF receiver demonstrates equivalent capability to detecting characteristic frequencies from MB emissions. The receiver can detect the same pressure threshold based on the onset of subharmonic emissions as that of the PZT receiver while being more sensitive to harmonic emissions. In other words, the PVDF receiver provides the whole acoustic spectrum of MB emissions and may provide new opportunities for control, such as peak broadening, which may be missed due to the narrow bandwidth of PZT receivers. This broadband characteristic enables more complex thresholding algorithms using MB emissions from multiple frequency bands and ultimately superior treatment control. In the context of array construction, signals from higher order harmonics may also be utilized for passive mapping purposes. A close-loop control algorithm can be constructed given the harmonic emissions after the pressure drop to achieve real-time acoustic monitoring. The large difference between sensitivity of the new receivers and receivers developed by O’Reilly and Hynynen in 2010 .To develop a receiver array, the directivity pattern of the current receiver provides sufficient estimation of the −6 dB acceptance angle for designing array geometry. The results indicate that a practical preclinical receiver array integrated in a hemispherical transmit array could allow bubble imaging at frequencies up to 5 MHz with an adequate imaging range to cover a whole mouse or rat brain. If the receiver is used in an array for super-resolution MB mapping, it is expected that single bubbles can be localized in capillaries due to the order of magnitude increase in the frequency compared with the previous work . MoreoveA miniature PVDF receiver for the acoustic monitoring of MB-mediated ultrasound brain therapy has been presented. Its sensitivity per unit area is comparable to the commercial needle hydrophone in the low MHz region; however, it has a design with great potential for constructing multi-element arrays. Its wide acceptance angle over the frequency range of interest suggests the receiver is suitable for use in a receiver array. With the low-cost, batch-wise fabrication protocol, the presented receiver has a strong potential for constructing a next generation pre-clinical receiver array enabling MB activity mapping. An array with MB mapping will allow precise localization and control of ultrasound brain therapy, enabling safe and accurate quantitative preclinical studies. The development of acoustic monitoring preclinical systems will increase the number of neurological indications for which FUS-induced BBBM is a viable and effective treatment option, ultimately allowing for more clinical trials and improving clinical outcomes.


EDA). However, lateral plate number is influenced by additional loci that remain poorly characterized. Here, we search for such loci by performing genome-wide differentiation mapping based on pooled whole-genome sequence data from a European stickleback population variable in the extent of lateral plating, while tightly controlling for the phenotypic effect of EDA. This suggests a new candidate locus, the EDA receptor gene (EDAR), for which additional support is obtained by individual-level targeted Sanger sequencing and by comparing allele frequencies among natural populations. Overall, our study illustrates the power of pooled whole-genome sequencing for searching phenotypically relevant loci and opens opportunities for exploring the population genetics and ecological significance of a new candidate locus for stickleback armor evolution.Variation in lateral plating in stickleback fish represents a classical example of rapid and parallel adaptation in morphology. The underlying genetic architecture involves polymorphism at the ectodysplasin-A gene ( SignificanceEDA receptor EDAR.Much remains to be learned about the genetic basis of phenotypic variation among natural populations. Performing genome scans in threespine stickleback fish, we search for genetic loci contributing to variation in lateral plating and identify a strong novel candidate gene, the Adaptive diversification among populations is ubiquitous , but mucGasterosteus aculeatus) (e.g. EDA)  e.g. . One claus) e.g. . This pl.g. EDA) , a gene EDA influencing lateral plate variation in a natural population of threespine stickleback. We focus on fish from the Lake Constance basin in Central Europe, a system including a large lake population adapted to a pelagic life style, and several neighboring populations residing in  tributary streams and exhibiting a benthic life style  polymorphism within intron 1 of EDA amplified by the marker Stn382 , allowing us to classify each individual as homozygote for the complete (CC) or low (LL) allele, or as heterozygote (CL). Throughout our paper, we indicate EDA genotypes by superscripts.Fin tissue samples from the 186 individuals assigned to plate morphs were next subjected to genomic DNA extraction with the Zymo Quick-DNA Miniprep Plus kit. We followed the manufacturer’s protocol, with the modification that the lysate resulting from protease digest was centrifuged, and DNA was extracted from the supernatant only. We also included an RNAse treatment . Because our aim was to discover loci other than EDA genotype, each individual was assigned to one of four categories for subsequent pooled whole-genome sequencing (poolSeq) (CC (n = 74); LowLL (n = 23); CompleteCL (n = 42); and PartialCL (n = 47). The latter two categories represent stickleback with the same genotype at EDA, but exhibiting distinct lateral plate morphs. After measuring individual DNA concentrations with a Qubit fluorometer using the Broad Range kit , DNA from all individuals within each of the four categories was combined in equimolar proportion into a single library. The four resulting DNA libraries were then barcoded individually and paired-end sequenced without PCR amplification to 151 base pairs on an Illumina HiSeq2500 instrument. Each library was sequenced on two lanes, yielding a median read depth per base of 65× (CompleteCC), 71× (LowLL), 118× (CompleteCL), and 100× . This combination of read depth and number of individuals is expected to allow estimating allele frequencies within groups with relatively high precision  : Completrecision .http://www.novocraft.com/products/novoalign) . Using the Rsamtools R package  produced similar results supporting the same conclusions.In an initial step, however, we performed a genomic comparison of the CompleteCL vs PartialCL comparison, we proceeded analogously, except that SNPs were here required to exhibit a read depth between 40× and 200× within each group . With this analytical stringency, we explicitly focused on loci with relatively large phenotypic effect only. Each of these loci was annotated by extracting from the reference genome annotation all genes located within a 180-kb window centered at the candidate SNP (or SNP cluster). For each resulting transcript ID, we retrieved gene name, gene ontology information, strand, and transcript start and end positions from the ensemble bioMART stickleback database (www.ensembl.org/biomart). Every gene was then evaluated for a role in bone or ectodermal development in humans and/or zebrafish by using the gene cards (www.genecards.org) and Zfin (www.zfin.org) databases. Genes were further subjected to literature search for whether they were connected to the tumor necrosis factor pathway (which includes EDA), or the Wnt/beta-catenin pathway  in lateral plate variation. To strengthen the evidence for this candidate locus, we performed targeted Sanger sequencing around the SNP showing the highest AFD between the CompleteCL and PartialCL groups at this locus. For this, we used a “validation panel” of 46 independent individuals collected for previous studies and not included in the poolSeq-based mapping.The above candidate gene search based on differentiation mapping with poolSeq data suggested a role for a polymorphism near the EDA locus (see below). These individuals originated from Lake Constance (n = 15), from NID stream (n = 12), or were F2 hybrids derived from these populations for the experiment reported in n = 19). We predicted that if the target polymorphism at the EDAR locus was associated with plate reduction, our validation panel should be enriched for the allele identified to be associated with reduced plating  relative to the expectation based on the natural population frequency. Combining individuals from the lake and stream with their F2 hybrids was adequate because the natural lake and stream populations were found to exhibit an almost identical frequency of the partial allele . DNA from the