Currently, the best clinical predictor for inflammatory bowel disease (IBD) is family history. Over 163 sequence variants have been associated with IBD in genome-wide association studies, but they have weak effects and explain only a fraction of the observed heritability. It is expected that additional variants contribute to the genomic architecture of IBD, possibly including rare variants with effect sizes larger than the identified common variants. Here we applied a family study design and sequenced 38 individuals from five families, under the hypothesis that families with multiple IBD-affected individuals harbor one or more risk variants that (i) are shared among affected family members, (ii) are rare and (iii) have substantial effect on disease development. Our analysis revealed not only novel candidate risk variants but also high polygenic risk scores for common known risk variants in four out of the five families. Functional analysis of our top novel variant in the remaining family, a rare missense mutation in the ubiquitin ligase TRIM11, suggests that it leads to increased nuclear factor of kappa light chain enhancer in B-cells (NF-κB) signaling. We conclude that an accumulation of common weak-effect variants accounts for the high incidence of IBD in most, but not all families we analyzed and that a family study design can identify novel rare variants conferring risk for IBD with potentially large effect size, such as the TRIM11 p.H414Y mutation. CD is characterized by discontinuous, transmural inflammation that can affect any part of the gastrointestinal tract, whereas UC presents with continuous inflammation that is usually restricted to the mucosa of the large intestine.Inflammatory bowel disease  is a chronic inflammatory condition of the gastrointestinal tract that affects about 3.7 million people in the US and Europe and shows increasing incidence in Asia.3 Compared with the general population, siblings of CD patients are at 20- to 35-fold increased risk for developing CD, and siblings of UC patients are at 8- to 15-fold increased risk.4 Overall, a family history positive for IBD is reported by 5–16% of CD patients and 8–14% of UC patients.4–6 This implies a strong genetic contribution that is indeed demonstrated by the success of genome-wide association studies (GWAS). The most recent meta-analysis of CD and UC GWAS identified and validated 163 IBD-associated loci with genome-wide significance by combining data from a total of 75,000 cases and controls.7 Most of the identified loci confer risk to both subtypes.7The strongest known risk factor for IBD is having an affected first-degree relative.8 Carrying one copy of this risk allele quadruples the probability of developing CD from the population background prevalence of a quarter of a percent to 1%. To estimate the combined effect of multiple risk variants present in an individual's genome, a polygenic risk score can be calculated.9 Owing to the lack of knowledge about interactions between genetic variants and between genes and environment, polygenic risk score models typically assume independence among these factors. Accordingly, polygenic risk scores are calculated by multiplying the odds ratios of each risk allele that is present in a genome and taking the natural log of the product.9 The predictive value of the polygenic risk score, that is, the degree to which it explains the disease variance between affecteds and unaffecteds, depends on the genetic contribution to the disease and on the extent to which the variants that have a role have been identified. For IBD, taking all the 163 associated variants into account was reported to explain 13.6% of the disease variance for CD and 7.5% of the disease variance for UC.7 This is only a fraction of the 50% CD heritability and 19% UC heritability, respectively, that were observed in twin studies.10 We expect that additional genetic risk variants exist and that their identification will improve the predictive value of the polygenic risk score. Rare variants are not typically assessed by GWAS and may contribute to the ‘missing heritability’. We therefore analyzed five families with multiple (three to six) IBD-affected family members to test the hypothesis that low frequency variants  are shared among affected family members and contribute to disease risk with intermediate to high effect sizes within these families.GWAS identify associations with common variants , which have small effect sizes. Indeed, the highest odds ratio reported for a genetic variant influencing IBD is 3.99 for a loss-of-function variant in NOD2 (p.L1007fs).11 An overview of disease onset and clinical features of the affected family members is provided in We studied five American families of European descent . Familie12 whole-genome sequencing (WGS), alignment to the reference genome (NCBI human genome assembly build 37) and variant calling were performed by Complete Genomics . The average coverage was greater than 40×.DNA was extracted from blood samples. Paired-end library preparation,13 We evaluated population frequencies of variants using Kaviar, a software program that estimates variant frequencies by taking into account a variety of public data sources as well as non-public genome sequences available from studies at the Institute for Systems Biology.14 The latest update of Kaviar includes genetic data from 77,238 individuals, comprising populations from around the world but with a bias for European-descent populations. We filtered for single-nucleotide variants and small indels that had Complete Genomics sequencing quality scores ⩾35, allele frequency ⩽3%, and a putative effect on gene function or activity, i.e., loss-of-function, missense or splice site mutations, or variants that putatively affect microRNA binding, promoter activity or enhancer activity. In addition, we filtered for variants that affect genes that are known to be of relevance for IBD in the Ingenuity Knowledge Base. These are (i) genes in a set of 292 genes directly connected to IBD, (ii) genes with interactions to any of these 292 genes or (iii) genes that have a connection with any of the 81 substances that have been related to IBD .Each family was analyzed separately because we were expecting rare family-specific risk variants, possibly in different genes in each family. To identify candidate variants, we used the Ingenuity Variant Analysis software  and the Family Genomics toolkit.7 into lists of 140 CD loci and 133 UC loci. One hundred and ten loci confer risk to both IBD subtypes and were included in both lists, but with IBD subtype-specific odds ratios. Because this set of loci does not contain two of the three well-characterized CD-associated loss-of-function variants in the NOD2 gene, we added these loci and calculated odds ratios for all the three loci based on reported frequencies in cases and controls in different European populations.15 Specifically, we used the following odds ratios: p.R702W, 2.023; p.G908R, 3.500; p.L1007 fs; 4.255. To calculate the polygenic risk score, we took the natural log of the product of the odds ratios of each risk variant that is observed in an individual’s genome, i.e., if a risk variant is homozygous, it is multiplied twice; if it is heterozygous, it is multiplied once; and if it is absent, it does not contribute to the risk score. As controls we used 1,096 ISB in-house WGS from non-IBD family studies that were also sequenced by Complete Genomics. None of the genomes were derived from a cancer sample.We divided a set of 163 previously identified IBD risk loci and their respective odds ratios17 As no crystal structure for TRIM11 was available, we used the Protein Data Bank template structure of the PRY-SPRY domain of TRIM20 (Protein Data Bank ID 2WL1). For both wild-type and mutant proteins, we sampled conformations 100 times using a Monte Carlo procedure to minimize the estimated total free energy of protein folding. The change in free energy upon mutation (ΔΔG) estimates the impact of the mutation on folding and stability, with positive ΔΔG values indicating destabilization.We modeled the functional impact of mutations in the PRY-SPRY domain on TRIM protein folding and stability with the Rosetta software.Wild-type TRIM11 was mutated to TRIM11 p.H414Y using a site-directed mutagenesis kit . HEK293T cells were transfected with reporter plasmid that harbors firefly luciferase under the control of (1) the promoter of nuclear factor of kappa light chain enhancer in B-cells (NF-κB1) or (2) the interferon signaling response element from the promoter of interferon-induced protein with tetratricopeptide repeats 1, in addition to TK-Renilla luciferase control plasmid, TANK-binding kinase-1 (TBK1)-expressing plasmid and wild-type or mutant TRIM11. At 24 h after transfection reporter luciferase activity was measured using the Dual-Luciferase Reporter Assay System .Tissue samples from the intestine of a 5-month-old C57BL6 mouse were fixed in 10% buffered formalin and embedded in paraffin. Four-micrometer-thick sections from the paraffin blocks were used. After deparaffinization and antigen retrieval by pressure cooker in 10 mmol/l sodium citrate buffer (pH 6.0) at full power for 4 min, tissue sections were treated with 3% hydrogen peroxide for 10 min. The primary antibody for TRIM11  was incubated for 30 min followed by