Age is the dominant risk factor for most chronic human diseases; yet the mechanisms by which aging confers this risk are largely unknown. 1 Recently, the age-related acquisition of somatic mutations in regenerating hematopoietic stem cell populations leading to clonal expansion was associated with both hematologic cancer 2 – 4 and coronary heart disease 5 , a phenomenon termed ‘Clonal Hematopoiesis of Indeterminate Potential’ (CHIP). 6 Simultaneous germline and somatic whole genome sequence analysis now provides the opportunity to identify root causes of CHIP. Here, we analyze high-coverage whole genome sequences from 97,691 participants of diverse ancestries in the NHLBI TOPMed program and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid, and inflammatory traits specific to different CHIP genes. Association of a genome-wide set of germline genetic variants identified three genetic loci associated with CHIP status, including one locus at TET2 that was African ancestry specific. In silico -informed in vitro evaluation of the TET2 germline locus identified a causal variant that disrupts a TET2 distal enhancer resulting in increased hematopoietic stem cell self-renewal. Overall, we observe that germline genetic variation shapes hematopoietic stem cell function leading to CHIP through mechanisms that are both specific to clonal hematopoiesis and shared mechanisms leading to somatic mutations across tissues.

Large-scale whole genome sequencing (WGS) studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests (RVATs) have limited scope to leverage variant functions. We propose STAAR (variant-Set Test for Association using Annotation infoRmation), a scalable and powerful RVAT method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce “annotation Principal Components”, multi-dimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness, and is scalable for analyzing very large cohort and biobank WGS studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery samples and 17,822 replication samples from the Trans-Omics for Precision Medicine program. We discovered and replicated novel RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.

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Piezoelectric polymers with good flexibility have attracted tremendous attention in wearable sensors and energy harvesters. As the piezoelectricity of polymers such as polyvinylidene fluoride (PVDF) and polyvinylidene fluoride− trifluoroethylene [P(VDF−TrFE)] is lower than that of their ceramic counterparts, various approaches have been employed to improve the piezoelectric output of PVDF-based sensors, such as electrospinning, heat annealing, nanoconfinement, polymer blending, and nanoparticle addition. Here, we report two strategies to improve the piezoelectric sensing performance of polymer-based piezoelectric nanofibers, which include the formation of barium titanate (BTO)/P(VDF−TrFE) composite nanofibers and fabrication of penetrated electrodes to enlarge the interfacial area. BTO/P(VDF−TrFE) nanofibers with a BTO weight fraction of 5 wt % exhibit the maximum β-phase crystallinity and piezoelectricity. The piezoelectric output of the BTO/ P(VDF−TrFE) nanofiber mat is significantly improved compared with that of pristine P(VDF−TrFE), which is confirmed by piezoresponse force microscopy (PFM) and compression loading tests. In order to form the penetrated electrodes, oxygen (O 2 ) plasma treatment is employed, followed by an electroless plating process. The BTO/P(VDF−TrFE) nanofibers with penetrated electrodes demonstrate increased dielectric constants and enhanced piezoelectric outputs. A BTO/P(VDF−TrFE) nanofiberbased sensor with penetrated electrodes is capable of discerning the energy of a free-falling ball as low as 0.6 μJ and sensing the movement of a walking ant.