individuals of the validation panel was extracted as described above, and PCR was performed using the primers and conditions specified in Supplementary Analysis 1. PCR products were sequenced on an ABI3130xl instrument (Applied Biosystems) and genotyped in FinchTV .The validation panel included individuals chosen to display the partially plated phenotype based on the same criteria as applied in our original screen, and hence to be heterozygous at the To evaluate the compatibility of the validation panel’s allele frequency with the random expectation, we first predicted Hardy–Weinberg proportions for all three diploid genotype classes by assuming a population frequency of the partial plating allele of 0.583 . Then we calculated the observed deviance from this expectation as the sum of the squared difference between the observed and predicted genotype frequencies across the three genotype classes. The magnitude of this statistic was then evaluated against a random distribution obtained by generating random panels of 46 diploid individuals 9,999 times according to the population allele frequency, and calculating the deviance for each of these iterations .EDA low allele and the partial allele at the new EDAR candidate locus in natural marine and freshwater populations. We predicted that the alleles reducing lateral plating should tend to be rare in the ancestral marine habitat where stickleback are selected for complete lateral plating, but display higher frequencies in freshwater—a pattern generally observed for EDA  proving fixed between the CompleteCC and LowLL groups. For the EDAR candidate locus, we considered the top-AFD SNP from the CompleteCL vs PartialCL comparison, and all flanking markers exhibiting differentiation of at least 0.35 .For each of these pools, we determined and plotted the frequency of the SNP alleles associated with reduced plating identified in the above genome scans. For Our phenotypic analysis confirmed high variability in lateral plating in our focal stream stickleback population  , or homozygous for the EDA complete allele (55%). Combining the phenotypic data with EDA genotypes thus revealed that individuals heterozygous at the EDA locus covered a wide range of plate phenotypes, as expected if genetic factors beyond EDA influence lateral plating in the NID population.Genotyping the same 186 individuals at the EDA genotype, we first mapped differentiation between the CompleteCC and the LowLL groups across the stickleback genome. This genome scan identified the neighborhood of the EDA gene as the only strongly differentiated genome region, with hundreds of SNPs across ca. 200 kb showing complete differentiation in allele frequency (i.e. AFD = 1) between the groups   . This fiCL and PartialCL categories in the same way identified SNPs reaching differentiation up to 0.541  were selected for the exploration of candidate genes. These SNPs included a single marker on the chromosomes II, III, XVI, and XVIII, a cluster of four markers on chromosome XX, and two SNPs on a scaffold unanchored to chromosomes. This genome scan also made clear that variation in plating between CompleteCL and PartialCL stickleback is not influenced by additional genetic variation in the EDA region  was located in a noncoding segment 86.5 kb upstream of the coding region of EDAR, the only annotated gene ontology for “bone development” within all chromosome segments screened for candidate genes. We hereafter refer to this region as the EDAR locus. Repeating our differentiation mapping based on an independent genome assembly derived from a specimen from the NID population confirmed the methodological robustness of the identification of the EDAR locus: in this alternative genome scan performed with higher statistical stringency, the SNP exhibiting the second highest differentiation value genome-wide (AFD = 0.478) was located on a scaffold segment homologous to the EDAR locus in the original genome scan, and coincided exactly with the original top-differentiation SNP at the EDAR locus  in an artificial cross in stickleback, albeit only in low plated individuals homozygous for the EDA low allele (corresponding to LowLL fish in our study) ; lacked support in the form of elevated differentiation across multiple markers flanking the top-differentiation SNPs . Although our validation panel included too few individuals to offer definitive evidence, our targeted sequencing experiment supports the idea that the detected polymorphism upstream of the EDAR coding region is associated with the extent of lateral plating.All 46 partially plated individuals from the validation panel produced robust PCR products for the DNA segment covering the top-differentiation SNP at the EDAR locus was in perfect physical linkage with a 2 bp indel polymorphism just 4 bp downstream of this marker . All code and All raw whole-genome sequence data are available from the NCBI sequence read archive (SRA) under the study number SRP222265 and the accession numbers listed by sample in a file on the Dryad repository (G3 online.jkac077_Supplementary_DataClick here for additional data file.


The importance of local breeds as genetic reservoirs of valuable genetic variation is well established. Pig breeding in Central and South-Eastern Europe has a long tradition that led to the formation of several local pig breeds. In the present study, genetic diversity parameters were analysed in six autochthonous pig breeds from Slovenia, Croatia and Serbia . Animals from each of these breeds were genotyped using microsatellites and single nucleotide polymorphisms (SNPs). The results obtained with these two marker systems and those based on pedigree data were compared. In addition, we estimated inbreeding levels based on the distribution of runs of homozygosity (ROH) and identified genomic regions under selection pressure using ROH islands and the integrated haplotype score (iHS).The lowest heterozygosity values calculated from microsatellite and SNP data were observed in the Turopolje pig. The observed heterozygosity was higher than the expected heterozygosity in the Black Slavonian, Moravka and Turopolje pig. Both types of markers allowed us to distinguish clusters of individuals belonging to each breed. The analysis of admixture between breeds revealed potential gene flow between the Mangalitsa and Moravka, and between the Mangalitsa and Black Slavonian, but no introgression events were detected in the Banija spotted and Turopolje pig. The distribution of ROH across the genome was not uniform. Analysis of the ROH islands identified genomic regions with an extremely high frequency of shared ROH within the Swallow-bellied Mangalitsa, which harboured genes associated with cholesterol biosynthesis, fatty acid metabolism and daily weight gain. The iHS approach to detect signatures of selection revealed candidate regions containing genes with potential roles in reproduction traits and disease resistance.Based on the estimation of population parameters obtained from three data sets, we showed the existence of relationships among the six pig breeds analysed here. Analysis of the distribution of ROH allowed us to estimate the level of inbreeding and the extent of homozygous regions in these breeds. The iHS analysis revealed genomic regions potentially associated with phenotypic traits and allowed the detection of genomic regions under selection pressure.The online version contains supplementary material available at 10.1186/s12711-022-00718-6. In the past, the development of livestock production was mainly based on the formation of local breeds that were well adapted to specific conditions and rearing practices. However, in the second half of the twentieth century, the lower economic performance of these breeds when raised under intensive production conditions resulted in a significant reduction of local pig breed populations, which were replaced by modern, highly productive pig breeds adapted to farm conditions and constraints . In receIn the present study, six local pig breeds: Banija spotted, Black Slavonian and Turopolje pig from Croatia, Mangalitsa  and Moravka from Serbia, and Krskopolje pig from Slovenia, were analysed. Most of these breeds have low to moderate production performances . While tThe genetic diversity and the relationships between the six breeds analysed in this study were previously estimated using microsatellite, mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) markers. Analyses based on microsatellite markers , 13 and The six autochthonous pig breeds analysed here have been subjected to different selective pressures in the past and are currently managed under conservation