treatment with the EnVision-HRP reagent  for 30 min. The slides were then sequentially incubated with DAB chromogen for 5 min, counterstained with Meyer’s hematoxylin and mounted for microscopy. All immunostaining steps were carried out at room temperature.We used WGS to analyze 38 individuals from five families including 15 CD patients, eight UC patients and 15 unaffected first-degree family members . Family et al.,18 and taking into account a recombination rate of 35.3 per meiosis,19 we calculated for families 1 and 2 the probability of observing an identical-by-descent segment shared by all affected family members . The probabilities for shared segments are 0.07 and 0.01 in family 1 and 2, respectively, suggesting that no identical-by-descent regions are expected other than causative segments in these families. However, Inheritance State Consistency Analysis13 on the sequence data identified a 2.6 Mb  and a 4.5 Mb  shared segment in family 1, spanning several genes as well as a gene desert. In family 2, we did not identify a shared segment.We first identified risk regions within which to focus our search for variants. Within family 1 and family 2, the affected family members are distantly related, i.e., separated by many meioses. Following our hypothesis that affected individuals in each family share risk variants, we aimed to identify genetic segments that are identical by descent among all affected family members. In pedigrees with distantly related affecteds, this approach greatly reduces the search space, and therefore the number of false-positive candidates, because with each meiosis the probability of sharing a segment that is not causing an ascertained phenotype, is approximately halved. According to Thomas TJAP1, HSP90AB1, NFKBIE and RUNX2 . We used 1,096 WGS controls available in-house at ISB from other family studies not related to IBD . To estimate the predictive value of the polygenic risk scores, we used receiver operator characteristic analysis. The area under the curve was 0.85 for discriminating CD cases from controls using the CD polygenic risk score ; for UC, we do not detect a difference between unaffected family members and controls . However, the transmission of risk variants from parents to offspring can deviate from the average 50% transmission rate per parent and this could lead to offspring with risk scores that exceed the parents' average risk score. We analyzed the transmission of risk scores from parents to offspring in our families and found that the ratio of observed risk score to expected risk score is higher in IBD-affected offspring than in unaffected offspring  [MIM 134610].23 The most severe disease mutations, p.M680I, p.M694I, p.M694V and p.V726A, are located in the PRY-SPRY domain of TRIM20.24 We used protein modeling to estimate the impact of TRIM11 p.H414Y and the pathogenic TRIM20 mutations on folding and stability of the PRY-SPRY domain. Our results indicate that mutation to Y at the TRIM11 p.H414-corresponding position in TRIM20 is potentially destabilizing, although the effect is presumably weak with a ΔΔG of 4.22 kcal/mol. Values exceeding 3 kcal/mol suggest reduced stability,25 but many potential mutations in the PRY-SPRY domain have ΔΔG values of 50 kcal/mol or higher  may have a role in IBD development in these families. For UC, the polygenic risk scores accounted for only a very small amount of the variance in these families (1%). One reason for this may be that we did not have as many UC patients as CD patients in our cohort and only one of the five families was mainly affected with UC. In general, the etiology of UC appears more complex than that of CD and may involve more contribution from non-genetic factors; the genetic associations with UC are usually smaller than for CD and the twin concordance rates are also much smaller. Therefore, explaining disease risk with a polygenic risk score based on known genetic associations is less feasible for UC than for CD.On the basis of the observed concordance rates in monozygotic twins, the heritability of CD was previously estimated to be up to 50%, and up to 19% for UC.et al.,7 that identified the loci and odds ratios we used to compute the polygenic risk scores, reported that these loci explained 13.6% of the CD variance in their cohort. Our value of the explained variance for CD, 39%, is substantially higher. One possible reason for our elevated explained CD variance is that IBD patients from highly affected families may have higher polygenic risk than sporadic IBD cases (and the majority of IBD patients recruited for GWAS are sporadic cases). We suspect that the loci contributing to the high polygenic risk scores are the main driver for the high disease burden in the families we analyzed. The accumulation of the risk may be of a stochastic nature, in part driven by the increased risk scores in both parents and in part driven by the higher than average transmission rate of common risk variants from the parents to the affected children.The study by Jostins 29 Mutations in TRIM20, better known as MEFV, can cause the autoinflammatory disease FMF in a recessive or dominant fashion.23 Interestingly, IBD-like symptoms have been observed in FMF patients and TRIM20 mutations show association with IBD in populations with a high rate of TRIM20 mutations and FMF.30–32 The majority of the pathogenic FMF mutations are located in the PRY-SPRY domain of TRIM20, which is also the location of p.H414Y in TRIM11. Our protein modeling analyses suggest that, similar to known TRIM20 mutations, TRIM11 p.H414Y affects the protein surface structure rather than protein stability and may therefore impact protein–protein interactions. The function of TRIM11 is not well understood but recently TRIM11 was reported to inhibit the TANK-binding kinase-1 (TBK1) via protein–protein interaction that does not involve ubiquitination.33 TBK1 is part of the virus-sensing signaling cascade and induces NF-κB and interferon signaling.35 Our reporter assays showed the expected decrease in NF-κB and interferon signaling when wild-type TRIM11 was ectopically expressed. The observed induction of NF-κB and interferon signaling after ectopic expression of mutant TRIM11 p.H414Y could indicate a gain-of-function effect. It has been shown that TRIM11 does not directly inhibit the kinase activity of TBK1 so it is possible that instead the interaction of TBK1 with other activating or inhibiting molecules is altered by TRIM11 binding.33 We therefore hypothesize that the p.H414Y mutation might interfere with the inhibitory effect of TRIM11 on TBK1 and instead induces TBK1, leading to a stronger inflammatory signaling in response to viruses in the gut. Several components of the NF-κB signaling pathway have been associated with IBD before.7We identified TRIM11 p.H414Y as a risk candidate that appears to have a role in IBD susceptibility in family 3. TRIM proteins are members of the RING family of ubiquitin E3 ligases and have been implicated in the regulation of innate immune signaling, including the NF-κB pathway, in which TRIM-mediated ubiquitination has been shown at several steps in the pathway.36 (ii) deep resequencing of IBD GWAS loci did not reveal risk variants with odds ratios higher than 4.5;37 and (iii) rare large-effect variants are rather a hallmark of Mendelian diseases with early (childhood) onset39 whereas IBD has a late onset, usually in adolescence or early adulthood. The only context in which highly penetrant variants have been found to cause monogenic forms of IBD is in severe very-early-onset IBD; it has been suggested that, given the distinct genetic and phenotypic characteristics, very-early-onset IBD may, in fact, represent a new disease entity that is different from classical IBD.41 The families in this study do not fall into the very-early-onset IBD category.We conclude from our results that the genomic architecture of familial IBD can involve both common and rare variant components. Common variants with small effect sizes, as have been identified by GWAS, appear to be sufficient to account for disease burden in many but not all families. In at least one family, we identified a rare variant plausibly contributing to IBD on the background of low polygenic risk. From our small study, we can neither confidently derive what fraction of families will fit each of the two models, nor what effect sizes rare variants may have in IBD. Large-effect rare variants may not be a typical driver for high incidence of IBD within families as (i) early genetic studies of IBD that used linkage analysis in families found, in comparison to monogenetic diseases, only modest linkage peaks, and no high-penetrance variants were identified in the linkage regions;To determine the effect sizes and disease risk contribution of rare variants such as the TRIM11 p.H414Y mutation, it will be necessary to study large cohorts of IBD cases and controls or families and use high-resolution approaches like WGS. We need more WGS studies of highly affected families to determine whether the results from our survey of five families will generalize to larger sets of families from many populations. In the future, we will not only need to include rare risk variants but also environmental factors in our risk models to predict disease risk with a level of accuracy that is clinically useful.


