DNA modifications are used to regulate gene expression and defend against invading genetic elements. In eukaryotes, modifications predominantly involve C5-methylcytosine (5mC) and occasionally N6-methyladenine (6mA), while bacteria frequently use N4-methylcytosine (4mC) in addition to 5mC and 6mA. Here we report that 4mC can serve as an epigenetic mark in eukaryotes. Bdelloid rotifers, tiny freshwater invertebrates with transposon-poor genomes rich in foreign genes, lack canonical eukaryotic C5-methyltransferases for 5mC addition, but encode an amino-methyltransferase, N4CMT, captured from bacteria >60 Mya. N4CMT deposits 4mC at active transposons and certain tandem repeats, and fusion to a chromodomain shapes its “histone-read-DNA-write” architecture recognizing silent chromatin marks. Furthermore, amplification of SETDB1 H3K9me3 histone methyltransferases yields variants preferentially binding 4mC-DNA, suggesting “DNA-read-histone-write” partnership to maintain chromatin-based silencing. Our results show how non-native DNA methyl groups can reshape epigenetic systems to silence transposons and demonstrate the potential of horizontal gene transfer to drive regulatory innovation in eukaryotes.

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) ¼ 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF ¼ 12% in African-Americans, MAF ¼ 2% in Hispanics) lowered HbA1c (À0.88% in hemizygous males, À0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF ¼ 0.5%; À0.98% in hemizygous males, À0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

OBJECTIVE Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters labeled: β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed. RESEARCH DESIGN AND METHODS Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD). RESULTS Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway. CONCLUSIONS Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Our lives begin with 1 cell, then 2, then 4, then the trillion cell adult, comprised of cell lineages, tissues, organs. How does this occur? Examination in numbers of cells, N, Cellular Phylodynamics, revealed two previously unappreciated processes: UNI-GROWTH, the slowing of growth that occurs as we become larger, caused by fewer cells dividing, captured by the Universal Mitotic Fraction and Universal Growth Equations, with accuracy confirmed for 13 species, including nematodes, mollusks, and vertebrates; and ALLO GROWTH, the creation of body parts from Founder Cells, captured by the Cellular Allometric Growth Equation, which describes mitotic expansion by Cell-Heritable change in the Cell Cycle Time. These equations can generate cell lineage approximations, bringing the power of coalescent theory to developmental biology.

93Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in 94 patients with diabetes. However, nonglycemic determinants, including genetic variation, may 95 influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-96 Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies 97 and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics and 407 East 98 Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-99 Americans, HK1 in Europeans and Hispanics, FN3K/FN3KRP in Europeans and G6PD in 100 African-Americans and Hispanics) and discovered a new African-ancestry specific low-101 frequency variant (rs1039215 in HBG2/HBE1, minor allele frequency (MAF)=0.03). The most 102 associated G6PD variant (p.Val98Met, rs1050828-T, MAF=12% in African-Americans, 103 MAF=2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous 104 females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics.105Additionally, we identified a rare distinct G6PD coding variant (rs76723693 -p.Leu353Pro, 106 MAF=0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected 107 significant association with HbA1c when aggregating rare missense variants in G6PD. We 108 observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 109 (G6PD) in the two largest TOPMed African-American cohorts and replicated the rs76723693 110 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 111 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals 112 carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, 113 assessment of these variants should be considered for incorporation into precision medicine 114 approaches for diabetes diagnosis.115 116 6 157We included in our analyses 10,338 TOPMed participants without diabetes from five

<a><i>Objective</i>: Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters: Beta cell, Proinsulin, Obesity, Lipodystrophy, and Liver/Lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed. </a> <p><i>Research Design and Methods</i>: Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in twelve studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n=454,193) and tested for cross-sectional association with T2D-related outcomes including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD). </p> <p><i>Results</i>: Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. <a>Increased Obesity and Lipodystrophy cluster pPS’s, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The Lipodystrophy and Liver/Lipid cluster pPS’s were each associated with CAD, with increasing and decreasing effects respectively. An increased Liver/Lipid cluster pPS was also significantly associated with reduced renal function. </a>The Liver/Lipid cluster includes known loci linked to liver lipid metabolism (e.g. <i>GCKR</i>, <i>PNPLA3,</i> and <i>TM6SF2)</i>, and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway. </p> <p><i>Conclusion</i>: Our findings support that genetically-driven pathways leading to T2D also predispose differentially to clinical outcomes. </p> <b><br> </b> <p><b> </b></p>

Type 2 diabetes is increasing in all ancestry groups1. Part of its genetic basis may reside among the rare (minor allele frequency <0.1%) variants that make up the vast majority of human genetic variation2. We analyzed high-coverage (mean depth 38.2x) whole genome sequencing from 9,639 individuals with T2D and 34,994 controls in the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program2 to show that rare, non-coding variants that are poorly captured by genotyping arrays or imputation panels contribute h2=53% (P=4.2×10−5) to the genetic component of risk in the largest (European) ancestry subset. We coupled sequence variation with islet epigenomic signatures3 to annotate and group rare variants with respect to gene expression4, chromatin state5 and three-dimensional chromatin architecture6, and show that pancreatic islet regulatory elements contribute to T2D genetic risk (h2=8%, P=2.4×10−3). We used islet annotation to create a non-coding framework for rare variant aggregation testing. This approach identified five loci containing rare alleles in islet regulatory elements that suggest novel biological mechanisms readily linked to hypotheses about variant-to-function. Large scale whole genome sequence analysis reveals the substantial contribution of rare, non-coding variation to the genetic architecture of T2D and highlights the value of tissue-specific regulatory annotation for variant-to-function discovery.

Hypothesis: The prevalence of type 2 diabetes is higher in Latino populations compared with other major ancestry groups. Not only has the Latino population been systematically underrepresented in large-scale genetic analyses, but previous studies relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation reference panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The NHLBI Trans-Omics for Precision Medicine (TOPMed) reference panel represents a unique opportunity to analyze rare genetic variations in the Latino population. Methods: We evaluate the TOPMed imputation performance using genotyping array and whole-exome sequence data in 6 Latino cohorts. To evaluate the ability of TOPMed imputation of increasing the identified loci, we performed a Latino type 2 diabetes GWAS meta-analysis in 8,150 type 2 diabetes cases and 10,735 controls and replicated the results in 6 additional cohorts including whole-genome sequence data from the All of Us cohort. Results: We show that, compared to imputation with 1000G, the TOPMed panel improves the identification of rare and low-frequency variants. We identified 26 distinct signals including a novel genome-wide significant variant (minor allele frequency 1.6%, OR=2.0, P=3.4−10-9) near ORC5. A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improves the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. Conclusions: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variation in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.