programmes. The Banija spotted breed is a newly recognised autochthonous breed and was genotyped using a high-density SNP panel within the frame of our study. Since the main goal of conservation programmes is to maintain genetic diversity, we used pedigree, microsatellite and SNP data to comprehensively investigate the level and patterns of genetic diversity and to infer population structure and isolation by distance for these six breeds. We also compared the performance of two systems of genetic markers (microsatellites and SNPs) and of pedigree data applied to the animal material available here.The six autochthonous pig breeds included in our study see Fig.  were: thBanija spotted Fig. a is the A workflow summary of the study is presented in Fig. Pedigree records for the Banija spotted, Black Slavonian and Turopolje pig populations were provided by the Croatian Ministry of Agriculture Table . The CFC-descent , was caled as in :Table 1PThe inbreeding rate  the number of complete generations, defined as the number of generations that separate the offspring from the most distant known ancestor for which its In total, 214 blood samples were obtained for the six breeds: Banija spotted (n = 24), Black Slavonian (n = 47), Turopolje pig (n = 17), Swallow-bellied Mangalitsa (n = 45), Moravka (n = 47) and Krskopolje pig (n = 34). Genomic DNA was extracted from the blood samples using the GeneJET™ Genomic DNA Purification Kit (Thermo Scientifc™) according to the manufacturer’s protocol. All animals were genotyped at 25 microsatellite loci  selected from the ISAG/FAO list , and gropoppr [ade4 [scatter3 function in the MATLAB R2020b software http://www.mathworks.com. We evaluated the population structure by using the STRUCTURE v.2.3.4 software [Genetic variability was measured across the remaining 24 microsatellites for each population using the F statistic parameters  was assessed using a NP80 NanoPhotometer . SNPs were genotyped using the GeneSeek Genomic Profiler (GGP) Porcine 80K Bead Chip .Two SNP datasets obtained from three different SNP arrays (see Table www.goldenhelix.com). SNPs located on the sex chromosomes and SNPs with a call rate lower than 90% were removed, and the samples with a call rate lower than 95% and the duplicated samples (detected using the SVS function “identity by descent estimation”) were excluded. Finally, in the first dataset, 258 samples and 57,781 SNPs were retained: Banija spotted (n = 24), Black Slavonian (n = 41), Turopolje pig (n = 47), Swallow-bellied Mangalitsa (n = 50), Moravka (n = 50) and Krskopolje pig (n = 46), and in the second dataset 2350 samples and 37,371 SNPs  remained for further analyses. The genomic coordinates of SNPs were based on the pig genome assembly Sscrofa 11.1.Data merging and quality control procedures were performed using the SNP & Variation Suite v8.8.3  used for the six breeds analysed in this study, the informativeness for assignment (ment}In)  implemenment}In)  package ment}In)  was calc ecodist  library  ecodist  was usedhttp://nov2020.archive.ensembl.org/) and plotted on chromosome idiograms using the RIdeogram [Runs of homozygosity (ROH) were identified separately for each population using the SNP and Variation Suite v8.9.0. The minimum run length was set to 1.0 Mb, the minimum number of homozygous SNPs within a run was set to 25, and a maximum of five SNPs with missing genotype and no heterozygous SNPs were allowed within a run. ROH were grouped by length: short (0.5 to 2.5 Mb), medium (2.5 to 5.0 Mb) and long (longer than 5 Mb). Genomic inbreeding coefficients , leaving 32,987 SNPs. To avoid bias related to sample size, we resized the dataset to a maximum of 24 randomly selected individuals per breed. Population structure was evaluated using the ADMIXTURE software [Principal component analyses (PCA) were performed using the  package , version package . We visusoftware . The prosoftware .ecodist package [Isolation-by-distance between populations was assessed using the Mantel test , which w package  in R [38Phasing and identity-by-descent (IBD) haplotype analysis for the dataset, comprising 152 pig populations , was performed using BEAGLE v4.1 . To corrThe maximum likelihood algorithm implemented in TreeMix 1.13 was applied to detect traces of genetic admixture between the analysed pig breeds. The dataset for TreeMix analysis was constructed by including samples from the pig breeds for which shared haplotypes with any of the six local breeds from this study were detected. The TreeMix analysis was performed for 1 to 15 migration events (five iterations per migration edge). LD between SNPs was considered by grouping SNPs in blocks of 1000. The tree was rooted with wild boar samples from Finland. The best predictor for number of migration events was selected using the ‘optM function’ in the R package optM . Then, arehh package [manhattanplot function provided by rehh package. Genes within candidate signatures of selection were plotted on chromosome idiograms using the RIdeogram [Analysis of signatures of selection was performed using phased SNP data. The  package  in R [38Ideogram  library Ideogram .The microsatellite data set contained genotypes at 24 microsatellites for at least 17 animals of each of the six breeds analysed (n = 214 pigs). Population statistic parameters from the microsatellite panel were estimated for each population Table . SignifiAfter quality control of the SNP chip data, 258 samples with 57,781 SNPs remained for further analyses Table . The SNPn was equal to 0.083 and the highest Informativeness of the microsatellite and SNP panels was evaluated for the studied populations Fig. . For theentclass1pt{minimaThe ted Fig.  and   Fig. a. A simi05) Fig. b. The onThe ancestral relationships between the six Balkan autochthonous pig breeds analysed here and pig populations worldwide  were identified by calling shared haplotypes   13, 14 and 15, overlapped with ROH islands in Black Slavonian, Banija spotted and Moravka breeds, respectively  was used to identify within-breed signatures of selection  and also pedigree data, to estimate population parameters in the six autochthonous Balkan pig breeds analysed here, and we evaluated the consistency of these estimates. In practice, the availability of full and complete pedigree datasets is rare , and thiEstimates of effective population size  data. Due to their resolving power, lower rate of genotyping errors, reproducibility of genotyping, and high abundance in the genome of domestic animals, SNPs have become a tool of choice for various genomic analyses . In our The coefficient of inbreeding derived from ROH  . On the u et al.  for the o et al.  for fiveMean lengths and number of ROH varied among the six autochthonous Balkan pig breeds analysed, but the mean number of ROH per population increased with increasing mean length of ROH Table . Short ROur results show that the genetic variability parameters calculated using microsatellite and SNP data were similar Table . EstimatSNP data . The lowSNP data , 19, andSNP data . The higr et al. . The higmentclasspt{minimaThe genetic differentiation between the populations was assessed by ST Table  with thef Barker , who sugST values between breeds , unsupervised Bayesian clustering algorithms (STRUCTURE and ADMIXTURE), population pairwise ers Fig. , Turopoleds Fig.  and the eds Fig.  and 9. Amentclasspt{minimaeds Fig.  or threeIn this study, we tested the relevance of the isolation-by-distance model to describe the genetic differentiation between the analysed breeds Fig. . The conOur results are consistent with those of Traspov et al. , which s= 0.083) was 5.42, which is consistent with results observed in other studies [The two marker panels used in our analysis were sufficiently informative to differentiate between the six autochthonous Balkan pig breeds analysed here. Regarding the differentiation between populations, random microsatellites were more informative than random SNPs Fig. . Howevertructure , and the studies .MC1R allele ED1 is high in both the Black Slavonian and Swallow-bellied Mangalitsa breeds , which suggests a common genetic origin. We also observed a genetic contribution of the Duroc breed to the Banija spotted and Krskopolje pig breeds. Crossing of autochthonous pig breeds with conventional breeds such as Duroc has often been used to improve low reproduction parameters [TreeMix analysis Fig.  was perframeters .PLSCR4, GYG1 and HPS3 genes is shared by 98% of the genotyped individuals in this breed. These three genes are associated with total number born and number born alive in pigs (PLSCR4) [GYG1) [HPS3) [SEC14L2 that is associated with the regulation