Further methodological development is needed to extend these methods to handle covariates, and to explore more powerful test alternatives.Until very recently, few methods existed to analyze rare-variant association with binary phenotypes in complex pedigrees. We consider a set of recently proposed methods applied to the simulated and real hypertension phenotype as part of the Genetic Analysis Workshop 18. Minimal power of the methods is observed for genes containing variants with weak effects on the phenotype. Application of the methods to the real hypertension phenotype yielded no genes meeting a strict Bonferroni cutoff of significance. Some prior literature connects 3 of the 5 most associated genes ( The advent of next-generation sequencing technology has allowed researchers to actively consider the impact of rare genetic variation on common disease. However, novel statistical methodologies have been needed to leverage the limited evidence provided by any single-nucleotide rare genetic variant (SNV). Two major classes of tests, collapsing and variance components, have been proposed for testing case-control association  for relationships with rare variants in arbitrarily complex pedigrees  as required for our analysis of the Genetic Analysis Workshop 18 (GAW18) data. Two recent papers ,3 proposThe only paper containing methods meeting our criteria for inclusion  is by Zhu and Xiong , who proIn this paper, we will apply the methods of Zhu and Xiong to investigate potential relationships between simulated and real phenotypes and genes, where genes consist of sets of SNVs within the gene. In particular, we are curious about the ability of the methods to maintain type I error and yield reasonable power for simulated phenotypes. Furthermore, through our analysis of the real phenotype, we will explore potential insights into disease etiology for hypertension using the GAW18 data.In the GAW18 data, real phenotype data was available for 855 individuals in pedigrees. We classified each of the 855 individuals as either ever-hypertensive or not-ever-hypertensive based on whether the individual was classified as hypertensive at any of up to 4 measurements (waves) at which phenotypic data was collected. At 849 individuals, the simulated phenotype sample was slightly smaller. When analyzing the simulated phenotype, we again focused on the (similarly defined) hypertension phenotype. In the GAW18 data, there were 200 separate phenotype simulations. Our analysis considers only simulated phenotype #1.SNVs were mapped to genes using a custom implementation of ANNOVAR, where an SNV is assigned to a gene if it is contained within the start-stop position of the gene . To imprcorrP, which summarizes the additional correlation in the samples occurring because of the complex pedigree structure. corr Pis a function of 2 times the kinship matrix. We used 2 approaches to compute the kinship matrix: (a) the theoretical kinship matrix as computed using the kinship2 R package based on stated genetic relatedness of the individuals from GAW18 documentation and (b) the estimated kinship matrix computed by estimating genetic relationships across 732,185 SNVs randomly sampled across the genome using KING [We applied 3 different family-based tests of association considered by Zhu and Xiong . The fol2 Ttest, which is a multivariate test comparing the mean allele counts across t variants  between the cases and controls; it is a multivariate version of the 2-sample t-test. Li and Leal [2 Ttest for case-control data. Zhu and Xiong prove that dividing the 2 Ttest statistic by corr Pproperly accounts for the pedigree structure yielding a t df chi-squared test statistic assuming additive polygenic inheritance.Zhu and Xiong  propose and Leal  consider2 Ttest is that in the presence of rare variants there can be power loss from the large number of rare variants (t) present, and the asymptotic distribution of the statistic may not be valid when many extremely rare variants  are present. Thus, Li and Leal [2 Ttest is then applied to the  collapsed data . Zhu and Xiong show that, similar to the 2 Ttest, adjusting the CMC 2 Tstatistic using corr Pproperly accounts for the pedigree structure. In our implementation of CMC, we used 2 MAF cutoffs: 5% and 0.5%.Of course, the downside to the and Leal  proposedp value from all single-marker tests within the gene. Zhu and Xiong compute a single-marker p value using either a Pearson p value using corrP. To generate a gene-based p value, we follow Zhu and Xiong and use the minimum p value across all variant sites within the gene. However, because we do not correct for multiple testing, we expect the type I error rate to be inflated using this approach.For comparison, Zhu and Xiong also consider taking the minimum Our analysis consisted of 2 main parts: using the simulated phenotype and using the real phenotype to investigate potential genotype-phenotype relationships in the sample. The results section is structured accordingly.We started by applying each of the tests described above to all 6625 genes, in particular, testing for association between the genotypes of the SNVs in each gene with the simulated hypertension phenotype. Table p values from the tests using the real and estimated kinship matrices are highly correlated , with the estimated kinship matrices providing lower p values on average. Thus, we will use the estimated kinship matrix for all subsequent analyses. Because of the inability of p values from T2 and CMC5% was 0.55 (Spearman = 0.54); between T2 and CMC0.5% 0.8 (Spearman = 0.80); and 0.67 between CMC5% and CMC0.5% (Spearman = 0.67).As expected, EPHA4, had a p <0.05 for all 3 approaches, which controlled the type I error rate . Of the remaining genes identified as significant by at least 1 of these 3 methods, most (6 of 10) had p values less than 0.10 for 1 of the other 2 methods, and all 10 had p values less than 0.18 for at least 1 of the other 2 methods.Table p values were below a Bonferroni-corrected alpha value of 0.05/6625 = 7.5 × 10−6 for the 6625 genes. The minimum p value for any gene with CMC5% was 1 × 10−4. For T2 there were 5 genes with p values less than 1 × 10−4 , with 1 of these (MYBPHL) being the only gene for CMC0.5%% with a p value less than 1 × 10−4.When we applied the 3 remaining tests to the real hypertension phenotype on the entire sample of 855 individuals using the estimated kinship matrix, no minimum Few methods currently allow for the analysis of binary phenotypes with rare variants in complex pedigrees. We have applied 4 of the published methods to both real and simulated phenotypes. While the methods appeared to control the type I error rate for smaller genes (less than 200 SNVs), they performed poorly (low power) when the variance in blood pressure was small. Although this finding is based on the simulation model for GAW18 data, power to detect causal genes may still be a significant hurdle in real analyses.We considered the use of both the theoretical and estimated kinship matrices. We found that use of the theoretical kinship matrix proved slightly overconservative, while using the estimated kinship matrix provided empirical type I error rates in line with the nominal levels. Even though the precise reasons for the overconservative nature of the theoretical kinship are unknown, we surmise that cryptic relatedness and population stratification between pedigrees, which are not reflected in the theoretical kinship matrix, will be controlled through use of the estimated kinship matrix. Because these issues may be present in any data set, we recommend estimating kinship matrices in practical applications of family-based tests of association for rare-variant data.2, CMC5%, and CMC0.5%), each test will be most powerful for particular genetic architectures. In particular, for truly causal genes the proportions of causal rare or common variants versus noncausal rare and common variants will determine which method is best. Because the "best" method here will be a product of the simulation strategy taken, we do not wish to extrapolate this simulated genetic architecture to suggest the use of one approach over the others.Even though minor differences in power existed across the 3 tests considered here  [ST6GALNAC2 is associated with susceptibility to IgAN, which is commonly associated with hypertension  cholesterol [TRAT1 and DHX8 have little exposure in the literature related to hypertension or related outcomes. We note, however, that none of these genes reached a conservative (Bonferroni-corrected) significance threshold, nor did we replicate SNVs known to be associated with hypertension in large genome-wide association studies.In applying the 3 tests to the real phenotype, we found modest evidence of a relationship between a handful of genes and hypertension. These genes are featured in a modest amount of prior literature, suggesting their potential association with blood pressure, cardiovascular development, and renal function. In particular, egnancy)  and maliegnancy) . ST6GALNsee Ref. ). MYBPHLlesterol . TRAT1 aIn addition to the lack of power from small sample size and, potentially, the choice of analysis methods, we note that our approach did not consider covariates because the methods considered in this paper do not provide obvious approaches to control covariates. As noted in the introduction, there is a dearth of methods available for the analysis of data like that in GAW18, and we know of few methods currently available that work for analysis of rare variants with binary traits on complex pedigrees, allowing for control of covariates and for which software is publicly available. With increased interest in family-based designs for the analysis of rare-variant data, further methodological development is needed in this area.The application of methods for the analysis of rare-variant data collected on complex pedigrees for relationship with a binary phenotype suggests the potential for modest power for large variant effects with a sample of 855 individuals, but minimal power for variants and genes with weaker effects. Application of the methods to a real phenotype found modest evidence of association between the hypertension phenotype and five genes, three of which have limited prior association with hypertension-related phenotypes. Further methodological work is needed to develop more powerful methods allowing for control of covariates in the analysis of complex pedigrees and for use on larger genes.The authors declare that they have no competing interests.All authors participated in design of the overall study; AL and AB carried out various aspects of data preprocessing, including gene mapping and building preliminary data analysis files; BG estimated the kinship matrices; CA, AH, and NT ran statistical tests, analyzed data, and conducted literature review. NT drafted the final manuscript. All authors read and approved the final manuscript.


