Collapsing singletons may boost signal for associating rare variants in sequencing study

Trait development and genetic parameters of resilience indicators based on variability in milk consumption recorded by automated milk feeders in North American Holstein calves

The implementation of automated milk feeders (AMF) on precision dairy farms has enabled efficient management of large numbers of group-housed replacement calves with reduced labor requirements and improved calf welfare. In this study, we investigated the feasibility of deriving calf resilience indicators based on variability in milk consumption using data from 10,076 North American Holstein calves collected between 2015 and 2021. We modeled and evaluated deviations in observed and predicted daily milk consumption trajectories as indicators of resilience to environmental perturbations. We also analyzed average milk intake and the number of treatments for bovine respiratory disease (BRD) and their genetic correlations with the derived resilience parameters. Milk consumption was recorded using the Förster-Technik AMF. Deviations in cumulative milk intake were modeled using various methods, including quantile regression and the Gompertz function. Ten resilience indicators were derived to quantify the degree and duration of perturbations, including amplitude, perturbation time, recovery time, and deviation velocities. After data editing, genomic data from 9,273 calves and pedigree information from 10,076 calves with 321,388 phenotypic records were used to estimate genetic parameters for 12 traits, including 10 calf resilience indicators as well as average milk intake and treatments for bovine respiratory disease. Substantial phenotypic variability was observed for all calf resilience indicators derived and genetic parameters related to these novel resilience indicators were estimated. The heritability estimates for the resilience traits are as follows: amplitude of the deviation (in L) 0.047 (0.032, 0.064) (HPD interval), perturbation time of deviation (in d) 0.011 (0.0056, 0.016), recovery time of the deviation (in d) 0.025 (0.016, 0.035), maximum velocity of perturbation (L/d) 0.039 (0.024, 0.053), average velocity of perturbation (L/d) 0.038 (0.022, 0.050), area between the curves (L x d) 0.039 (0.027, 0.054), recovery ratio 0.053 (0.036, 0.072), deviation variance 0.049 (0.32, 0.068), log-deviation variance 0.027 (0.016, 0.044), deviation auto-correlation 0.010 (0.0042, 0.017) and number of deviation occurrences 0.023 (0.0094, 0.036). Some of the highlighted genetic correlations observed with average milk consumption include amplitude: 0.569 (0.474, 0.666), perturbation time: -0.534 (-0.73, -0.342), and average velocity: 0.554 (0.432, 0.672). Similarly, the genetic correlations between the number of times treated for BRD with perturbation time was 0.494 (0.251, 0.723), -0.294 (-0.52, -0.095) with number of deviations, and 0.348 (0.131, 0.578) with deviation autocorrelation. This study highlights the genetic influence on various resilience traits in calves, including amplitude, perturbation time, recovery time, and velocity measures of the perturbation. Our findings suggest the need for prioritizing genetic selection based on traits like recovery time, which exhibits higher heritability and a moderate genetic correlation with the number of times a calf is treated for BRD. The combination of AMF data, mathematical modeling, and genomic evaluation provides a comprehensive framework for assessing and breeding more resilient dairy calves in the face of environmental and health challenges.

Advances in the Genetics of Hypertension: The Effect of Rare Variants

Worldwide, hypertension still represents a serious health burden with nine million people dying as a consequence of hypertension-related complications. Essential hypertension is a complex trait supported by multifactorial genetic inheritance together with environmental factors. The heritability of blood pressure (BP) is estimated to be 30-50%. A great effort was made to find genetic variants affecting BP levels through Genome-Wide Association Studies (GWAS). This approach relies on the "common disease-common variant" hypothesis and led to the identification of multiple genetic variants which explain, in aggregate, only 2-3% of the genetic variance of hypertension. Part of the missing genetic information could be caused by variants too rare to be detected by GWAS. The use of exome chips and Next-Generation Sequencing facilitated the discovery of causative variants. Here, we report the advances in the detection of novel rare variants, genes, and/or pathways through the most promising approaches, and the recent statistical tests that have emerged to handle rare variants. We also discuss the need to further support rare novel variants with replication studies within larger consortia and with deeper functional studies to better understand how new genes might improve patient care and the stratification of the response to antihypertensive treatments.

The protective role of DOT1L in UV-induced melanomagenesis

The DOT1L histone H3 lysine 79 (H3K79) methyltransferase plays an oncogenic role in MLL-rearranged leukemogenesis. Here, we demonstrate that, in contrast to MLL-rearranged leukemia, DOT1L plays a protective role in ultraviolet radiation (UVR)-induced melanoma development. Specifically, the DOT1L gene is located in a frequently deleted region and undergoes somatic mutation in human melanoma. Specific mutations functionally compromise DOT1L methyltransferase enzyme activity leading to reduced H3K79 methylation. Importantly, in the absence of DOT1L, UVR-induced DNA damage is inefficiently repaired, so that DOT1L loss promotes melanoma development in mice after exposure to UVR. Mechanistically, DOT1L facilitates DNA damage repair, with DOT1L-methylated H3K79 involvement in binding and recruiting XPC to the DNA damage site for nucleotide excision repair (NER). This study indicates that DOT1L plays a protective role in UVR-induced melanomagenesis.

Summary of results and discussions from the gene-based tests group at Genetic Analysis Workshop 18

I present a summary of the results and discussions held within the working group on gene-based tests at Genetic Analysis Workshop 18 (GAW18). The main focus of interest in our working group was modeling the action of combinations or "groups" of genetic variants, with a group of variants most often defined as a set of single-nucleotide polymorphisms lying within a known gene. Some contributions investigated the performance of previously proposed methods (particularly rare variant collapsing or burden-type methods) for addressing this question, applied to the GAW18 data, and other contributions developed novel approaches and addressed novel questions. Most approaches were successful in detecting significant effects at MAP4 in the simulated data. No other genetic effects were consistently detected across different analyses. Low power was noted, particularly for those methods that restricted analysis to purely the subset of unrelated individuals.