of cholesterol biosynthesis in mice [PLA2G3 associated with fatty acid metabolism [PLA2G3 gene [KYNU gene, which is associated with daily weight gain in Duroc boars [KIT gene, which is associated with coat colour and the white belted phenotype that characterises this breed [The distribution of ROH across the genome is population-specific . Identif(PLSCR4) , glycoge) [GYG1)  and brow) [HPS3) . However in mice , and PLAtabolism . A non-s2G3 gene . This RO2G3 gene . The ROHoc boars  and is ioc boars . Finallyis breed , 118.In addition, another approach was used to detect genomic traces of selective events within the analysed breeds. The iHS method compares extended haplotype homozygosity (EHH)  between We analysed six indigenous pig breeds from Croatia, Serbia, and Slovenia using two types of molecular markers (microsatellites and SNPs) and pedigree data. Genetic diversity estimates for both types of markers were generally consistent but some of the observed differences can be explained by the highly polymorphic nature of microsatellites and by the limited amount and ascertainment bias of SNP data . The lowSEC14L2, PLA2G3 and KYNU genes that are associated with cholesterol biosynthesis, fatty acid metabolism and daily weight gain, respectively. The iHS approach that detects signatures of selection revealed candidate regions containing genes with a potential role in reproduction traits; ARHGAP12 (in Black Slavonian), ATP5F1A , WDR19 (in Moravka), HSPA4 and A2M (in Krskopolje pig). In addition, the SEZ6L and SLA-DRB genes associated with disease resistance were identified in regions under selection pressure in the Banija spotted and Black Slavonian breeds.The distribution of ROH across the genome was not uniform. ROH island analysis revealed genomic regions with an extremely high frequency of shared ROH in the Mangalitsa breed, which harbour the Additional file 1: Table S1. Primer sequences. Three multiplex reactions with microsatellite markers, corresponding chromosome location, primer sequence with an indication of the dye used for labelling, annealing temperature and fragment size.Additional file 2: Table S2. Statistics of genetic diversity across the microsatellites used in this study. Statistics of the genetic diversity across the 24 microsatellites used in the Banija spotted, Black Slavonian, Turopolje pig, Swallow-bellied Mangalitsa, Moravka and Krskopolje pig breeds.Additional file 3: Table S3. Pairwise Additional file 4: Table S4. List of the most informative SNPs.Additional file 5: Table S5. Effective population size  and cross-validation (CV) error estimates of ADMIXTURE, with varying levels of K (B).Additional file 7: Table S6. Nei's genetic distances based on microsatellites and SNPs.Additional file 8: Figure S2. Breed-pairwise shared identical-by-descent segments .Additional file 9: Figure S3. Manhattan plot of ROH islands.Additional file 10: Table S7. ROH islands in the Banija spotted, Black Slavonian, Swallow-bellied Mangalitsa, Moravka and Krskopolje pig breeds [g breeds , 120–136Additional file 11: Figure S4. Manhattan plot of signatures of selection (iHS).Additional file 12: Table S8. Signatures of selection in the Banija spotted, Black Slavonian, Turopolje pig, Swallow-bellied Mangalitsa, Moravka and Krskopolje pig breeds [g breeds , 137–140


Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p-value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis.Combining SNP  GWAS summary data is an important resource for dissecting the genetics of complex traits. In contrast to the individual-level genotype and phenotype data, summary data allows much broader access because of less privacy risk . The sump-values. Combining the p-values has multiple advantages. The p-values are the direct measure of statistical significance. Combining them does not concern the problem of signal cancellation in adding SNP z-scores of opposite directions  distributed under the null as long as the statistics are continuous. Therefore, p-values from statistics of different types or scales can be directly combined.GWAS summary data is often used to test the association between a trait and sets of SNPs in genes or other genomic segments. Such SNP-set test can reveal weak genetic effects that are unidentifiable by individual SNPs . Many merections . Furtherp-value test is preferred for detecting sparse and strong signals   remains robustly high power for various signal patterns by automatically adapting to a subset of important SNPs. Unlike the ACAT-O, which involves different types of test statistics, the oTFisher restricts to the same family of statistics. The adaptation is through truncating and weighting SNP p-values, which provides a vehicle for screening important SNPs. If the SNP-set is significantly associated, the important SNPs selected by oTFisher are likely trait relevant. This feature is useful for two reasons. First, important SNP screening based on the SNP-set test could help to identify SNPs with weak genetic effects because the SNP-set test has the potential to detect the totality of genetic effects that single-SNP analysis cannot. Second, the important SNPs that drive the association of a SNP set, e.g., a gene, could help reveal genetic architecture, disease mechanism, and other downstream analyses of the gene.We propose an adaptive TFisher) . The TFip-values are dependent because of the LD among SNPs. For controlling the type I error rate, we could rely on a re-sampling-based strategy to get the empirical p-value of the oTFisher. However, this strategy is computationally expensive, especially for moderate to large SNP sets. We design an efficient algorithm to calculate the p-value of the TFisher and the oTFisher. It is a hybrid of the generalization of Brown’s method (GB) . The SKRM can significantly improve calculation accuracy for smaller p-value.The exact distribution of oTFisher is challenging to obtain when SNP hod (GB)  and a mohod (GB) . The GB p-value combination methods. The results contributed more insights into the genetics of osteoporosis.The oTFisher is shown robustly powerful through extensive simulations. The type I error rate is adequately controlled even at a stringent significance level. We applied the oTFisher to analyze the summary data from the Genetic Factors for Osteoporosis Consortium (GEFOS). The oTFisher systematically identified more literature disease genes than the current n SNPs have p-values Pi, i = 1, … , n. The TFisher statistic tests the genetic association between a trait and the SNP set by combining these p-values while allowing for a general scheme of truncation and weighting  ≤ ⋯ ≤ Pn) , 2007: ) method . Let P, the smallest SNP p-values should be included in the statistic by small τ1 and τ2.The TFisher is a flexible framework to maximize the detection of SNP-set associations over a broad spectrum of signal patterns. Different signal patterns are in favor of different truncating and weighting parameters. For example, when association signals are dense, more SNP τk1, τk2), k = 1, … , K}, where K is the total number of τ values to search on. Denote P(k) the test p-value of Tn . The omnibus statistic is defined as the smallest P(k), which indicates the maximal association evidence for the whole SNP-set:P(k)’s (P(k)’s by the inverse cumulative distribution function (CDF) of the standard Cauchy distribution. Because of the heavy tail of Cauchy distribution, oTFisher_cct is dominated by p-values closer to 0 or 1. In practice, we truncate P(k) = 1 to be 0.9 so that oTFisher_cct is dominated by small p-values and performs similarly to oTFisher_minp. Note that since P(k) depends on the LD , the oTFisher implicitly accounts for the LD information.In reality, the signal patterns are often unknown and substantially differ over traits and loci. Therefore, we rely on the data-adaptive omnibus testing procedure to automatically select appropriate parameters. Specifically, we consider a discrete search domain over {(f P(k)’s :oTFishep-values α is the adjusted significance level, g is the number of SNP sets  studied simultaneously.1) SNP-set testing: Identify the significantly associated SNP-sets by their oTFisher t SNPs contained in the significant SNP sets, get s candidate SNPs with their p-value less than a threshold p⋆.2) Screening: From the p-values of the s candidate SNPs. Get s1 validated SNPs with their p-values less than α/s.3) Validation: Use an independent data to get new We can utilize the oTFisher as a procedure to screen for important SNPs. The oTFisher procedure has three steps:p⋆ = τ*, where P (k*) is the minimal P(k)). Meanwhile, P(k) could have similar values over different k. To be conservative and reduce the false discoveries, we recommend p⋆ = min{τ*, 0.1} (denoted by oTFisher_r as a restricted version).A natural choice of the threshold is p-values less than p⋆ = α/L, where L is the total number of SNPs.