👤 Anne V Buchanan

Obesity and Alzheimer’s disease (AD) are epidemiologically associated. The locus coeruleus (LC)—the brain’s primary and most significant source of norepinephrine—is one of the earliest sites of neurodegeneration in AD. The LC participates in feeding behavior through connections with the hypothalamus. The cellular composition of the LC has been characterized at single-cell resolution. However, the constituent cellular signatures of genes related to energy homeostasis—such as the melanocortin pathway genes—in the LC are unclear. We performed single-nucleus RNA sequencing and spatial transcriptomics (Visium) in the human LC, and HiPlex RNAscope in the LC of mice. The melanocortin pathway gene The online version contains supplementary material available at 10.1186/s40478-026-02287-x. Show less

Understanding the physiological mechanisms by which common variants predispose to type 2 diabetes requires large studies with detailed measures of insulin secretion and sensitivity. Here we performed the largest genome-wide association study of first-phase insulin secretion, as measured by intravenous glucose tolerance tests, using up to 5,567 individuals without diabetes from 10 studies. We aimed to refine the mechanisms of 178 known associations between common variants and glycemic traits and identify new loci. Thirty type 2 diabetes or fasting glucose-raising alleles were associated with a measure of first-phase insulin secretion at Show less

Growth hormone transgenic (GHTg) Atlantic salmon (Salmo salar) have enhanced growth when compared to their non-transgenic counterparts, and this trait can be beneficial for aquaculture production. Biological confinement of GHTg Atlantic salmon may be achieved through the induction of triploidy (3N). The growth rates of triploid GH transgenic (3NGHTg) Atlantic salmon juveniles were found to significantly vary between families in the AquaBounty breeding program. In order to characterize gene expression associated with enhanced growth in juvenile 3NGHTg Atlantic salmon, a functional genomics approach (32K cDNA microarray hybridizations followed by QPCR) was used to identify and validate liver transcripts that were differentially expressed between two fast-growing 3NGHTg Atlantic salmon families (AS11, AS26) and a slow-growing 3NGHTg Atlantic salmon family (AS25); juvenile growth rate was evaluated over a 45-day period. Of 687 microarray-identified differentially expressed features, 143 (116 more highly expressed in fast-growing and 27 more highly expressed in slow-growing juveniles) were identified in the AS11 vs. AS25 microarray study, while 544 (442 more highly expressed in fast-growing and 102 more highly expressed in slow-growing juveniles) were identified in the AS26 vs. AS25 microarray study. Forty microarray features (39 putatively associated with fast growth and 1 putatively associated with slow growth) were present in both microarray experiment gene lists. The expression levels of 15 microarray-identified transcripts were studied using QPCR with individual RNA samples to validate microarray results and to study biological variability of transcript expression. The QPCR results agreed with the microarray results for 12 of 13 putative fast-growth associated transcripts, but QPCR did not validate the microarray results for 2 putative slow-growth associated transcripts. Many of the 39 microarray-identified genes putatively associated at the transcript expression level with fast-growing 3NGHTg salmon juveniles (including APOA1, APOA4, B2M, FADSD6, FTM, and GAPDH) are involved in metabolism, iron homeostasis and oxygen transport, and immune- or stress-related responses. The results of this study increase our knowledge of family-specific impacts on growth rate and hepatic gene expression in juvenile 3NGHTg Atlantic salmon. In addition, this study provides a suite of putative rapid growth rate-associated transcripts that may contribute to the development of molecular markers [e.g. intronic, exonic or regulatory region single nucleotide polymorphisms (SNPs)] for the selection of GHTg Atlantic salmon broodstock that can be utilized to produce sterile triploids of desired growth performance for future commercial applications. Show less

Insulin clearance is a highly heritable trait, for which few quantitative trait loci have been discovered. We sought to determine whether validated type 2 diabetes and/or glycaemic trait loci are associated with insulin clearance. Hyperinsulinaemic-euglycaemic clamps were performed in two Hispanic-American family cohorts totalling 1329 participants in 329 families. The Metabochip was used to fine-map about 50 previously identified loci for type 2 diabetes, fasting glucose, fasting insulin, 2 h glucose or HbA1c. This resulted in 17,930 variants, which were tested for association with clamp-derived insulin clearance via meta-analysis of the two cohorts. In the meta-analysis, 38 variants located within seven loci demonstrated association with insulin clearance (p < 0.001). The top signals for each locus were rs10241087 (DGKB/TMEM195 [TMEM195 also known as AGMO]) (p = 4.4 × 10(-5)); chr1:217605433 (LYPLAL1) (p = 3.25 × 10(-4)); rs2380949 (GLIS3) (p = 3.4 × 10(-4)); rs55903902 (FADS1) (p = 5.6 × 10(-4)); rs849334 (JAZF1) (p = 6.4 × 10(-4)); rs35749 (IGF1) (p = 6.7 × 10(-4)); and rs9460557 (CDKAL1) (p = 6.8 × 10(-4)). While the majority of validated loci for type 2 diabetes and related traits do not appear to influence insulin clearance in Hispanics, several of these loci do show evidence of association with this trait. It is therefore possible that these loci could have pleiotropic effects on insulin secretion, insulin sensitivity and insulin clearance. Show less

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation. Show less

OBJECTIVE Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action. RESEARCH DESIGN AND METHODS We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084). RESULTS The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 x 10(-71)). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction. CONCLUSIONS Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes. Show less

Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958-30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, beta (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 x 10(-15)). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 x 10(-17); ratio of insulin to glucose area under the curve, P = 1.3 x 10(-16)) and diminished incretin effect (n = 804; P = 4.3 x 10(-4)). We also identified variants at ADCY5 (rs2877716, P = 4.2 x 10(-16)), VPS13C (rs17271305, P = 4.1 x 10(-8)), GCKR (rs1260326, P = 7.1 x 10(-11)) and TCF7L2 (rs7903146, P = 4.2 x 10(-10)) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09-1.15, P = 4.8 x 10(-18)). Show less

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes. Show less

While there is considerable appeal to the idea of selecting a few SNPs to represent all, or much, of the DNA sequence variability in a local chromosomal region, it is also important to quantify what detail is lost in adopting such an approach. To address this issue, we compared high- and low-resolution depictions of sequence diversity for the same genomic region, the APOA1/C3/A4/A5 gene cluster on chromosome 11. First, extensive re-sequencing identified all nucleotide and sequence haplotype variation of the linked apolipoprotein genes in 72 individuals from three populations: African-Americans from Jackson, Miss., Europeans from North Karelia, Finland, and European-Americans from Rochester, Minn. We identified 124 SNPs in 17.7 kb and significant differences in variation among genes. APOC3 gene diversity was particularly distinctive at high resolution, showing large allele frequency differences ( F(ST) values >0.250) between Jackson and the other two samples, and divergent population-specific haplotype lineages. Next, we selected haplotype-tagging SNPs (htSNPs) for each gene, at a density of approximately one SNP per kb, using an algorithm suggested by Stram et al. (2003). The 17 htSNPs identified were then used to reconstruct low-resolution haplotypes, from which inferences about the structure of variation were also drawn. This comparison showed that while the htSNPs successfully tagged common haplotype variation, they also left much underlying sequence diversity undetected and failed, in some cases, to co-classify groups of closely related haplotypes. The implications of these findings for other haplotype-based descriptions of human variation are discussed. Show less