Alcohol consumption and the risk of all-cause and cause-specific mortality-a linear and nonlinear Mendelian randomization study.

BACKGROUND: Many observational studies support light-to-moderate alcohol intake as potentially protective against premature death. We used a genetic approach to evaluate the linear and nonlinear relationships between alcohol consumption and mortality from different underlying causes. METHODS: We used data from 278 093 white-British UK Biobank participants, aged 37-73 years at recruitment and with data on alcohol intake, genetic variants, and mortality. Habitual alcohol consumption was instrumented by 94 variants. Linear Mendelian randomization (MR) analyses were conducted using five complementary approaches, and nonlinear MR analyses by the doubly-ranked method. RESULTS: There were 20 834 deaths during the follow-up (median 12.6 years). In conventional analysis, the association between alcohol consumption and mortality outcomes was 'J-shaped'. In contrast, MR analyses supported a positive linear association with premature mortality, with no evidence for curvature (Pnonlinearity ≥ 0.21 for all outcomes). The odds ratio [OR] for each standard unit increase in alcohol intake was 1.27 (95% confidence interval [CI] 1.16-1.39) for all-cause mortality, 1.30 (95% CI 1.10-1.53) for cardiovascular disease, 1.20 (95% CI 1.08-1.33) for cancer, and 2.06 (95% CI 1.36-3.12) for digestive disease mortality. These results were consistent across pleiotropy-robust methods. There was no clear evidence for an association between alcohol consumption and mortality from respiratory diseases or COVID-19 (1.32, 95% CI 0.96-1.83 and 1.46, 95% CI 0.99-2.16, respectively; Pnonlinearity ≥ 0.21). CONCLUSION: Higher levels of genetically predicted alcohol consumption had a strong linear association with an increased risk of premature mortality with no evidence for any protective benefit at modest intake levels.

Genetic and brain similarity independently predict childhood anthropometrics and neighborhood socioeconomic conditions.

Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ an integrative approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, associations between cortical thickness similarity and traits were limited to anthropometrics such as height, weight, and birth weight, as well as a marker of neighborhood socioeconomic conditions. Common genetic variants explained significant proportions of variance across nearly all included outcomes, although estimates were somewhat lower than previous reports. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as neighborhood socioeconomic conditions and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample.

Lifestyle, genetic risk and incidence of cancer: a prospective cohort study of 13 cancer types.

BACKGROUND: Genetic and lifestyle factors are associated with cancer risk. We investigated the benefits of adhering to lifestyle advice by the World Cancer Research Fund (WCRF) with the risk of 13 types of cancer and whether these associations differ according to genetic risk using data from the UK Biobank. METHODS: In 2006-2010, participants aged 37-73 years had their lifestyle assessed and were followed up for incident cancers until 2015-2019. Analyses were restricted to those of White European ancestry with no prior history of malignant cancer (n = 195 822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined ('overall cancer'), and a lifestyle index was calculated from WCRF recommendations. Associations with cancer incidence were estimated using Cox regression, adjusting for relevant confounders. Additive and multiplicative interactions between lifestyle index and PRSs were assessed. RESULTS: There were 15 240 incident cancers during the 1 926 987 person-years of follow-up (median follow-up = 10.2 years). After adjusting for confounders, the lifestyle index was associated with a lower risk of overall cancer [hazard ratio per standard deviation increase (95% CI) = 0.89 (0.87, 0.90)] and of eight specific cancer types. There was no evidence of interactions on the multiplicative scale. There was evidence of additive interactions in risks for colorectal, breast, pancreatic, lung and bladder cancers, such that the recommended lifestyle was associated with greater change in absolute risk for persons at higher genetic risk (P < 0.0003 for all). CONCLUSIONS: The recommended lifestyle has beneficial associations with most cancers. In terms of absolute risk, the protective association is greater for higher genetic risk groups for some cancers. These findings have important implications for persons most genetically predisposed to those cancers and for targeted strategies for cancer prevention.

Metabolic profile predicts incident cancer: A large-scale population study in the UK Biobank.

BACKGROUND AND AIMS: Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction. METHODS: We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 individual biomarkers and cancer risk. RESULTS: In total, 21,973 individuals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03-5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as "high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II ("high triglycerides & liver enzymes") was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of individual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were individually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65). CONCLUSIONS: Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.

Comparison of Helical Blade Systems for Osteoporotic Intertrochanteric Fractures Using Biomechanical Analysis and Clinical Assessments.

Background and Objectives: This study aimed to compare the biomechanical properties and outcomes of osteoporotic intertrochanteric fractures treated with two different helical blade systems, the trochanteric fixation nail-advanced (TFNA) and proximal femoral nail antirotation II (PFNA), to evaluate the efficacy and safety of the newly introduced TFNA system. Materials and Methods: A biomechanical comparison of the two helical blades was performed using uniaxial compression tests on polyurethane foam blocks of different densities. The peak resistance (PR) and accumulated resistance (AR) were measured during the 20 mm advancement through the test block. For clinical comparison, 63 osteoporotic intertrochanteric fractures treated with TFNA were identified and compared with the same number of fractures treated with PFNA using propensity score matching. Ambulatory status, medial migration, lateral sliding, fixation failure, and patient-reported outcomes were compared between the two groups over a minimum of 1 year's follow up. Results: The uniaxial compression test showed that a slightly, but significantly lower resistance was required to advance the TFNA through the test block compared with the PFNA (20 PCF, p = 0.017 and p = 0.026; 30 PCF, p = 0.007 and p = 0.001 for PR and AR, respectively). Clinically, the two groups showed no significant differences in post-operative ambulatory status and patient-reported outcomes. However, in TFNA groups, significantly more medial migration (TFNA, 0.75 mm; PFNA, 0.40 mm; p = 0.0028) and also, lateral sliding was noted (TFNA, 3.99 mm; PFNA, 1.80 mm; p = 0.004). Surgical failure occurred in four and two fractures treated with the TFNA and PFNA, respectively. Conclusions: The results of our study suggest that the newly introduced TFNA provides clinical outcomes comparable with those of the PFNA. However, inferior resistance to medial migration in the TFNA raises concerns regarding potential fixation failures.

Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology.

Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.

Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank.

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294-91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits-12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs-one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic-covariate and residual-covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

Screw penetration of the iliopsoas muscle causing late-onset pain after total hip arthroplasty: A case report.

BACKGROUND: Postoperative pain following total hip arthroplasty (THA) may occur in a few patients but may pose a significant challenge to surgeons if the etiology is not identified. Herein, we report the case of a patient who developed late-onset pain following THA due to screw penetration of the iliopsoas tendon. CASE SUMMARY: We report the case of a 77-year-old man who developed inguinal pain 7 years after THA. While the symptoms resembled that of iliopsoas impingement by the acetabular cup, the pain resolved only when the supplementary acetabular screw protruding through the ilium was decompressed. Decompression was performed using the pararectus approach. The patient was able to ambulate pain-free immediately after surgery. CONCLUSION: A protruded screw through the ilium may penetrate the iliopsoas muscle, causing pain following THA. Pain may resolve with the decompression of the protruded screw.

GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data.

Genetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

Classification of epileptic seizure using feature selection based on fuzzy membership from EEG signal.

BACKGROUND: Feature selection is a technology that improves the performance result by eliminating overlapping or unrelated features. OBJECTIVE: To improve the performance result, this study proposes a new feature selection that uses the distance between the centers. METHODS: This study uses the distance between the centers of gravity (DBCG) of the bounded sum of the weighted fuzzy memberships (BSWFMs) supported by a neural network with weighted fuzzy membership (NEWFM). RESULTS: Using distance-based feature selection, 22 minimum features with a high performance result are selected, with the shortest DBCG of BSWFMs removed individually from the initial 24 features. The NEWFM used 22 minimum features as inputs to obtain a sensitivity, accuracy, and specificity of 99.3%, 99.5%, and 99.7%, respectively. CONCLUSIONS: In this study, only the mean DBCG is used to select the features; in the future, however, it will be necessary to incorporate statistical methods such as the standard deviation, maximum, and normal distribution.

Investigating Shared Genetic Basis Across Tourette Syndrome and Comorbid Neurodevelopmental Disorders Along the Impulsivity-Compulsivity Spectrum.

BACKGROUND: Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum. METHODS: Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers). RESULTS: As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism-based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD. CONCLUSIONS: Our work underlines the value of redefining the framework for research across traditional diagnostic categories.

Time taken to resume driving following hip arthroscopy.