• Bonferroni procedure: The screened SNPs are those with their p-value less than k⋆ is the largest k such that the ordered SNP p-values Pk) procedure:The SNPs screened by Bonferroni and BH are validated in the same way as the validation stage for the oTFisher.τ* is influenced by the proportion of genetic signals .Following the literature , we accoon tests ). As oneprojects . Althougτ1, τ2 and n, the TFisher statistic Tn  in p-values are truncated. Define s = −2 log (τ1/τ2) ≥ 0 when τ2 ≥ τ1, and s = −2n log (τ1/τ2) < 0 when τ2 < τ1. In either case Tn  ≥ s and its distribution is a mixture of point mass at 0 and a continuous distribution defined in [s, ∞). That is, Tn  ∼ p0 ⋅ 0 + (1 − p0) ⋅ T′, where T′ denotes an appropriate continuous random variable.At given p0 is easy to calculate under normality in T′ is challenging to obtain. We propose to use the gamma distribution model to approximate it for a few reasons. First, the model is consistent with the distribution of the TFisher under independence, which is a weighted gamma distribution (n is large (see details below). Overall, gamma distribution provides a flexible and straightforward distribution model, vital for computational speed and accuracy.The exact value of ribution . Second,ribution . Third, X′ − b ∼Γ, the gamma distribution with shape parameter a and scale parameter θ. We consider a shift parameter b so that X′ ∈ [b, ∞). Let Fa,θ) (Γ(x) denote the CDF of Γ. The CDF of X isp-value of the TFisher at an observed statistic t isFX(t) in the following.Specifically, we approximate the distribution of the TFisher statistic byT and X. Specifically, we set the shift parameter b = s so that X′ and T′ have the same domain. The parameters a and θ are determined by matching the means and the variances of Tn  and X. Denote μT = E ) and Tn  for large n by the Central Limit Theorem (CLT). Specifically, when n → ∞, p0 → 0, so μT = μX ≈ aθ + b and a, θ) ≈ N  for large a, the distribution model in n, the distribution model in p-value calculation.Method 1: The generalized Brown’s method (GB): This method follows the essential idea of Brown’s method  to matchμT = 2nτ1 (1 − log  τ1 + log  τ2). For the variance k would give sufficient accuracy for Straightforward calculation gives or σT2 . The proor σT2  and is gp-value highly depends on the precise approximation of the right tail of the null distribution. In this method, we do not require the shifting parameter b = s but treat it as additional freedom to capture the right-tail information of the TFisher statistic. That is, in addition to the first two moments, we further match the skewness-kurtosis ratios of Tn  and X. Engaging higher-order moments could provide more flexibility in the distribution and thus improve the accuracy of p-value calculation. In particular, matching the skewness-kurtosis ratios is a cost-efficient method–it captures two higher moments using only one extra parameter.Method 2: Skewness-kurtosis-ratio matching method (SKRM). Accurate calculation of small T′, and γX′ and κX′ be the skewness and kurtosis of X′, respectively. By matching the ratio between skewness and excess kurtosisμT′ = μX′ and a, θ and b are determined, the p-value of the TFisher can be calculated by Specifically, let γT′ and κT′ would be intricate due to the complexity of the high moments of the summational terms in the TFisher statistic. We rely on simulation by p-value directly. Therefore, the SKRM is still computationally more efficient than getting p-values solely based on the re-sampling strategy.Exact calculation of p-value. Because the GB is fast and accurate for controlling the type I error rate at α ≥ 0.01 , the GB is applied in the first stage. If the obtained p-value is less than 0.01 (the chance is about 1% under the null), the SKRM method will obtain the final p-value in the second stage. With a single core of 2.80 GHz AMD EPYC 7543 CPU and 20G memory, the computation times for calculating TFisher’s p-values for SNP sets of 30/50/100/200 SNPs are about 0.07/0.13/0.30/0.74 s by the GB method (implemented in R version 4.2.0). Correspondingly, the expected times by the hybrid method  are 0.09/0.15/0.33/0.78 s.Method 3: Hybrid method. To balance computational speed and accuracy, we recommend a simple two-stage calculation of TFisher’s p-value byp0 is the observed statistic of oTFisher_minp, ΦR denotes the CDF of a multivariate normal distribution with mean zero and correlation matrix R. We obtain R by scaling Ω, the covariance matrix of Tn , k = 1, … , K, given by Lemma 2 in Supplementary Material. That is, R = ΛΩΛ with the diagonal matrix For the oTFisher_minp in Tn ’s are roughly normal distributed. This requirement is justifiable because Tn  is in the format of summation so that it is roughly normal distributed by the CLT when n is moderately large and τ1 is not too small. Denote the observed statistic by ccto. We directly apply the result by , k = 1, … , K.As for oTFisher_cct in esult by  to approp-value calculation, statistical power, and SNP-screening performance of the oTFisher procedures. The genotype data were generated by the Cosi2 package  is the design matrix corresponding to (10), and W is a diagonal matrix: Mi is standardized to get the marginal score statistic N → ∞ under H0. The correlation matrix of p-values in p-values, which corrects the bias due to the unbalanced distribution of rare variants’ genotype data  controls the error much better. Therefore, the hybrid method combining the GB and the SKRM balances accuracy and computational speed. Consistent simulation results with various n and τ = 10–5, 10–3 or 10–2 in Under the null, quantitative and binary trait values were generated by setting p-value combination tests in GWAS summary data analysis: the GATES , and the adaptive RTP (ARTP). The ATPM follows (1) and (2) with fixed τ2 = 1, and adapts over τ1 ∈ {Pk), the CCβ in α to avoid potentially unavailable or inaccurate p-value calculation for some tests. For example, there are no p-value calculation methods for the RTP and the ARTP under dependence. We got the critical value of one statistic by the upper 100α% percentile of its values generated from 10,000 simulations under the null. The statistical power was obtained by the percentage surpassing the critical value among the statistics generated from 1,000 simulations of the alternative. For the RTP and ARTP, we applied a one-level simulation algorithm consistent with literature  and their effects are strong. Fisher’s method shows an opposite pattern—it is preferred if causal SNPs are dense, especially when effects are weak. These patterns are consistent with literature results . The oTFisher_cct and oTFisher_minp are similar; their power is the best in most scenarios, showing an overall advantage over unknown genetic architectures. In comparison with the ATPM and the ARTP, p-value calculation algorithm provides a practical advantage for applying the oTFisher over the ARTP in computation. These comparison patterns remain similar for quantitative traits, rare variants, and different α levels. Comparisons under other settings are given in Statistical power was systematically studied under various settings regarding trait type (quantitative or binary), SNP type (common or rare), the number of causal SNPs  results . TFisherL = 1,000 SNPs (with the LD r2 < 0.3) in g = 10 genes of equal size. Two causal genes contained causal SNPs with random locations. The continuous and binary traits were obtained using models in β and the proportion of causal SNPs in the two causal genes. The sample size N = 1,000; the cases and controls were balanced for binary traits.We studied the performance of SNP-screening procedures measured by the accuracy of detecting causal SNPs. Because non-causal SNPs in LD with causal SNPs also show statistical associations, the study focused on rare-variant analysis with weak LDs for simplicity. To mimic a gene-based SNP-set analysis, we simulated s candidate SNPs as being picked from in total t SNPs in the significant genes; the rest t − s SNPs are unpicked. At the validation stage, the s1 validated SNPs are picked, and the rest t − s1 SNPs in the significant genes are unpicked. The accuracy measures were averaged over 1,000 simulations.We considered accuracy by the sensitivity, specificity, and balanced accuracy  based on the true positives , false positives , true negatives , and false negatives . These numbers are determined after defining the “picked” and “unpicked” SNPs. At the screening stage of the oTFisher procedure described in Section 2.2, we consider the m is known, the oracle, i.e., the best possible, SNP screening procedure is to pick SNPs by setting p⋆ = Pm) analyses for hunting putative genetic factors associated with bone mineral density (BMD) related traits and fall risk. The Supplementary Material gives details on our data pre-processing, including the pipeline to map SNPs to genes and haploblocks . FurtheWe extensively searched the literature and obtained comprehensive lists of 2,179 “literature” genes and 4,802 literature SNPs reported to be associated with osteoporosis, bone fracture, and various traits of bone mineral density (BMD). For the falling risk, we took the 16 genes reported by  as literp-values less than Bonferroni adjusted threshold: 0.05 divided by the total number of SNPs in all genes. Second, we applied two broadly applied SNP-set methods using SNP p-values: the ACAT . The polygenic genetic architecture is possible for complex human diseases, including the BMD-related traits . The first cluster gives a major network containing 12 pathways: Wnt signaling pathway (hsa04310), breast cancer (hsa05224), hepatocellular carcinoma (hsa05225), pathways in cancer (hsa05200), gastric cancer (hsa05226), basal cell carcinoma (hsa05217), signaling pathways regulating pluripotency of stem cells (hsa04550), proteoglycans in cancer (hsa05205), hippo signaling pathway (hsa04390), human papillomavirus infection (hsa05165), Cushing syndrome (hsa04934), and mTOR signaling pathway (hsa04150). The top two significant pathways, the Wnt signaling and breast cancer pathways, have been reported in literature  for the top-hit genes identified by oTFisher. Based on gene ontology (GO) and KEGG pathways, the analysis was conducted by KEGG Orthology-Based Annotation System intelligent version (KOBAS-i) . The anaterature . The larHSPG2  is included in a significantly enriched pathway of proteoglycans in cancer . It was shown to be associated with segregating developmental dysplasia of the hip  is included in the significantly enriched MAPK signaling pathway . It is related to lissencephaly type 3 - metacarpal bone dysplasia and infantile osteopetrosis with neuroaxonal dysplasia in the Open Targets Genetics  is included in the significantly enriched Apelin signaling pathway . It is related to bone marrow failure syndrome in the Open Targets Genetics. Novel top-hit genes contained in the enriched GOs and pathways from GEFOS data-specific results are summarized in the supplementary file novel_genes_in_top_GOs_pathways_study-specific.xlsx. More discussion of them is given in Supplementary Material, including genes connected to relevant traits such as osteoarthritis, osteosarcoma, and bone metastasis.The top-hit novel genes included in the enriched GO terms and pathways are likely disease genes that influence the corresponding functionalities. In particular, we obtained three top-hit novel genes in the 21 significantly enriched pathways obtained by pooling 173 top-hit genes of the BMD traits. Gene  the hip . Gene MAGenetics . Gene PRGene-based analysis has the limitation of a small coverage of the genome. For a whole-genome association study, we grouped and analyzed SNPs by haplotype blocks . The haploblock analysis could also map additional disease genes over the gene-based analysis. ATXN7L3, AXIN1, CPED1, FUBP3, LOC100272217, and SOX6. For all GEFOS studies, we had 27 literature genes and 119 literature SNPs (63 of them are outside gene regions) detected by haploblock analysis but not by gene-based analysis. Supplementary Figure S29 lists the numbers of literature genes and SNPs found by haploblock analysis versus gene-based analysis. Furthermore, the top-hit blocks (including single-SNP blocks) contained all GEFOS-reported significant SNPs, indicating no information lost compared to the original GEFOS studies.The haploblock-based analysis provided complementary results to the single-SNP analysis and gene-based analysis. As a SNP-set analysis method, haploblock analysis could detect additional disease SNPs over single-SNP analysis. For example, in the study GEFOS2012_FN, the top-hit blocks by the oTFisher discovered 56 literature SNPs that the single-SNP analysis failed to detect  contains three SNPs rs13393949 , rs4671215 , and rs7560205  that locate at a strong enhancers of gene SPTBN1 in cells Huvec  and NHEK . Gene SPTBN1 is shown associated with heel bone mineral density . By epigenetic annotation (Haploreg v3 ), 58 of  density . The enhReal-data analysis results show that the SNPs-screening procedure by the oTFisher_r could likely yield more disease SNPs than the Bonferroni procedure. Specifically, we used GEFOS2012 data sets to collect screened SNPs and validate them by a large data of osteoporosis from the UK Biobank  . Figure p-value combination test, the oTFisher, which has robustly high statistical power through adapting to unknown patterns of genetic effects. We develop computationally efficient algorithms to calculate the p-value of the oTFisher, which account for the LD of the SNPs. One advantage of such p-value combination test is that they do not assume a special type of SNP statistics. In principle, the same calculation can be carried out as long as the correlations among the SNP statistics can be estimated.GWAS summary data is a rich resource for hunting genetic factors associated with the susceptibility of human complex diseases. To facilitate analyzing such data, in this paper, we propose a SNP p-values maximizes the significance of the potential underlying genetic effects . With well-controlled type I error rate of the oTFisher, this automatic truncating scheme could serve as a vehicle for screening important SNPs that contribute to the overall association of the given SNP set. Results show that this screening procedure could better identify disease SNPs than the traditional Bonferroni and FDR procedures. Meanwhile, because the screening procedure is relatively liberal, validating these screened SNPs using an independent high-quality data set is critical for controlling false positives. Furthermore, one should always be cautious about interpreting the screened SNPs in the sense that statistical association does not necessarily mean causality. The associated SNPs could be due to LD with causal SNPs or even confounding effects. Nevertheless, the oTFisher provides a new way of exploring important SNPs not from their individual perspective but from the combined effects of the group as a whole.As given in We applied the oTFisher to a comprehensive study of osteoporosis-related traits using GEFOS data. Besides demonstrating the merit of the new method, we also generated novel genes and haploblocks that could benefit the downstream study of osteoporosis genetics. Further biological validations of these results are desired.Our GEFOS data analysis focused on gene and haplotype block-based SNP-grouping strategies for simple biological interpretability. Based on the data-adaptive omnibus testing principle, the oTFisher can also be extended to other SNP-grouping as well as annotation-weighting strategies in whole genome sequencing studies, especially for studying the noncoding regions, following the ideas proposed in recent literature , 2022.p-values were inflated, the subsequent SNP-set testing results will be inflated. Current inflation correction procedures could partially address the problem but are still limited. Further research in this direction is needed. Indeed, high-quality data is essential; we highly appreciate data-generating studies providing high-quality summary data for the sake of both primary and secondary data analyses.In general, the quality of GWAS summary data analysis highly depends on the quality of the input data. For example, if the SNP


