Objective To evaluate the accuracy of predicting the risk of developing pre‐eclampsia (PE) according to first‐trimester maternal demographic characteristics, medical history and biomarkers using artificial‐intelligence and machine‐learning methods. Methods The data were derived from prospective non‐interventional screening for PE at 11–13 weeks' gestation at two maternity hospitals in the UK. The data were divided into three subsets. The first set, including 30 437 subjects, was used to develop the training process, the second set of 10 000 subjects was utilized to optimize the machine‐learning hyperparameters and the third set of 20 352 subjects was coded and used for model validation. An artificial neural network was used to predict from the demographic characteristics and medical history the prior risk that was then combined with biomarker values to determine the risk of PE and preterm PE with delivery at < 37 weeks' gestation. An additional network was trained without including race as input. Biomarkers included uterine artery pulsatility index (UtA‐PI), mean arterial blood pressure (MAP), placental growth factor (PlGF) and pregnancy‐associated plasma protein‐A. All markers were entered using raw values without conversion into standardized multiples of the median. The prediction accuracy was estimated using the area under the receiver‐operating‐characteristics curve (AUC). We further computed the detection rate at 10%, 20% and 40% false‐positive rates (FPR). The impact of taking aspirin was also added. Shapley values were calculated to evaluate the contribution of each parameter to the prediction of risk. We used a non‐parametric test to compare the expected AUC with the one obtained when we randomly scrambled the labels and kept the predictions. For the general prediction, we performed 10 000 permutations of the labels. When the AUC was higher than the one obtained in all 10 000 permutations, we reported a P‐value of < 0.0001. For the race‐specific analysis, we performed 1000 permutations. When the AUC was higher than the AUC in permutations, we reported a P‐value of < 0.001. Results The detection rate for preterm PE vs no PE, at a 10% FPR, was 53.3% when screening by maternal factors only, and the corresponding AUC was 0.816; these increased to 75.3% and 0.909, respectively, with the addition of biomarkers into the model. Information on race was important for the prediction accuracy; when race was not used to train the model, at a 10% FPR, the detection rate of preterm PE vs no PE decreased to 34.5–45.5% (for different races) when screening by maternal factors only and to 55.0–62.1% when biomarkers were added. The major predictors of PE were high MAP and UtA‐PI, and low PlGF. The accuracy of prediction of all PE cases was lower than that for preterm PE. Aspirin use was recommended for cases who were at high risk of preterm PE. The AUC of all PE vs no PE was 0.770 when screening by maternal factors and 0.817 when the biomarkers were added; the respective detection rates, at a 10% FPR, were 41.3% and...

Recently, haplo‐identical transplantation with multiple HLA mismatches has become a viable option for stem cell transplants. Haplotype sharing detection requires the imputation of donor and recipient. We show that even in high‐resolution typing when all alleles are known, there is a 15% error rate in haplotype phasing, and even more in low‐resolution typings. Similarly, in related donors, the parents' haplotypes should be imputed to determine what haplotype each child inherited. We propose graph‐based family imputation (GRAMM) to phase alleles in family pedigree HLA typing data, and in mother‐cord blood unit pairs. We show that GRAMM has practically no phasing errors when pedigree data are available. We apply GRAMM to simulations with different typing resolutions as well as paired cord‐mother typings, and show very high phasing accuracy, and improved allele imputation accuracy. We use GRAMM to detect recombination events and show that the rate of falsely detected recombination events (false‐positive rate) in simulations is very low. We then apply recombination detection to typed families to estimate the recombination rate in Israeli and Australian population datasets. The estimated recombination rate has an upper bound of 10%–20% per family (1%–4% per individual).