BACKGROUND: Resuming driving is a common concern among patients undergoing hip arthroscopy. The present study aimed to assess whether patients who had undergone right hip arthroscopy presented with poorer driving performance than patients with normal hips and to analyze the time required to regain preoperative driving performance. METHODS: Forty-seven patients who had undergone right hip arthroscopy and consented to our test protocol were included in this study. Using an immersive driving simulator, the patients were tested for their brake reaction time (BRT), total brake time (TBT), and brake pedal depression (BPD) preoperatively and postoperatively. The first postoperative assessments were conducted when the patients could comfortably sit on the driving seat, and the follow-up assessments were conducted for 6 consecutive weeks at weekly intervals. The patients were divided into the following two groups based on the type of surgery that they underwent: the femoroacetabular impingement (FAI) surgery group and the simple hip arthroscopy (SA) group. Twenty healthy volunteers underwent driving assessments thrice at weekly intervals and constituted the control group. The braking parameters were compared between preoperative and postoperative measurements and among the FAI surgery, SA, and control groups. RESULTS: The preoperative braking parameters of the patients who underwent arthroscopy did not differ significantly from those of the controls (p = 0.373, 0.763, and 0.447 for the BRT, TBT, and BPD, respectively). All braking parameters returned to normal in 2 weeks in the FAI surgery group and in 1 week in the SA group. CONCLUSIONS: Our study suggests that the driving performance of patients who underwent right hip arthroscopy is comparable to that of individuals with normal hips and that the braking parameters may normalize to the preoperative state at 1 week after SA and 2 weeks after FAI surgery.

Accuracy of planar anteversion measurements using anteroposterior radiographs.

BACKGROUND: Several methods using simple anteroposterior (AP) radiographs have been suggested for the measurement of anteversion of the cup component after total hip arthroplasty. Herein, we compared six widely used anteversion measurement methods using two different types of AP radiograph, the conventional pelvis AP and hip-centered AP radiographs, to identify the measurement method and the type of radiograph that would provide the highest accuracy and reliability. METHODS: We developed two custom-made bi-planar anteversion measurement models for the validation test. The models were designed for pelvis AP and hip-centered AP radiographs, respectively. The radiographs were acquired using the inclination angles of both models, changing from 10° to 70° at 10° increments. For each inclination angle, anteversion was changed from 0° to 30° at 5° increments. The measurements were obtained independently by two orthopedic surgeons blinded from each other's measurements, using the methods of 1) Pradhan et al., 2) Lewinnek et al., 3) Widmer et al., 4) Liaw et al., 5) Hassan et al., and 6) Ackland et al. The measurements were repeated after 2 months. The accuracy, compared with that of the reference angle, and intra-observer and inter-observer reliabilities of each method were calculated. RESULTS: The highest accuracy was found when the method of Liaw et al. was used with hip-centered AP radiographs, which showed a difference of 1.37° ± 1.73 from the reference angle. Moreover, regardless of the type of radiograph, the methods by Pradhan et al., Lewinnek et al., and Liaw et al. showed excellent correlations with the reference anteversion. However, substantial differences were found when the methods by Widmer et al., Hassan et al., and Ackland et al. were used, regardless of the type of radiograph used. When anteversion was measured in an inclination between 30° and 50°, the method of Pradhan et al., when used with pelvis AP radiographs, showed the highest accuracy (1.23° ± 0.92°). We also found no significant difference in anteversions between the measurements made on pelvic and hip-centered AP radiographs. Both interobserver and intraobserver reliabilities were high for all the measurements tested. CONCLUSIONS: The methods by Pradhan et al., Liaw et al., and Lewinnek et al. may provide relatively accurate anteversion measurements with high reliability, regardless of the type of radiograph.

The Impact of Genomic and Traditional Selection on the Contribution of Mutational Variance to Long-Term Selection Response and Genetic Variance.

De novo mutations (DNM) create new genetic variance and are an important driver for long-term selection response. We hypothesized that genomic selection exploits mutational variance less than traditional selection methods such as mass selection or selection on pedigree-based breeding values, because DNM in selection candidates are not captured when the selection candidates' own phenotype is not used in genomic selection, DNM are not on SNP chips and DNM are not in linkage disequilibrium with the SNP on the chip. We tested this hypothesis with Monte Carlo simulation. From whole-genome sequence data, a subset of ∼300,000 variants was used that served as putative markers, quantitative trait loci or DNM. We simulated 20 generations with truncation selection based on breeding values from genomic best linear unbiased prediction without (GBLUP_no_OP) or with own phenotype (GBLUP_OP), pedigree-based BLUP without (BLUP_no_OP) or with own phenotype (BLUP_OP), or directly on phenotype. GBLUP_OP was the best strategy in exploiting mutational variance, while GBLUP_no_OP and BLUP_no_OP were the worst in exploiting mutational variance. The crucial element is that GBLUP_no_OP and BLUP_no_OP puts no selection pressure on DNM in selection candidates. Genetic variance decreased faster with GBLUP_no_OP and GBLUP_OP than with BLUP_no_OP, BLUP_OP or mass selection. The distribution of mutational effects, mutational variance, number of DNM per individual and nonadditivity had a large impact on mutational selection response and mutational genetic variance, but not on ranking of selection strategies. We advocate that more sustainable genomic selection strategies are required to optimize long-term selection response and to maintain genetic diversity.