Prostatitis is a common disease of the male genitourinary system, which seriously disturbs the physical and mental health of male patients. It is related to many factors such as living habits, age, and race, but the etiology has not been fully elucidated. This study investigated whether there is a causal relationship between clinical biochemical indicators  and prostatitis through Mendelian randomization. The subjects of the study were prostatitis patients and related SNPs in the Guangxi Fangchenggang health examination cohort.  According to the requirements of Mendelian randomization (MR), the single nucleotide polymorphisms (SNPs) related to prostatitis patients and 29 common SNPs related to clinical biochemical indicators were analyzed by linkage disequilibrium, and the calculated SNPs were selected. Finally, the related SNPs were analyzed by Mendelian randomization method. P = 0.039), which was statistically significant. The other 28 clinical endophenotypes were all negative.  15 biochemical indicators such as complement C4, FOL, CRP, HCY, and estradiol have shared chronic prostatitis SNP sites, and five qualified SNPs were finally screened for complement C4. Finally, complement C4 was obtained by Mendelian randomization method ( The results show that there was a causal relationship between complement C4 and prostatitis, and the more consistent SNP is rs2075799. Prostatitis is the third most common urinary system disease that threatens men's health after benign prostatic hyperplasia and prostate cancer . OverallThe samples were from six large-scale tertiary grade A hospitals in Guangxi. This study received consent and approval from the Medical Ethics Committee of Guangxi Medical University. The diagnosis of CP was carried out according to the CP classification standard of the National Institutes of Health (NIH) , and theThe inclusion criteria were matched by age and sex; normal subjects were selected from unrelated areas in the same area as CP patients. The exclusion criteria were no urinary system-related diseases or tumors and cardiovascular diseases, no diabetes, psychosis, hepatic and renal insufficiency, inertial diarrhea or patients with inflammatory intestinal diseases, lumbar diseases, central, and peripheral neuropathy, etc.The first phase of male health samples from the First People's Hospital of Fangchenggang was genotyped on DNA samples using Human Omni 1-Quad chip from Illumina company in the United States. The second phase of male urology outpatient samples from six large-scale tertiary grade A hospitals in Guangxi was genotyped on DNA samples using Human Omni ZhongHua-8 chip from Illumina company in the United States. The brief procedure of the experiment was as follows: amplification of whole genome DNA → endonuclease digestion to fragment DNA → isopropanol precipitation of DNA → DNA resuspension → DNA hybridization with chip → washing → single base extension → staining. After scanning the fluorescence signal by Illumina iScan chip scanning system, the data were obtained according to the different fluorescence emitted by the fluorescent groups represented by different deoxyribose bases. The obtained fluorescence data were analyzed by Genome Studio software to obtain SNP typing data files.−3.The genotyping data were filtered using PLINK 1.07 software, and strict quality control was performed on the obtained SNPs data, with the following data exclusion criteria: call rate < 0.95, minor allele frequency (MAF) < 0.01, and Hardy-Weinberg equilibrium (HWE) < 1 × 10According to the linkage disequilibrium (LD) rules based on Hapmap Phase II Han Chinese in Beijing (CHB) population release#24 panel, the IMPUTE 5 software was to fill the genome of SNP sites that had not been typed, and the sites with a posterior probability greater than 90% were reserved. And based on the same exclusion criteria as above, fill the SNP data after genome filling again.P < 5 × 10−8. (2) SNPs were independent of each other to avoid the bias caused by linkage disequilibrium (LD), when R2 of LD > 0.01, one of them was eliminated [In order to better investigate the causal relationship between clinical biochemical indicators and CP, the SNPs we selected need to meet the following criteria: (1) high association between SNPs and clinical biochemical indicators with genome-wide study significance, i.e., iminated .F value of each SNP is usually used to determine the strength of the correlation and to avoid bias from weak instrumental variables, and bias from weak instrumental variables was generally considered to be absent when the F value was greater than 10 [http://cnsgenomics.com/shiny/mRnd/). In Mendelian randomization, it was important to ensure that instrumental variables and confounding factors are independent of each other and cannot be indirectly linked to outcome variables through confounding factors. Therefore, SNPs associated with confounding factors were excluded by whether they were associated with confounding factors or not.To ensure that the correlation between instrumental variables and exposure factors was strong, the  than 10 . The staMendelian randomization estimates the relationship between genetically related intermediate endophenotypes and CP by the following three MR methods: inverse-variance weighted (IVW), MR-Egger regression, and weighted median (WM). IVW is one of the commonly used methods, but it presupposes that all instrumental variables are valid, and as long as one SNP does not satisfy the assumptions of instrumental variables, this method will be biased. Although multiple genetic variants can enhance the statistical power of Mendelian randomization analysis, due to the existence of pleiotropy, the causal relationship with CP is biased when some genetic variants do not satisfy the assumption of instrumental variables . HoweverQ test was used to evaluate the heterogeneity test of the instrumental variables [https://www.r-project.org), and differences were considered statistically significant at P < 0.05.To further evaluate the effect of heterogeneity on the causal estimation, Cochran's ariables , while oariables . StatistF values ranging from two to 12, partially biased by weak instrumental variables.The overall situation of the respective shared SNP sites associated with 29 clinical biochemical indicators of chronic prostatitis is shown in P = 0.039), as shown in P = 0.143), Mendelian randomization Egger regression , simple mode , and weighted mode , settled with a P value > 0.05  and chronic prostatitis . In the sensitivity analysis, we eliminated one SNP in turn and analyzed the remaining SNPs, and there was no one SNP that had a significant effect on the outcome effect, as shown in To ensure the credibility of this study, the MR-Egger method was used to test the instrumental variables. As shown in Mendelian randomization (MR) is an important epidemiological method that can be used for causal reasoning –23. It uComplement C4, complement C3, CRP, and IL-6 are commonly used in clinic to evaluate inflammation, but no causal relationship between C3, CRP, IL-6, and prostatitis was found in this study. However, Hartwig et al.  calculatF = 11.58), rs12660700 (F = 9.13), rs17201248 (F = 8.61), rs2075799 (F = 11.58), rs4112312 (F = 2.36), and rs9268577 (F = 4.20), only the F value of rs2075799 is 11.58. According to the ideal state, the F value was greater than 10, there was no weak instrumental variable, while it was consistent with the result resolution consistency of multiple randomization methods. Among them, rs2075799 (F = 11.58) was the ideal SNP site that meets the above criteria. The SNP of rs2075799 exist in the MHC II region of 2-mb on the chromosome, and this SNP site is highly correlated with the level of complement C4. rs2075799-related genes are related to schizophrenia, but there is no literature reported that it was related to chronic prostatitis. In MR-Egger analysis of complement C4, its intercept was close to zero, the P value was less than 0.005, and there was no horizontal pleiotropy, which was consistent with the results of inverse-variance weighted operation. The statistical results of the two are consistent, which makes it clear that the results are credible. With the development of MR methodology, a multivariate MR Egger regression analysis method for adjusting multieffects has been proposed, which was beyond the range that can be explained by genetically estimated exposure factors and has the same direction, but it was larger and more complex [Prospective studies have shown that genetic susceptibility to CRP levels is positively associated with the risk of infection in adults , 28. At  complex . These s complex . The pot complex . Some al12 F = 2., and rs9 complex . If a noFrom the perspectives of statistics, genetics, and epidemiology, we used a two-sample Mendelian randomization method for the first time to analyze the causal relationship between clinical indicators and CP, which confirmed that there is a causal relationship between complementC4 and CP.Although the sample size of this research group is not large, the sample comes from the same region and has a certain regional representation, which reduces the bias caused by different populations. The study of Mendelian randomization method is very strict with the standards of exposure factors and causal correlation. There are more Mendelian randomization study methods, each with its own basic conditions, and each method has its own advantages and