Drosophila melanogaster from temperate (European) and tropical (African) regions. This resulted in two relatively small regions of 131 kb and 124 kb. The latter one co-localizes with a very strong selective sweep in the European population. We examined the genes within and near the sweep region individually using gene expression analysis and P-element insertion lines. Of the genes overlapping with the sweep, none appears to be related to CCRT. However, we have identified a new candidate gene of CCRT, brinker, which is located just outside the sweep region and is inducible by cold stress. We discuss these results in light of recent population genetics theories on quantitative traits.There is a growing interest in investigating the relationship between genes with signatures of natural selection and genes identified in QTL mapping studies using combined population and quantitative genetics approaches. We dissected an X-linked interval of 6.2 Mb, which contains two QTL underlying variation in chill coma recovery time (CCRT) in  Quantitative genetics assumes that selection on an adaptive trait involves simultaneous selection at multiple loci contributing to this trait. This causes small to moderate allele frequency shifts at these loci . Therefoebony have contributed to the darker color of the abdominal segments of high-altitude Drosophila melanogaster from Uganda  just outside the sweep is induced by cold stress.Sophisticated methods have been developed to detect selective sweeps in a genome . In thishttp://flystocks.bio.indiana.edu). Although the QTL interval was defined by To conduct quantitative complementation tests on chromosomal deletions, a set of available deficiency lines were ordered at the Bloomington stock center  locus, F1 flies exhibiting the 1B mutant phenotype were considered as balancer bearers, whereas wild-type appearance was indicative of bearing the deletion. Sorted flies were kept at room temperature until CCRT scoring on their fourth to sixth day of life.Prior to CCRT scoring experiments, virgin female flies bearing the deficiency chromosome and the respective balancer were mated with males of the A* and E* lines, respectively. Eggs were allowed to develop in the same medium in which they were laid at 23°. Female F1 were sorted on hatching by phenotype as balancer or deletion bearer. Because all balancer types used to maintain the deletions have a dominant mutation for eye shape at the Assessments of expression levels of candidate genes were conducted using 4- to 6-day-old female flies belonging to the Netherlands population  and the Zimbabwean population . Flies were reared at 23° and subjected to cold stress in the same manner as reported for CCRT scoring. Three flies per line were used as controls (not exposed to cold). Three flies of each line were snap-frozen in liquid nitrogen at 10 min after being brought to room temperature, whereas three remaining flies per line were scored for their CCRT and frozen 15 min after the minute in which they were reported as recovered. Control flies, which remained at 23° in glass vials during the 7 hr of treatment, were also snap-frozen at the end of this time period. Frozen material was stored at −80° until RNA extractions were performed. Population pools per line/treatment were made prior to RNA extraction. Each population pool per treatment consisted of eight flies of the same population. Three population pools per treatment were made for both the Netherlands and Zimbabwe.Once flies reached 4 to 6 days of age, they were scored for CCRT following the protocol of E*/def, A*/def, E*/bal, and A*/bal were scored. For ANOVA analysis, log-transformed CCRT scores per genotype, line (L), and genomic background (G) were kept as fixed effects. We focused on the significance of the interactions of these two factors (L × G) as well as on the following two conditions to call the procedure quantitative failure to complement: the differences in CCRT for the genotypes bearing the balancer had to be small compared with that of the genotypes bearing the deletion; and in the latter case, the E*/def flies should show reduced CCRT with respect to the A*/def genotypes. It is expected that if these conditions are satisfied, failures to complement due to the presence of other QTL outside the region in question can be ruled out. However, a failure to complement detected with a given deficiency either can be caused by its interaction with QTL alleles in the region uncovered by a deficiency  or arise by interaction between this deficiency and QTL alleles elsewhere in the genome (epistasis)  . BecauseRNA was extracted from pools using the MasterPure RNA Purification Kit , followed by DNase treatment. Purified RNA was quantified with a nanodrop apparatus and tested for genomic DNA contamination based on a PCR (Phusion) protocol using a primer pair binding in nontranscribed regions of the X-chromosome . Samples tested positive for genomic DNA were excluded from downstream protocols. cDNA synthesis was performed with SuperScript III Reverse Transcriptase  on 1400 ng of RNA per reaction.CG1958, CG1677, CG2059, unc-119, brk, and Atg5 were performed with primers designed using the online tool QuantPrime (www.quantprime.de) to match all possible transcript types per candidate gene. The ribosomal genes RpS20 and RpL32 were taken as reference genes, against which relative gene expression levels of our genes of interest were normalized. RT-qPCR assays consisted of a total of three biological replicates each run in triplicate and were conducted on a Real-Time thermal cycler CFX96 platform . Each reaction was taken to a final volume of 10 µl using iQ SYBR Green Supermix . Further details of the experimental setup, such as amplification efficiency assessments with dilution series, can be provided on request.RT-qPCR assays for candidate genes t-tests were performed on comparisons with fold differences above a threshold . The P-value correction was applied to control for false positives, due to the high number of simultaneous tests performed.Obtained Cq values per replicate within line (or pool) and treatment were transformed to calibrated normalized relative quantities (CNRQ) following Df(1)ED9606; i.e., a total of 124 kb between coordinates 7,089,000 and 7,212,999. Publically available whole-genome sequences generated by Illumina NGS technology for four D. melanogaster populations were retrieved from the Drosophila Population Genomics Project (DPGP) at http://www.dpgp.org. The populations include the Netherlands (NL) with 11 lines , France (FR) with 8 lines , Rwanda (RG) with 25 lines , and Southeast Africa consisting of a pool of 18 lines from Zambia , Zimbabwe , and Malawi . The corresponding D. simulans sequence (Molecular variation was characterized in the genomic fragment uncovered by the deletion π (W (xyD) to the outgroup , we considered two additional site classes consisting of sites that are monomorphic in the European sample and polymorphic in the Rwandan sample. Extending the SFS in this way was shown to improve the power of the method to detect selective sweeps  statistic , input files were prepared following the authors’ instructions. The different runs were performed using default parameters with the sequence data of the same DPGP samples from the Netherlands, France, Rwanda, and Southeast Africa. In addition, lines from the following African populations were included: Ethiopia , Cameroon , and South Africa . SNP exclusion criteria were as follows: positions showing more than two segregating alleles as well as sites with less than 50% base calls in one population were excluded from the analysis.For the set of FBayeScan  . We observed failure to complement in 4 of the 24 tested deletions (L) and a significant line by genomic background interaction (L × G) as long as the differences in CCRT followed the expected direction. That is, there were shorter CCRT times for flies bearing the E* X-chromosome in the presence of the deletion compared with the corresponding flies bearing the A* X-chromosome in the presence of the same deletion, but no difference was shown between the CCRT of the flies bearing the E* and A* X-chromosomes in the presence of the balancer chromosome.To dissect the QTL interval reported by eletions . FailureDf(1)ED6906 and Df(1)C128 were the only ones that revealed a highly significant failure to complement. In the case of Df(1)ED6906, the difference between the means of the CCRT scores for the flies bearing this deletion is 9.18 min, whereas that of the flies harboring the balancer chromosome is 1.82 min (Df(1)ED6906 has potentially uncovered E* X-chromosome alleles and/or alleles with the opposite effect residing on the A* X-chromosome. A similar interpretation can be given for the results obtained for Df(1)C128, Df(1)BSC592 and Df(1)BSC537, which also failed to complement, as evidenced by the significant L and L × G effects and by the higher CCRT differences in the presence of the deficiency than in the presence of the balancer. However, for the two last deletions, these effects were only marginally significant ED6906 and Df(1)C128 meet the requirements to be considered allelic failures to complement, these can also be interpreted as an epistatic failure to complement due to interactions of these deficiencies with other loci that affect CCRT residing elsewhere on the X or in the other two major chromosomes. We are aware of the limitation of quantitative deficiency mapping to tell these two causes apart (Although our results with Df(1)ED6906, the 67.15 kb overlapping with deletion Df(1)BSC536 were subtracted from the stretch of interest (Df(1)BSC711) allowed us to subtract an additional 19.64 kb from the 210 kb encompassing Df(1)ED6906 (Df(1)ED6906, its overlap with deletion Df(1)HA32 is not known at the base pair level. Thus, the redefined fraction of interest under deletion Df(1)ED6906 encompasses 124 kb between coordinates 7.09 and 7.21 Mb. Similarly, for the other highly significant deletion Df(1)C128, the redefined region of interest has a of length 131 kb (between coordinates 7.85 and 7.98 Mb).The fact that we used a set of overlapping deficiencies allowed us to better define the stretch that revealed quantitative failure to complement. With respect to the 210-kb-long deletion interest . Further1)ED6906 . At the Df(1)ED6906, and 14 genes were uncovered by deletion Df(1)C128. This is remarkable given the substantial fraction of uncharacterized genes in the 6.2 Mb of the QTL defined by a priori candidate genes for CCRT in this chromosomal region.This quantitative complementation mapping approach based on overlapping deletions has allowed us to reduce the number of initial candidate genes within the QTL undergoing study from 622 to a subset of 21. A total of 7 genes are located within the 124 kb uncovered by deletion Df(1)ED6906 overlaps with a selective sweep identified in several previous analyses (Df(1)C128 (see below). Therefore, in the following section we focus on deletion Df(1)ED6906.In the next section we show that of the two regions identified by the complementation tests, the 124-kb region uncovered by deletion Df(1)ED9606 in natural populations of D. melanogaster. First, we