European Journal of Human Genetics 

About: European Journal of Human Genetics is an academic journal published by Nature Portfolio. The journal publishes majorly in the area(s): Population & Gene. It has an ISSN identifier of 1018-4813. Over the lifetime, 5977 publications have been published receiving 223358 citations. The journal is also known as: Eur. J. Hum. Genet..

Sex-specific, male-line transgenerational responses in humans.

Abstract: Transgenerational effects of maternal nutrition or other environmental ‘exposures’ are well recognised, but the possibility of exposure in the male influencing development and health in the next generation(s) is rarely considered. However, historical associations of longevity with paternal ancestors’ food supply in the slow growth period (SGP) in mid childhood have been reported. Using the Avon Longitudinal Study of Parents and Children (ALSPAC), we identified 166 fathers who reported starting smoking before age 11 years and compared the growth of their offspring with those with a later paternal onset of smoking, after correcting for confounders. We analysed food supply effects on offspring and grandchild mortality risk ratios (RR) using 303 probands and

Cardiovascular and diabetes mortality determined by nutrition during parents' and grandparents' slow growth period.

Abstract: Overfeeding and overeating in families are traditions that are often transferred from generation to generation. Irrespective of these family traditions, food availability might lead to overfeeding, in its turn leading to metabolic adaptations. Apart from selection, could these adaptations to the social environment have transgenerational effects? This study will attempt to answer the following question: Can overeating during a child's slow growth period (SGP), before their prepubertal peak in growth velocity influence descendants' risk of death from cardiovascular disease and diabetes? Data were collected by following three cohorts born in 1890, 1905 and 1920 in Overkalix parish in northern Sweden up until death or 1995. The parents' or grandparents' access to food during their SGP was determined by referring to historical data on harvests and food prices, records of local community meetings and general historical facts. If food was not readily available during the father's slow growth period, then cardiovascular disease mortality of the proband was low. Diabetes mortality increased if the paternal grandfather was exposed to a surfeit of food during his slow growth period. (Odds Ratio 4.1, 95% confidence interval 1.33-12.93, P=0.01). Selection bias seemed to be unlikely. A nutrition-linked mechanism through the male line seems to have influenced the risk for cardiovascular and diabetes mellitus mortality.

The family based association test method: strategies for studying general genotype--phenotype associations.

Abstract: With possibly incomplete nuclear families, the family based association test (FBAT) method allows one to evaluate any test statistic that can be expressed as the sum of products (covariance) between an arbitrary function of an offspring's genotype with an arbitrary function of the offspring's phenotype. We derive expressions needed to calculate the mean and variance of these test statistics under the null hypothesis of no linkage. To give some guidance on using the FBAT method, we present three simple data analysis strategies for different phenotypes: dichotomous (affection status), quantitative and censored (eg, the age of onset). We illustrate the approach by applying it to candidate gene data of the NIMH Alzheimer Disease Initiative. We show that the RC-TDT is equivalent to a special case of the FBAT method. This result allows us to generalise the RC-TDT to dominant, recessive and multi-allelic marker codings. Simulations compare the resulting FBAT tests to the RC-TDT and the S-TDT. The FBAT software is freely available.

A text-mining analysis of the human phenome.

Abstract: A number of large-scale efforts are underway to define the relationships between genes and proteins in various species. But, few attempts have been made to systematically classify all such relationships at the phenotype level. Also, it is unknown whether such a phenotype map would carry biologically meaningful information. We have used text mining to classify over 5000 human phenotypes contained in the Online Mendelian Inheritance in Man database. We find that similarity between phenotypes reflects biological modules of interacting functionally related genes. These similarities are positively correlated with a number of measures of gene function, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein-protein interaction. Phenotype grouping reflects the modular nature of human disease genetics. Thus, phenotype mapping may be used to predict candidate genes for diseases as well as functional relations between genes and proteins. Such predictions will further improve if a unified system of phenotype descriptors is developed. The phenotype similarity data are accessible through a web interface at http://www.cmbi.ru.nl/MimMiner/.

The COVID-19 Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic.

Abstract: The COVID-19 pandemic is a global crisis creating severe disruptions across the economy and health system. Insights into how to better understand and treat COVID-19 are desperately needed. Early studies have focused on the clinical characteristics [1–3], epidemiology [1, 4, 5], and genomic characterization [6–8] of SARS-CoV-2 infection. These studies have also highlighted the value and importance of transparent data sharing across countries, which have enabled the live tracking of the disease widespread worldwide [9, 10]. The role of host genetics in impacting susceptibility and severity of COVID-19 has been less studied. Previous work has supported the role of human leukocyte antigen (HLA) in susceptibility [11] and severity [12] for several viral infections. Moreover, a synonymous variant in the IFN-induced transmembrane protein-3 gene has been reported to cause severe clinical outcomes in patients infected with H7N9 and H1N1 influenza viruses [13, 14], although results did not reach established P value thresholds (P < 5 × 10). In addition, candidate variant studies have suggested host factors that are critical for severe disease in other coronavirus infections, such as infections due to the related SARS-CoV [15]. Given the importance and urgency of exploring the role of the host genome in conjunction with COVID-19 clinical and genomic variability, and the recognition that this can only be achieved with the combined effort of the scientific community, we launched the ‘COVID-19 Host Genetics Initiative’. This initiative brings together the human genetics community to generate, share, and analyze data to learn the genetic determinants of COVID-19 susceptibility, severity, and outcomes. Such discoveries could help to identify individuals at unusually high or low risk, generate hypotheses for drug repurposing, and contribute to global knowledge of the biology of SARS-CoV-2 infection and disease. The initiative has three main goals: