Lost in translation: On the impact of data coding on penalized regression with interactions

Penalized regression approaches are standard tools in quantitative genetics. It is known that the fit of an \emph{ordinary least squares} (OLS) regression is independent of certain transformations of the coding of the predictor variables, and that the standard mixed model \emph{ridge regression best linear unbiased prediction} (RRBLUP) is neither affected by translations of the variable coding, nor by global scaling. However, it has been reported that an extended version of this mixed model, which incorporates interactions by products of markers as additional predictor variables is affected by translations of the marker coding. In this work, we identify the cause of this loss of invariance in a general context of penalized regression on polynomials in the predictor variables. We show that in most cases, translating the coding of the predictor variables has an impact on effect estimates, with an exception when only the size of the coefficients of monomials of highest total degree are penalized. The invariance of RRBLUP can thus be considered as a special case of this setting, with a polynomial of total degree 1, where the size of the fixed effect (total degree 0) is not penalized but all coefficients of monomials of total degree 1 are. The extended RRBLUP, which includes interactions, is not invariant to translations because it does not only penalize interactions (total degree 2), but also additive effects (total degree 1). Our observations are not restricted to ridge regression, but generally valid for penalized regressions, for instance also for the $\ell_1$ penalty of LASSO

Editorial: Genomic selection: Lessons learned and perspectives

Genomic selection (GS) has been one of the most prominent Research Topics in breeding science in the last two decades after the milestone paper by Meuwissen et al. (2001). Its huge potential for increasing the efficiency of breeding programs attracted scientific curiosity and research funding. Many different statistical prediction methods have been tested, and different use cases have been explored. We organized this Research Topic to look both back and forward. The objectives were to review the developments of the last 20 years, to provide a snapshot of current hot topics, and potentially also to define areas on which more (or less) focus should be put in the future, thereby supporting readers with formulating and prioritizing their ideas for future research. Several questions were brought up when organizing this Research Topic including: How did GS change breeding schemes? Which impact did GS have on realized selection gain? What, considering the context of particularities of different crops, may be optimal breeding schemes to leverage the full potential of GS? What has been the impact of and what is the potential of hybrid prediction, statistical epistasis models, deep learning and other methods? What are the long-term effects of GS? Can predictive breeding approaches also be used to harness genetic resources from germplasm banks in a more efficient way

Mismatch repair genes founder mutations and cancer susceptibility in Lynch syndrome

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Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

OBJECTIVES: To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN: Prospective cohort study. SETTING: A total of 306 ICUs from 24 European countries. PARTICIPANTS: Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81-87 y]; 51.8% male). MEASUREMENTS: Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS: The 30-day-mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30-day mortality in 91.1% of all patients who die. CONCLUSION: A predictive model of cumulative events predicts 30-day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision-making capacity

Sepsis at ICU admission does not decrease 30-day survival in very old patients: a post-hoc analysis of the VIP1 multinational cohort study.