calculated a set of summary statistics on a 2-kb nonoverlapping window basis using next-generation sequence data from two European (the Netherlands and France) and two African (Rwanda and Southeast Africa) populations. The Netherlands population and a set of Southeast African lines represent the gene pools from which the E* and A* lines were derived. The additional two populations consisted of French and Rwanda sequence data from the DPGP (We characterized molecular variation in the genomic region of 124 kb uncovered by deletion π) and Watterson’s estimator (θW). The European populations showed a three-fold to four-fold reduction in nucleotide diversity when compared with the African populations  1 SD lower than the average over the 124-kb region. This pattern is in contrast to that observed in the two African populations for the same coordinates, for which nucleotide diversity values tend to be above their respective population averages. FST estimates along the 124-kb region between each of the European populations and the Southeast African pool parallel the diversity estimates such that FST vales are higher where diversity is low in the two European populations . Regarding divergence of each population to D. simulans, the averages of the two European populations are approximately 10% whereas those of the African populations have values between 12% and 13% . The African samples from Rwanda and Southeast Africa show generally negative D values that are typical for these populations .We also analyzed possible deviations from the standard neutral SFS using Tajima’s D in both European populations that extends for approximately 40 kb between relative position 65,000 and 105,000 in the 124-kb region. This reduction already has been identified as a footprint of positive selection in non-African populations . In our region of interest, the fragment between relative positions 65,000 and 105,000 exhibits a SFS that is in contrast to that of the genomic background and is better described by a selective sweep model (CLR >300) are within the top 1% of CLR values along the entire region of the X-chromosome analyzed  and are above the significance threshold of 72 that corresponds to the 95th quantile of the top CLR values of 100 simulated subgenomic regions of 5 Mb. This value did not increase when larger genomic regions were simulated . Simulations were based on our current understanding of the demographic history of European populations (Because demographic scenarios (particularly bottlenecks) can create similar genomic patterns as positive directional selection , we subjep model . The CLRDf(1)C128, the second highly significant deletion uncovered by the quantitative complementation test (between coordinates 7.85 and 7.98 Mb). However, we could not find evidence for selective sweeps .Using the same CRL test, we also analyzed in detail CG1958, CG1677, CG5059, and unc-119; see the gene model below)   containsficients . We consficients , we obtaDR of 5% . These fDR of 5% . The 65-T values . HoweverThe exclusion of European populations from the analysis did not change the pattern of high-differentiation outlier SNPs (results not shown). This suggests that allele frequency differentiation at outlier SNPs had already started within the African continent. Furthermore, because the European populations probably have experienced more severe bottlenecks than the African populations  and 86,670T/C . These two nonsynonymous SNPs are located in exon 5 of the computationally predicted gene CG1677 and show alleles in perfect linkage disequilibrium (LD)  is in high frequency in the Southeast African samples and is intermediate in Rwanda; its frequency decreases with increasing latitude to be replaced in the European populations by the CC haplotype. Both SNPs predict changes in the amino acid sequence of the protein. The common Southeast African form of the protein codes for a threonine (Thr) and an asparagine (Asn) at residues 936 and 939, respectively, whereas the cosmopolitan form has an alanine (Ala) and aspartic acid (Asp) at these two positions. The third highly significant SNP is 80,089A/G  located between genes CG1958 and CG1677  is located 5 kb upstream of gene unc-119 within the large intron of gene CG1677  . The TT d CG1677 . Its alle CG1677 .brinker and Atg5, but spans four of the seven candidate genes in the 124-kb region (see gene model below) . Expression of candidate genes was measured at two time points after cold stress exposure as well as under control conditions. The two post-cold stress time points were 10 min after the end of cold stress and 15 min after flies recovered from chill coma. Controls consisted of flies of the same lines that were not subjected to cold stress.We observed that the CLR profile of the selective sweep does not overlap with l below) . To analincluded . qPCR asCG1958 and brinker showed significant differences in constitutive expression levels between the Netherlands pool and the Zimbabwean pool (P < 0.01) (brinker (P < 0.001). Expression levels measured at 15 min after recovery from chill coma revealed one significant difference within pools: brinker was significantly overexpressed with respect to controls in the Netherlands pool (P < 0.05). Between-pool contrasts at 15 min after recovery from chill coma revealed only a significant difference for brinker (P < 0.01). This suggests that—of the six candidate genes—only brinker is induced by cold stress and may contribute to CCRT variation between temperate and tropical populations.Of the six genes,  < 0.01) . This dibrinker induction on the A* and E* lines individually. Again, we found that brinker is significantly induced 15 min after recovery from chill coma only in the E* line (P < 0.01). Furthermore, we noted that the constitutive expression differences between A* and E* disappeared (in contrast to the experiment with the African and the Netherlands pools described above), presumably because the A* and E* lines share the same autosomal background (data not shown).We also investigated CG1677 and unc-119) for which lines with P-element insertions were available. None of these tests (performed in the same way as with the deletions) revealed quantitative failure to complement (CG1677 and unc-119) affect cold tolerance.Finally, we performed quantitative complementation tests on two of the four genes under the sweeps (mplement . This fuDf(1)ED6906. Third, we investigated the genes within and near the sweep region by gene expression analysis. We found no evidence that the four genes within the sweep region  are related to cold tolerance. However, this analysis also revealed a new candidate gene related to CCRT: brinker, a gene located just outside the sweep region that was induced by cold stress. In the following, we discuss these results, including the methods used.First, we dissected a QTL interval for CCRT (a proxy for cold tolerance) using quantitative complementation tests. This approach revealed two deletions that failed to complement. Second, we used population genetic methods to narrow the number of genes in these two deletions. This approach led to the precise demarcation of a strong selective sweep in deletion L × G interactions were found in the presence of significant L effects. This is not uncommon in D. melanogaster  could be reduced to seven candidate genes. However, there is a caveat, because for both deletions significant Df(1)ED6906 in the European population is very strong, the strongest on the entire X-chromosome analyzed . It encompasses approximately 40 kb (with boundaries that are sharply defined). Based on the demarcation of the sweep, we observe that brinker, the only candidate gene that was induced by cold stress, is located outside the sweep. The four genes under the sweep are not induced in both African and European populations. CG1958 is differentially expressed at the constitutive level (CG1677 expression is increased relative to constitutive levels during sustained cold stress (CG1677 and unc-119) for which lines with P-element insertions were available. None of these tests (performed in the same way as with the deletions) revealed quantitative failure to complement  in coding regions, we need to analyze the sweep profile in more detail. Yet, because variation is almost completely depleted in this genomic region, we cannot use the CLR approach even if we include LD  (Do these adaptive fixations have anything to do with cold adaptation? The protein encoded by l below) . There ibrinker located just outside the sweep is related to this trait. A similar observation was made previously for another QTL of cold tolerance in D. melanogaster . This raises the question of to what extent CCRT is under stabilizing selection and to what extent CCRT is under directional selection. Although there is evidence that cold tolerance may have experienced positive directional selection from one optimum in Africa to another optimum in Europe, it is currently unclear whether this optimum shift is sufficiently large to overcome stabilizing selection that is expected to be widespread .One of our salient observations is that the genes within the selective sweep region do not affect CCRT, whereas brinker is a candidate gene affecting variation in cold tolerance. However, it is important to note that brinker is located outside the large selective sweep described above and thus is not affected by the strong selection generating this sweep. This is consistent with current theory that sweeps at genes controlling phenotypic traits under stabilizing selection are expected to be rare (see above).Based on our gene expression study , brinkerbrinker and found one conspicuous indel polymorphism  at relative positions 109,442 to 109,976, i.e., approximately 3 kb upstream of brinker and thus also outside the sweep region. This indel is located close to a polymorphic marker  that is significantly associated with CCRT in a Raleigh population . Based on linear regression analysis of the frequencies of the nondeletion haplotypes, we detected two antiparallel latitudinal clines where one spans from the populations near the equator  to the north (France and the Netherlands) and another one from the equator to the south (Southeast Africa and South Africa) (P < 0.05 in both cases). This agrees with models of weak selection on highly polygenic traits. However, to what extent the observed frequency differences from the equator to the north and to the south explain the expression differences of brinker between tropical and temperate populations  predict the occurrence of allele frequency shifts between populations as a hallmark of polygenic selection (ions see  is curre