Detection of genomic regions underlying resistance to gastrointestinal parasites in Australian sheep.

BACKGROUND: This study aimed at identifying genomic regions that underlie genetic variation of worm egg count, as an indicator trait for parasite resistance in a large population of Australian sheep, which was genotyped with the high-density 600 K Ovine single nucleotide polymorphism array. This study included 7539 sheep from different locations across Australia that underwent a field challenge with mixed gastrointestinal parasite species. Faecal samples were collected and worm egg counts for three strongyle species, i.e. Teladorsagia circumcincta, Haemonchus contortus and Trichostrongylus colubriformis were determined. Data were analysed using genome-wide association studies (GWAS) and regional heritability mapping (RHM). RESULTS: Both RHM and GWAS detected a region on Ovis aries (OAR) chromosome 2 that was highly significantly associated with parasite resistance at a genome-wise false discovery rate of 5%. RHM revealed additional significant regions on OAR6, 18, and 24. Pathway analysis revealed 13 genes within these significant regions (SH3RF1, HERC2, MAP3K, CYFIP1, PTPN1, BIN1, HERC3, HERC5, HERC6, IBSP, SPP1, ISG20, and DET1), which have various roles in innate and acquired immune response mechanisms, as well as cytokine signalling. Other genes involved in haemostasis regulation and mucosal defence were also detected, which are important for protection of sheep against invading parasites. CONCLUSIONS: This study identified significant genomic regions on OAR2, 6, 18, and 24 that are associated with parasite resistance in sheep. RHM was more powerful in detecting regions that affect parasite resistance than GWAS. Our results support the hypothesis that parasite resistance is a complex trait and is determined by a large number of genes with small effects, rather than by a few major genes with large effects.

Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep.

BACKGROUND: This study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel. RESULTS: The accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS [Formula: see text] threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS [Formula: see text] threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01). CONCLUSIONS: Our results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep.

Hip Arthroscopy of a Painful Hip with Borderline Dysplasia.

PURPOSE: Hip arthroscopy has been considered for treating hip dysplasia; however, its efficacy is still a matter of controversy. Here, we report outcomes of patients with borderline dysplasia treated with a contemporary hip arthroscopy technique. MATERIALS AND METHODS: Forty-seven hips with borderline hip dysplasia were treated using hip arthroscopy. Patients underwent procedures to correct torn labrums or ligamentum teres with additional procedure on the acetabular capsule. Patient outcomes were assessed using visual analogue scale (VAS), modified Harris Hip Score (mHHS), Nonarthritic Hip Score (NAHS) and patient satisfaction. Risk factors for poor prognosis were also investigated. RESULTS: The mean follow up period was 25.9 months. At the last follow up, mean VAS score decreased from 6.1±1.6 to 3.5±2.8 (P=0.016). The mHHS and NHAS at the last follow up improved from 61.0±7.6 to 78.6±19.5 (P=0.001) and 62.1±7.5 to 80.0±18.5 (P=0.002), respectively. While significant improvement was observed in all patient reported outcome measures tested, 19 (40.4%) hips indicated that "the operation was unsatisfactory." The only factor shown to influence outcomes was preoperative VAS (i.e., worse scores potentially an indicator of poor outcomes). CONCLUSION: The results of the current study indicate that arthroscopic management may be beneficial for a subset of patients with borderline dysplasia; however, the dissatisfaction rate associated with this treatment approach may be as high as 40%. The poor preoperative pain score appears to be the sole indicator for poor outcomes.

Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction.

Reference populations for genomic selection usually involve selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree-based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single-Step approach (SSGBLUP) using both. For a scenario with no-selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single-Step approach to obtain accurate and unbiased prediction of GEBV.

Effect of Spine-Pelvis Relationship in Total Hip Arthroplasty.

When spine-pelvic motion is normally coordinated, the pelvis may tilt posteriorly and acetabular anteversion may increase as the patient's position changes from standing to sitting; this scenario allows for improved clearance of the femoral head and neck during hip flexion. However, changes in the mobility of the spine and pelvis may result in impingement after total hip arthroplasty (THA), with the most obvious complication being dislocation. Understanding the spinal-pelvic relationship in the sagittal plane is essential for planning THA in patients with spinal fusion or a known spine disease. Careful attention should be payed to the cup position when performing THA on patients with an increased risk of dynamic impingement.