BACKGROUND: The number of intensive care patients aged ≥ 80 years (Very old Intensive Care Patients; VIPs) is growing. VIPs have high mortality and morbidity and the benefits of ICU admission are frequently questioned. Sepsis incidence has risen in recent years and identification of outcomes is of considerable public importance. We aimed to determine whether VIPs admitted for sepsis had different outcomes than those admitted for other acute reasons and identify potential prognostic factors for 30-day survival. RESULTS: This prospective study included VIPs with Sequential Organ Failure Assessment (SOFA) scores ≥ 2 acutely admitted to 307 ICUs in 21 European countries. Of 3869 acutely admitted VIPs, 493 (12.7%) [53.8% male, median age 83 (81-86) years] were admitted for sepsis. Sepsis was defined according to clinical criteria; suspected or demonstrated focus of infection and SOFA score ≥ 2 points. Compared to VIPs admitted for other acute reasons, VIPs admitted for sepsis were younger, had a higher SOFA score (9 vs. 7, p < 0.0001), required more vasoactive drugs [82.2% vs. 55.1%, p < 0.0001] and renal replacement therapies [17.4% vs. 9.9%; p < 0.0001], and had more life-sustaining treatment limitations [37.3% vs. 32.1%; p = 0.02]. Frailty was similar in both groups. Unadjusted 30-day survival was not significantly different between the two groups. After adjustment for age, gender, frailty, and SOFA score, sepsis had no impact on 30-day survival [HR 0.99 (95% CI 0.86-1.15), p = 0.917]. Inverse-probability weight (IPW)-adjusted survival curves for the first 30 days after ICU admission were similar for acute septic and non-septic patients [HR: 1.00 (95% CI 0.87-1.17), p = 0.95]. A matched-pair analysis in which patients with sepsis were matched with two control patients of the same gender with the same age, SOFA score, and level of frailty was also performed. A Cox proportional hazard regression model stratified on the matched pairs showed that 30-day survival was similar in both groups [57.2% (95% CI 52.7-60.7) vs. 57.1% (95% CI 53.7-60.1), p = 0.85]. CONCLUSIONS: After adjusting for organ dysfunction, sepsis at admission was not independently associated with decreased 30-day survival in this multinational study of 3869 VIPs. Age, frailty, and SOFA score were independently associated with survival

Relationship between the Clinical Frailty Scale and short-term mortality in patients ≥ 80 years old acutely admitted to the ICU: a prospective cohort study.

BACKGROUND: The Clinical Frailty Scale (CFS) is frequently used to measure frailty in critically ill adults. There is wide variation in the approach to analysing the relationship between the CFS score and mortality after admission to the ICU. This study aimed to evaluate the influence of modelling approach on the association between the CFS score and short-term mortality and quantify the prognostic value of frailty in this context. METHODS: We analysed data from two multicentre prospective cohort studies which enrolled intensive care unit patients ≥ 80 years old in 26 countries. The primary outcome was mortality within 30-days from admission to the ICU. Logistic regression models for both ICU and 30-day mortality included the CFS score as either a categorical, continuous or dichotomous variable and were adjusted for patient's age, sex, reason for admission to the ICU, and admission Sequential Organ Failure Assessment score. RESULTS: The median age in the sample of 7487 consecutive patients was 84 years (IQR 81-87). The highest fraction of new prognostic information from frailty in the context of 30-day mortality was observed when the CFS score was treated as either a categorical variable using all original levels of frailty or a nonlinear continuous variable and was equal to 9% using these modelling approaches (p < 0.001). The relationship between the CFS score and mortality was nonlinear (p < 0.01). CONCLUSION: Knowledge about a patient's frailty status adds a substantial amount of new prognostic information at the moment of admission to the ICU. Arbitrary simplification of the CFS score into fewer groups than originally intended leads to a loss of information and should be avoided. Trial registration NCT03134807 (VIP1), NCT03370692 (VIP2)

Effizienz verschiedener statistischer Methoden zur genomischen Vorhersage

Plant breeding programs are currently being revolutionized by technological developments in genomic research. With genome-wide marker data the genotypic value of an individual can be predicted based on its DNA profile. Statistical methods are required to derive marker effects from a training population comprising genotyped and phenotyped individuals. The efficiency of different methods was compared with both simulated and experimental data and important determinants of prediction performance, such as trait heritability and genetic architecture were quantified.Pflanzenzüchtungsprogramme werden derzeit durch den technologischen Fortschritt in der Genomanalyse revolutioniert. Genomweite Markerdaten ermöglichen es den genotypischen Wert eines Individuums basierend auf seiner DNA vorherzusagen. Mit Regressionsmodellen werden die Markereffekte anhand von genotypisierten und phänotypisierten Individuen geschätzt. Die Effizienz verschiedener statistischer Methoden wurde anhand von experimentellen und simulierten Datensätzen verglichen. In diesem Zusammenhang wurden wichtige Einflussgrößen auf die Vorhersagefähigkeit wie die Heritabilität und die genetische Merkmalsarchitektur untersucht