With the objective of making advancements in the area of pro-thrombotic microparticle characterization in cardiovascular biology, we present a novel method to separate blood circulating microparticles using a membrane-based, nanopore filtration system. In this qualitative study, electron microscopy observations of these pro-thrombotic mouse microparticles, as well as mouse platelets and leukocytes obtained using a mouse inferior vena cava ligation model of deep-vein thrombosis are presented. In particular, we present mouse microparticle morphology and microstructure using SEM and TEM indicating that they appear to be mostly spherical with diameters in the 100 to 350 nm range. The nanopore filtration technique presented is focused on the development of novel methodologies to isolate and characterize blood circulating microparticles that can be used in conjunction with other methodologies. We believe that determination of microparticle size and structure is a critical step for the development of reliable assays with clinical or research application in thrombosis and it will contribute to the field of nanomedicine in thrombosis. Our focus is on venous thromboembolism (VTE) . VTE incal cells . The actal cells ,6,7. Thial cells . MPs theal cells ,10 It isal cells ,12,13 anal cells ,15 whereal cells ,17. In recent years, circulating MPs have received attention as potential biomarkers in the diagnosis and prognosis of disease, including thrombosis. However, such studies have been hampered by the lack of accurate methods for the detection and quantification of MPs. Despite the great efforts by experts in vascular biology, the field of thrombosis is facing the problem that the methods currently being used for detection and/or quantification of MPs are inadequate. They involve techniques that do not “visualize” the microparticles and, unfortunately, do not provide enough information regarding their physical characteristics, like flow cytometry  or functIn this study, MPs were generated using a well-documented mouse inferior vena cava (IVC) ligation model of DVT ,22,23,24It is important to note that most of these previous works have studied MPs in human samples. To our knowledge, visualization and characterization of these MPs in mice has not been previously performed. In addition, we are not aware of any study in which MPs have been visualized by TEM or SEM after artificial induction of the thrombotic event in a mouse model.Twenty C57BL/6 male mice  weighing 20-25 grams were used and blood was collected in acid citrate dextrose  to obtain blood cells and MPs from two groups: true control (TC) mice, where no IVC ligation was performed; and mice subjected to the IVC ligation procedure, from now on referred to as (2D), because the extraction of the MPs took place 2 days after ligation ,22. A flThis protocol is one of the regular separation methods for MPs . 0.5 mL 0.5 mL of whole blood was drawn from each mouse, by terminal cardiac puncture, with a 26 g needle into a 1 mL syringe pre-loaded with 0.05 mL of ACD . We used0.5 mL of whole blood draw was extracted from each mouse by terminal cardiac puncture and transferred into 10% acid citrate dextrose (ACD). Platelet rich plasma (PRP) was obtained by centrifuging blood at 200 g at room temperature for 10 min. Cell suspensions were then used for TEM and SEM analysis.After re-suspension in HEPES buffer and for all separation procedures, MPs, platelets, or leukocytes were fixed for 30 minutes in 2% formalin. The suspensions with cells were each separated in three equal aliquots. One aliquot was filtered with an 80 nm filter; a second aliquot with a 400 nm filter and the third aliquot with a 1 µm filter. The same procedure was repeated for the MPs suspensions. Samples were then dehydrated through a graded series of ethanol and dried overnight with hexamethyldisilazane . A thin film of gold was deposited on the samples using a common gold sputterer. SEM micrographs were taken with an Amray 1910 Field Emission SEM.MPs separated using MPS-ULTRA procedure and platelets or leukocytes separated using PLAT procedure were pelleted and then fixed with 2.5 percent glutaraldehyde in 0.1 M Sorensen’s buffer , pH 7.4. The fixed cell pellets were then encapsulated in Histogel (Thermo Scientific). After several buffer rinses, they were post-fixed for one hour in one percent osmium tetroxide  in the same buffer. Next, they were rinsed in Sorensen’s buffer and then dehydrated in ascending concentrations of ethanol. They were then treated with propylene oxide , and infiltrated and embedded in Epon epoxy resin . Semi-thin sections were stained with toluidine blue for tissue orientation. Selected areas were ultra-thin sectioned 70 nm in thickness, applied to copper grids and post-stained with uranyl acetate and lead citrate . They were examined using a Philips CM100 electron microscope at 60 kV. Images were recorded digitally using a Hamamatsu ORCA-HR camera system operated using AMT software .Following the PLAT protocol, our studies using separation by filters and electron microscopy revealed that mouse MP sizes from TC are under 1 µm in diameter and that their shape is spherical or near spherical. Our results were separated in The SEM images obtained from a 400 nm filter C,D and 8A) ; (B) (TC MPs extracted using MPS-ULTRA); and (C) (2D MPs extracted using MPS-ULTRA). The low concentration of MPs obtained for samples (B) and (C) prevented to reach any definitive conclusion regarding differences in morphology of the TC MPs and the 2D MPs.An attempt to compare the aspect of the MPs between TC and 2D was made . No concWe observed low concentration of MPS on the filters. To increase this concentration and to facilitate imaging, a procedure without centrifugation was devised  consisting of a small amount of cytoplasm and a very well structured plasma membrane characterized by a controlled transverse lipid distribution termed “rafts” . Given tMethods typically used for determination of size do not provide visual evidence of the characteristics of the particles and there are increased discussions regarding what MPs size and number these techniques actually provide . These iWe note that we used standard protocols for extraction of microparticles published in the literature and commonly used by one of the authors, including the type of fixatives, with the innovative modification of using filters for their separation by size. We initiated our analysis of MPs using an animal model of DVT where thrombosis can be surgically induced in mice, which permits the analysis of the generated MPs during thrombus formation, in this case at day two. This controlled analysis cannot be performed in humans. We are not aware of previous publications where pro-thrombotic microparticles from mice were studied using SEM and TEM. The selection of studying blood from an animal at two days after IVC ligation is based on the fact that MP concentrations are known to be the highest at this particular time point, based on previous studies . From daPrevious investigations have suggested that MPs are very heterogenic in size . MPs larOn the other hand, small MPs may overlap in size with a group of particles known as exosomes (40-80 nm), which are derived from multivesicular bodies and have different biological origin and function . We did Our experiments were always performed with fresh samples but it has been reported that cold storage and ultracentrifugation may differentially affect MPs of different biological origins . Is not et al. [We also observed that the concentration of MPs visible in the filters during some of the filtration procedures were low. In addition, all platelets were retained in the 1 µm filter thus we concluded it could be possible to get increased concentrations of MPs by using a filtration, instead of an ultracentrifugation, method (MPS-NOULTRA). To verify these assessments, an experiment was performed using MPs obtained from blood at 2D post-ligation without ultracentrifugation (MPS-NOULTRA), obtained from a poor plasma platelet suspension and separated using a sequential filtration process. As mentioned, the observations confirm that the size of the MPs is mainly in the 100 to 350 nm range. In addition, the technique of sequential filtering appears to produce higher concentrations of MPs than when ultracentrifugation is used, suggesting that this technique  and the regular MP extraction (MPS-ULTRA) procedure, three types of MPs were observed, as seen in In conclusion, the data presented will help advance the exploration into MPs size and provides visual evidence of their morphology. We believe the technical approach of using filters to separate MPs should be explored in more detail, particularly if used in combination with other novel technologies for the study of these biological particles . We limi


Low-level stochastic vestibular stimulation (SVS) has been associated with improved postural responses in the medio-lateral (ML) direction, but its effect in improving balance function in both the ML and anterior-posterior (AP) directions has not been studied. In this series of studies, the efficacy of applying low amplitude SVS in 0–30 Hz range between the mastoids in the ML direction on improving cross-planar balance function was investigated. Forty-five (45) subjects stood on a compliant surface with their eyes closed and were instructed to maintain a stable upright stance. Measures of stability of the head, trunk, and whole body were quantified in ML, AP and combined APML directions. Results show that binaural bipolar SVS given in the ML direction significantly improved balance performance with the peak of optimal stimulus amplitude predominantly in the range of 100–500 μA for all the three directions, exhibiting stochastic resonance (SR) phenomenon. Objective perceptual and body motion thresholds as estimates of internal noise while subjects sat on a chair with their eyes closed and were given 1 Hz bipolar binaural sinusoidal electrical stimuli were also measured. In general, there was no significant difference between estimates of perceptual and body motion thresholds. The average optimal SVS amplitude that improved balance performance  was estimated to be 46% in ML, 53% in AP, and 50% in APML directions. A miniature patch-type SVS device may be useful to improve balance function in people with disabilities due to aging, Parkinson’s disease or in astronauts returning from long-duration space flight. Stochastic resonance (SR) is a phenomenon based on the concept of maximizing the flow of information through a non-linear system by the presence of non-zero level of noise –3. StochOver the past century the transcutaneous delivery of electric currents to the vestibular afferents commonly referred to as galvanic vestibular stimulation (GVS) has been used to study and understand the function of the vestibular system (for review see ). BilateIntensities of electrical stimulation scaled to a fixed ratio of subjective thresholds used in previous studies showed improvement in physiologic functions with the SR phenomenon. Threshold to the imposed stimuli could be defined, as in previous studies, by examining physiologic responses (body motion response or eye movement response) or perceptual responses, based on subjects identifying what they feel  either using a stochastic signal or a sinusoidal stimulus profile. Some studies have used electrical stimulus amplitudes that were chosen to be 90% of a physiologic threshold such as nystagmus  or 60% oData presented in this paper were collected during four different studies, on healthy subjects as shown in For the first three studies, 35 subjects were recruited from the Human Test Subject Facility at NASA—Johnson Space