disadvantages. The application of MR methods must require that any single SNP site must be strongly correlated with exposure factors, not weakly correlated and correlated with confounding factors, and it must be a unidirectional positive relationship, and there must be no multiple effects and reverse causality. It has been suggested that various methods such as sample size calculation and model hypothesis can be used to solve potential methodological problems . There aWith the development of genome-wide association studies and the continuous increase of genetic association-related data and the opening of public data, the current application of Mendelian randomization has a basis of data sources and gradually appears to be relatively simple and more widely used. However, it is still difficult to obtain reliable results from database surveys, because in the traditional inverse-variance weighting, all genetic variations must be effective instrumental variables to obtain consistent results. So sensitivity analysis of genetic variation was carried out by median regression method and MR-Egger regression method in Mendelian randomization study .This study is still deficient in that only a few of the 29 clinical endophenotypes have one to two shared SNP sites, especially when there are less than five SNP sites. Except IVW method, other methods should be inconclusive. This may lead to false negatives. This may be due to the relatively small sample size.However, this is based on the original data provided by the current GWAS study, which is a retrospective study, and the existing statistical data does not increase the number of SNP sites by increasing the number of samples. In the future, when more sample data are added and the relevant SNP sites are expanded, subsequent researchers can conduct MR analysis of these intermediate endophenotypes again.In a word, MR can find the causal relationship between exposure factors and genetic variation of outcome from molecular mechanism. The MR study of complement C4 and CP in this study shows that there is a causal relationship between complement C4 and CP, which can provide a new idea and method for the molecular mechanism and immune mechanism of prevention and treatment of chronic prostatitis. If further study can confirm, it is of great significance in the early treatment and prevention of chronic prostatitis and even in the prevention of prostate cancer.


Salmo salar). We detected an association at five loci out of 116 candidates previously identified in an aquaculture strain with maturation timing in wild Atlantic salmon. We found that at four of these five loci, variation explained by the locus was predominantly driven by a single SNP suggesting the genetic architecture of this trait includes multiple loci with simple, non-clustered alleles and a locus with potentially more complex alleles. This highlights the diversity of genetic architectures that can exist for fitness-related traits. Furthermore, this study provides a useful multi-SNP framework for future work using sequencing data to characterize genetic variation underlying phenotypes in wild populations.Characterizing the role of different mutational effect sizes in the evolution of fitness-related traits has been a major goal in evolutionary biology for a century. Such characterization in a diversity of systems, both model and non-model, will help to understand the genetic processes underlying fitness variation. However, well-characterized genetic architectures of such traits in wild populations remain uncommon. In this study, we used haplotype-based and multi-SNP Bayesian association methods with sequencing data for 313 individuals from wild populations to test the mutational composition of known candidate regions for sea age at maturation in Atlantic salmon ( The debate over the role of different mutational effect sizes in the evolution of fitness has been ongoing for a century Orr . Our abiPhilomachus pugnax) mating morphs   migratory ecotypes  analyzing the same trait did not identify a single SNP in bab that passed the genome-wide significance threshold. Alleles consisting of multiple SNPs were associated with high proportions of the variation, whereas single SNPs had only small effects and were therefore missed in the single-SNP GWAS. Additionally, Linnen at al. (2013) and Kerdaffrec et al. ? Or alternatively, did the locus emerge via recombination that gave rise to new combinations of existing variants? Numerous studies from the past decade have shown that major effect loci involve the cumulative effects of multiple mutations, rather than a single mutation, thus highlighting the relevance of considering the latter scenarios. For example, Bickel et al.  found thc et al.  also iden at al.  and Kerdn et al. , this isn at al.  and Kerdn et al. . This caSalmo salar) sea age at maturity—the number of years spent in the marine environment before reaching maturity and returning to the natal river (freshwater) to reproduce. Atlantic salmon individuals can spend anywhere from one to five years in the marine environment before maturation occurs. Prior to this marine phase, individuals spend one to seven years in their natal river before migrating to the sea. Moreover, some individuals will reach maturity in freshwater without ever having migrated to the sea, known as mature parr   v4.0.3.0., following GATK’s best practice recommendations. Picard v2.18.7  < 0.25 && ReadPosRankSum < 0.0) || MQ < 30.0”. SNPs were then filtered using SNPable procedure   greater than 0.2 within a 50 kb block using the –indep-pairwise 50 10 0.2 function implemented in PLINK v1.9  betweenvcftools  using the getfasta function in BEDTools  contains a known assembly error within the 500 kb region surrounding the known candidate loci hapQTLv1.00  were included as covariates in the models. Each hapQTL run consisted of 2 EM runs (-e 2) with 40 steps (-w 40), 2 upper clusters (-C 2), 10 lower clusters (-c 10). Three replicate hapQTL runs were performed for each of the 116 selected regions. Based on recommendations from Jeffreys  (hmin = 0.001 and hmax = 0.999) and the number of SNPs in the model  calculated with the pruned SNP dataset explained 1.96%, 0.68%, 0.63%, 0.59%, 0.56% and 0.51% of the genetic variance, respectively  association signals at 5 of the 116 candidate regions  in these regions are reported in Supplementary Fig. Multi-SNP association analysis with 19% Fig. . However19% Fig. , Table 2vgll3 and six6, likely play a key role in determining age at maturity and additional smaller effect loci may be important for fine-tuning the trait across heterogeneous environments.Despite that combined effects of multiple variants at trait-associated loci are playing an important role in controlling fitness traits across a variety of species  may help to resolve this in the future. Additionally, if an additional SNP is in high LD with the top-associated SNP, disentangling its effects from the top-associated SNP is challenging and its association signal could be undetectable post-regression of the top SNP. In such cases, study designs that take advantage of recombination events between highly linked SNPs are useful for characterizing genetic architectures at finer-scales (Sinclair-Waters et al. We do not find strong evidence of multi-SNP associations at candidate loci examined in this study, however, we cannot yet disregard the utility of multi-SNP association methods for further resolving the genetic architecture of Atlantic salmon maturation. First, we do not examine the entire genome due to computational restrictions, rather, we focussed on 116 previously identified candidate regions. Second, the Atlantic salmon genome is highly complex (Lien et al. Our analytical framework, combining both single and multi-SNP association methods, reveals that single SNP variation is sufficient for explaining the association at multiple previously identified candidate loci for Atlantic salmon maturation timing. Previous empirical and theoretical work have described trait-associated loci that have complex alleles with multiple variants, our findings therefore demonstrate the diversity of genetic architectures for fitness-related traits. Additional data, and a greater diversity of species and traits, will serve to better understand why this diversity of genetic architectures exists and how these particular genetic architectures evolve. The analytical framework used here will be a valuable resource for accomplishing this as individual-level resequencing data for wild species with phenotyped individuals becomes increasingly available.Supplementary Table S1Supplementary Table S2Supplementary Figures