Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies.

Background: Age is one of the major risk factors for a wide range of diseases. Nevertheless, some individuals can better cope with these changes and become centenarians. We hypothesize that their blood transcriptome may provide insights into the mechanisms contributing to healthy aging, as well as enable the discovery of candidate therapeutic targets.

Exonic Variation and Its Clinical Impact in 7221 Old Order Amish.

The Amish of Lancaster County, PA has been the focus of genetic studies for many years due to its demographic history and unique genetic makeup that includes a historical bottleneck event and subsequent genetic drift, resulting in a marked decrease in genetic diversity and increased frequency of some variants that have substantially shaped the health of the community. To characterize the coding variation in the Amish genome, we sequenced the exomes of 7221 adult community members, and in this report, we contrast genetic diversity between the Amish and Europeans from the UK Biobank. Exome sequences of 7221 Amish contained only 14% as many variants as the same number of UKB participants.

Improving dairy calf health through inclusion of diarrhea and respiratory health data into a US national genetic evaluation.

Calf diarrhea (DIAR) and respiratory illnesses (RESP) are leading causes of calf mortality. This study aimed to develop a comprehensive US national genomic evaluation for these important calf health traits using producer-recorded data from the National Cooperator Database. Analyses included 207,602 calf records for DIAR (age 3 to 60 d) and 681,741 records for RESP (age 3 to 365 d) from all breeds (97.

Multi-ancestry genome-wide association analyses incorporating SNP-by-psychosocial interactions identify novel loci for serum lipids.

Serum lipid levels, which are influenced by both genetic and environmental factors, are key determinants of cardiometabolic health and are influenced by both genetic and environmental factors. Improving our understanding of their underlying biological mechanisms can have important public health and therapeutic implications. Although psychosocial factors, including depression, anxiety, and perceived social support, are associated with serum lipid levels, it is unknown if they modify the effect of genetic loci that influence lipids.

Genetic study of von Willebrand factor antigen levels ≤ 50 IU/dL identifies variants associated with increased risk of von Willebrand disease and bleeding.

Background: von Willebrand disease (VWD) is a common inherited bleeding disorder caused by low levels or activity of circulating von Willebrand factor (VWF). Genetic susceptibility to VWF antigen (VWF:Ag) below normal (≤ 50 IU/dL) in the general population is underexplored.

Objectives: To identify genetic variants influencing VWF:Ag levels ≤ 50 IU/dL.

A large-scale genome-wide study of gene-sleep duration interactions for blood pressure in 811,405 individuals from diverse populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discovered 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes.

A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 Individuals from Diverse Populations.

Background: Gene-environment interactions may enhance our understanding of hypertension. Our previous study highlighted the importance of considering psychosocial factors in gene discovery for blood pressure (BP) but was limited in statistical power and population diversity. To address these challenges, we conducted a multi-population genome-wide association study (GWAS) of BP accounting for gene-depressive symptomatology (DEPR) interactions in a larger and more diverse sample.

General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals.

Integrating multi-omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi-omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to assess the aggregate effect of multiple genomic data types (e.

Metabolite signatures of chronological age, aging, survival, and longevity.

Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways.

Interactions between genes involved in physiological dysregulation and axon guidance: role in Alzheimer's disease.

Dysregulation of physiological processes may contribute to Alzheimer's disease (AD) development. We previously found that an increase in the level of physiological dysregulation (PD) in the aging body is associated with declining resilience and robustness to major diseases. Also, our genome-wide association study found that genes associated with the age-related increase in PD frequently represented pathways implicated in axon guidance and synaptic function, which in turn were linked to AD and related traits (e.

Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis.

Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis. These lesions are precursors for blood cancers, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point.

SLEMM: million-scale genomic predictions with window-based SNP weighting.

Motivation: The amount of genomic data is increasing exponentially. Using many genotyped and phenotyped individuals for genomic prediction is appealing yet challenging.

Results: We present SLEMM (short for Stochastic-Lanczos-Expedited Mixed Models), a new software tool, to address the computational challenge.