Center (JSC) in Houston, TX, USA. Study 1 and 2 used 15 healthy subjects each, and Study 3 used 11 healthy subjects, six of whom were repeat subjects from Study 2. All of these subjects had passed the equivalent of an Air Force Class III physical examination within 12 months of beginning the study. In Study 4, 15 healthy subjects were tested at Baylor College of Medicine (BCM). These 15 subjects underwent head-thrust, Dix-Hallpike, and vestibular evoked myogenic potential (VEMP) tests to screen them for any vestibular impairment. No subject had any reported history of otologic, neurologic, cardiovascular, orthopedic, or traumatic disorder. For the 50 subjects, mean ± standard deviation (SD) age was 34.5 ± 9.5 years, height was 173.9 ± 10.0 cm, and weight was 75.4 ± 14.3 kg, with normal or corrected-to-normal vision.The experimental protocols were approved by the Institutional Review Boards at NASA JSC and BCM. All subjects gave their written informed consent before participating in the study and were free to withdraw at any time.For each subject, the 5 cm x 10 cm electrodes  were centered over the mastoid processes on both sides using methods described in our previous paper and described briefly herein . The skiBalance performance was assessed in Studies 1, 2 and 4 see . SubjectThreshold estimation was conducted in Studies 2, 3, and 4 see . SubjectSubjects were asked for feedback and comments after the tasks on any unpleasant sensation they might have felt during the tasks. In addition, in Study 4, subjects were asked to use the second joystick available on the gamepad with the non-dominant hand to report any non-motion sensation occurring during the thresholding task. The specific instruction given was: “Sometime, people can feel metallic taste in mouth, flashing in eyes, prickly or tingling sensation as well. Use the non-dominant hand to indicate these sensations by pulling the other joystick to either left or right and holding it until the sensation goes away”. Further, they were also asked to complete a simple questionnaire immediately after the thresholding and balance tasks, to track the presence and severity level of any unpleasant perceptions during testing (adapted from ). SubjecDuring both tasks, a portable constant-current stimulator with subject isolation was used to deliver a bipolar constant current stimulus in the ±5 mA range to a load of up to 10 kΩ. The stimulator was powered using a 3.7 V battery pack and was operated in a standalone mode with the stimulation profiles stored on its internal flash memory card.The duration of each balance trial was up to 44 s with two periods: baseline period lasting for up to the first 21.5 s had zero amplitude levels, while stimulus period spanning the remaining 22.5 s presented the signal at the selected amplitude level. Stimulus levels used in the different studies are presented in The thresholding task was designed to identify the level of vestibular stimulation at which subjects were able to discern/or exhibit motion induced by the stimulation. Previously, in studies investigating effects of GVS, a periodic stimulus had been used to identify sensory threshold, which is usually the intensity at which the experimenter observed body sway at the input frequency , 12, 29.In general the stimulus profile consisted of 15 s periods with the 1 Hz sinusoidal stimulation signals, interspersed with 20 s periods of no stimulation. Based on feedback from Study 2, durations of baselines preceding the 15 s stimulation bursts were varied randomly in the range between 20 s and 23 s in Studies 3 and 4 in order to reduce the predictability of timing of the start of stimulation signals during the thresholding task. The different current peak-to-peak amplitude ranges used for Studies 2, 3 and 4 are shown in All data analyses were performed using MATLAB  scripts and functions. The force plate and IMU data acquired for the middle 20 s during each of baseline and stimulus periods were first filtered at 10 Hz using a first order, zero phase response, low-pass Butterworth filter. The root mean squares (RMS) of 12 physiologic measures (six each in ML and AP direction) were calculated to characterize the balance performance of subjects during baseline and stimulus period for each of the 11 current ranges. The six balance parameters in the ML direction were RMS of: shear force (Fy), roll moment (Mx), linear accelerations for the head, and trunk segments , roll angular velocity for the head and trunk segments  and the six balance parameters in the AP direction were RMS of: shear force (Fx), pitch moment (My), linear accelerations for the head and trunk segments , pitch angular velocity for the head and trunk segments . The first two parameters in each direction reflect postural responses across all segments of the subject and were calculated using the force plate data , and the remaining four parameters reflect the upper body segmental postural responses  and were calculated using the data from IMUs attached to the head and trunk segments.Methods similar to those used in our previous paper  were useWe pooled together data for all parameters from the control and optimal trials for all subjects for further analysis. Data from all measures were then compared using multivariate repeated measures ANOVA. This analysis used all six measures for the ML and AP directions, and 12 measures for APML direction, with two within-subject factors: Period (2 levels—baseline and stimulus), and Trials  at a significance level of 0.05. In addition, a paired t-test was used to compare performance, using the ratios of all parameters for stimulus with respect to baseline periods, between the control and optimal trials at a significance level of 0.05.Raw joystick data were used in data analysis. For body motion measures, the data were first filtered at 1.5 Hz using a first order low-pass Butterworth filter and then at 0.5 Hz using a first order high-pass Butterworth filter, straddling the frequency of stimulation (1 Hz). This helped in eliminating some of the instrumentation noise, which is often present in physiologic measurements . Further, test subjects can also mask the effects of stimulation by being very fidgety  or stiff (resist imposed sway). To identify and screen out potential outliers, cross-correlation and frequency analyses were carried out on the body motion measures. For each stimulation period, cross-correlation functions were obtained between each of the six filtered body motion measures and the electrical stimulation profiles at the different amplitudes within each trial. Typically, any movement of the subject is expected to be captured by both the force plate and the IMUs. Therefore, if it was observed that the cross-correlation peak  for any body motion measure differed from other motion data, then the raw data for that measure were manually reviewed by the first and last authors. In the frequency domain, the dominant frequency was expected to be close to 1 Hz. If it was not, the raw data were manually reviewed to subjectively assess if the person was stiff or fidgety and if he/she was, then that data were discarded. During pilot testing for Study 2, subjects’ reports of perceptual responses of motion during applied stimulus were predominantly in the ML direction. Hence we limited our analyses of joystick and body motion to the ML direction only. The six measurements that characterized body motion in the ML direction were: (1) shear force (Fy); (2) roll moments (Mx); (3) linear acceleration for the head segment (Hay); (4) roll angular velocity for the head segment (Hrv); (5) linear acceleration for the trunk segment (Tay); and (6) roll angular velocity for the trunk segment (Trv). The first two body motion measurements were obtained using the force plate data and the remaining four body motion measurements were obtained from IMUs attached to the head and trunk segments.X is the predictor variable (stimulation amplitude), and b0 and b1 are the coefficients of logistic regression. The data in this case were two MATLAB vectors–stimulation amplitudes (X) and the corresponding normalized percentage time of motion detection (Y). The function call in MATLAB (version R2011a with Statistics Toolbox v. 7.5) was  = glmfit, where b is a two-element vector of regression coefficients , dev is the deviance  and is an indicator of goodness of fit. The threshold curve is generated using regression coefficients, by the function call glmval . Threshold was defined as the amplitude of stimulation at the point of subjective equality, at which there is a 50% chance of motion detection [dev > 4 was also used to screen for potential outliers in perceptual responses.For the joystick data, percentage time of “perceived motion reported by the subject” for each stimulation and baseline periods was calculated. Joystick movement was interpreted as “perceived motion reported by the subject”, when its output amplitude exceeded 0.05 V . For the body motion data, for each of the six measures, the percentage time was estimated during each stimulation/baseline period for which body motion was detected by the sensors. Sensor data were interpreted as body motion whenever the amplitude exceeded 2 standard deviations from the mean calculated over the last 5 s of the baseline level before each stimulation level. The percentage time at each stimulation and baseline level for perceptual and body motion detection was normalized with respect to the largest value across all levels of stimulation. While the primary criterion was the decision whether the subject detected the signal or not, there were only two possible outcomes, “yes” (1) or “no” (0), hence a binomial distribution function was fit to the data with a generalized linear model (GLM) and a logit link function, which is very common in psychophysical studies . This yietection . dev > 4One of the goals was to compare subjects’ physiologic response threshold, obtained by measuring body motion (with IMUs and force plate), with the perceptual threshold based on joystick responses. There was no significant difference in thresholds for the six repeat subjects across Studies 2 and 3 . Hence, we inferred that the small changes in procedures in threshold task during Studies 3 and 4 that were done based on feedback from Study 2 would not have affected their outcome. Therefore, threshold values obtained from perceptual and body motion measures were pooled together from subjects across studies 2, 3 and 4 for all further analyses. Thresholds obtained from the perceptual estimate (using the joystick) and the six motion measures (using the force plate and IMUs) were compared using repeated measures ANOVA with a significance level of 0.008 (after applying Bonferroni correction). We have also performed linear regressions between the perceptual measurement of threshold and physiological measurements of threshold to investigate how they are related with a significance level of 0.008 (after applying Bonferroni correction).We were specifically interested in investigating the relationship between the perceptual threshold amplitude and the peak stimulation level that resulted in optimal balance performance from subjects participating in Studies 2 and 4 for whom both threshold and balance assessment data were available. Data from both studies were pooled together and used for further analysis. A linear regression analysis was performed to examine the relationship between the peak stimulus amplitude at which optimal balance performance was determined and perceptual threshold amplitude using a significance level of 0.05.p<0.001; AP: Wilks’ Lambda = 0.390, p<0.001; APML: Wilks’ Lambda = 0.298, p<0.001), and the interaction Trial * Period was also significant . The significant interaction of Trial * Period is explained by the larger differences in RMS amplitudes for all the parameters between the baseline and stimulation periods for the optimal trials compared with that for the control trials . These two subjects also showed optimal improvement in APML at the two current levels as in the AP direction. In addition, one subject showed most improvement in APML direction at supra-threshold current amplitude (220%), but the second best improvement was at sub-threshold current amplitude (40%).dev values for the curve fit for both body motion and perceptual motion detected across all subjects for all measurements and amplitude levels. For six subjects who were part of both Studies 2 and 3, thresholding data were used from Study 3 thus reducing the number of subjects’ data available for analyses to 35. One subject was removed as instructions were not followed by this subject and did not move the joystick during the thresholding task. Another subject was removed from the analysis because equipment noise persisted in the physiologic measures throughout the thresholding task. Furthermore, two subjects showed multiple false positives during the baseline periods (0 stimulation) of their perceptual motion detection responses. One subject was determined to be fidgety based on physiological measures and the other subject did not follow instructions. False positives cause the threshold curve to become too deviant and result in unreliable estimates of thresholds and hence these two subjects were not included in the analysis. Additionally, 13 subjects were removed from the analysis, as their physiologic measures were not consistent as they were either fidgety or stiff. Eight of these 13 subjects showed motion during baseline periods well above the baseline equipment noise variations (fidgety). For five out of these 13 subjects, no motion was detected by force plate or IMUs even though they indicated perceiving motion using the joystick input (stiff subjects). The first and last author manually reviewed potential outliers suggested by the cross-correlation and frequency analyses of the body motion measurements and p = 0.024, Greenhouse–Geisser correction applied). Follow-up paired t-tests for each of the seven measures, revealed that there were no significant differences in thresholds obtained from perceptual measurements to the thresholds from any of the body motion measures after Bonferroni correction was applied .The repeated measures ANOVA showed that thresholds obtained using the seven measures were different from each other  = 3.2, We were specifically interested in using the perceptual threshold amplitude for predicting the stimulation level that would result in optimal balance performance. We used the threshold estimates obtained from the perceptual measure to compare with the optimal trial peak current levels using available data from 28 of 30 subjects who completed their balance assessments from Studies 2 and 4 (threshold data were not obtained from the 15 subjects who participated in Study 1 and one subject in Study 2 did not follow the instructions during thresholding procedure and one subject in Study 4 gave erroneous readings). However, optimal trial peak current levels in ML, AP and APML directions showed no significant correlations with the perceptual threshold amplitude (p>0.05).The mean (SD) subjective assessment scores across 15 subjects of Study 4, after completing the thresholding and balance tasks are shown in Our results show that low levels of binaural bipolar SVS applied in the ML direction, significantly improved balance performance consistent with the SR phenomenon previously observed, in normal healthy subjects, in ML, AP, and combined APML directions, respectively. The peak amplitude of optimal stimulus for improving balance performance was predominantly in the range of 100–500 μA across all the directions. These results confirm and extend the results obtained in our previous study of improving balance control in the ML direction .Our results show that binaural bipolar 1 Hz sinusoidal electrical stimulation can be used to provide an objective estimate of the threshold for induced movement. Thresholds obtained using perceptual responses were not significantly different than those estimated using direct physiologic measures of induced movement responses. The average current level of SVS stimulation that resulted in improved balance performance in terms of percentage of perceptual thresholds was estimated to be 46% in ML, 53% in AP, and 50% in APML directions.Overall our data show that the application of low levels of SVS resulted in a significant improvement in cross planar balance performance at a non-zero noise amplitude. Further, it is also important to realize that only a subset of these subjects showed improved balance control during the specific sub-threshold amplitude of stimulation with respect to their nominal performance in the control trial on an unstable foam surface with their eyes closed as indicated by the reduction of sway in ML (32 of 45 subjects), AP (30 of 45 subjects), and APML (22 of 45 subjects) directions See . This beThis series of studies used SVS signals in the range of 0–30 Hz to enable the enhancement of information transfer across the wide responsive frequency range exhibited by the vestibular spinal function , 29. FurThe average percentage improvements  were comOnly 16 of 45 subjects showed optimization at the same current level in all three directions  perceptual responses reported by the subject focusing on motion perceived due to the stimuli indicated using a joystick, and (2) body motion that is induced in response to the stimuli that was measured using force plate and IMUs fixed to the head and trunk segments. 6% of the subjects 2 out of 35) had to be dropped because they erroneously reported motion sensation using the joystick at multiple times during baseline periods, which overestimated their threshold. One subject was deemed fidgety from the analysis of body motion sway measurements while another moved the joystick in accordance with the instructions to report sensations other than motion and hence was deemed as not following operator instructions. Measurement of any physiologic response is also masked by various sources of noise , 35, 36. had to bin vivo, evaluated motor effects of SVS at or below threshold levels [Some researchers are of the opinion that binaural electrical stimulation applied across the mastoids primarily activates semicircular canals , whereasd levels . That std levels . SVS indd levels . SVS mayd levels . Interesd levels . It was d levels .For bidirectional stimuli, threshold can be considered as a measure of internal noise which includes all physiologic sources of variability  . Some inRoughly 30% of our subjects (13 of 45 in ML and 15 of 45 in AP) did not show improvements in their optimal trials to SVS relative to their control trials with the application of low-levels of SVS in the ML direction. We have perceptual thresholds for nearly half of the non-responsive subjects and they were in the same range as those in responsive subjects, which indicates that internal noise levels were comparable across both responders and non-responders. We hypothesize that these non-responders may have preferences for inherently reducing the weighting afforded to the vestibular signals while increasing weighting of sensory contributions from vision and proprioception in the sensorimotor integration process for postural control , 53. TheDizziness, tingling, metallic taste and light flashes were the most common subjective symptoms reported by the subjects during the thresholding and balance tasks. These symptoms occurred at supra-threshold levels of electrical stimulations and thus it is unlikely that it would have affected subjects’ perceptual thresholds or the improvements in balance assessments. Most importantly, the stimulus levels during the optimal trials were sub-threshold and were not perceived by subjects. Lack of any major subjective symptoms is consistent with another study , which uTo conclude, we have shown that an imperceptibly low level of white noise-based stochastic electrical stimulation of the vestibular system improved balance performance in both ML and AP directions, and is consistent with the SR phenomenon in normal healthy control subjects. In general, using stimulation amplitudes at 46% to 53% of perceptual motion threshold improved balance performance significantly compared to control (no stimulation) conditions. The peak amplitude of vestibular stimulation that improved balance function was predominantly in the range of ±100 to ±500 μA for all three directions. These results are consistent with the findings in our previous study . We haveHigher levels of electrical stimulation have been shown to degrade postural performance by creating temporary vestibular disturbance to simulate clinical behavior or as a training tool . Some reS1 TablePage shows the Perceptual threshold (μA), optimal Amplitude Range , the optimal amplitude range as a percentage of threshold (%), trial numbers at which the optimal stimulus trial was performed, and responsiveness of subject (Y) showing their optimal trial cost function value was less than that for their control trial across the three ML, AP and APML directions.(XLSX)Click here for additional data file.S2 TableThis page shows the RMS values for the six variables Fy, Mx, Hay, Hrv, Tay, and Trv for the baseline and stimulation periods for the control trials and optimal trials for all subjects in ML direction.(XLSX)Click here for additional data file.S3 TableThis page shows the RMS values for the variables Fx, My, Hax, Hpv, Tax, and Tpv for the baseline and stimulation periods for the control trials and optimal trials for all subjects in AP direction.(XLSX)Click here for additional data file.S4 TableThis page shows the RMS values for the variables Fy, Mx, Hay, Hrv, Tay, Trv, Fx, My, Hax, Hpv, Tax, and Tpv for the baseline and stimulation periods for the control trials and optimal trials for all subjects in APML direction.(XLSX)Click here for additional data file.S5 TableThis page shows the threshold amplitude determined using the physiologic (body motion) variables and the perceptual (joystick) variable for the subjects whose data were available for analysis. This page also shows the reason for subjects whose data became unavailable for analyses.(XLSX)Click here for additional data file.S6 TableThis page shows the histogram analyses for the optimal trial stimulation amplitude (μA) in the ML, AP and APML directions.(XLSX)Click here for additional data file.