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This study compared the performance of four serology assays for Coronavirus Disease 2019 (COVID-19) and investigated whether COVID-19 disease history correlates with assay performance. Samples were tested at Northshore using the Elecsys Anti-SARS-CoV-2 (Roche Diagnostics), Access SARS-CoV-2 IgG anti-RBD (Beckman Coulter), and LIAISON SARS-CoV-2 S1/S2 IgG (DiaSorin) as well as at Genalyte using Maverick Multi-Antigen Serology Panel. The study included one hundred clinical samples collected before December 2019 and ninety-seven samples collected from convalescent plasma donors originally diagnosed with COVID-19 by PCR. COVID-19 disease history was self-reported by the plasma donors. There was no difference in specificity between the assays tested. Clinical sensitivity of these four tests was 98% (Genalyte), 96% (Roche), 92% (DiaSorin), and 87% (Beckman). The only statistically significant differences in clinical sensitivity was between the Beckman assay and both Genalyte and Roche assays. Convalescent plasma donor characteristics and disease symptoms did not correlate with false negative results from the Beckman and DiaSorin assays. All four tests showed high specificity (100%) and varying sensitivities (89–98%). No correlations between disease history and serology results were observed. The Genalyte Multiplex assay showed as good or better sensitivity to three other previously validated assays with FDA Emergency Use Authorizations.

The aim of this study was to measure the impact of genetic data in improving the prediction of type 2 diabetes (T2D) in the Malmö Diet and Cancer Study cohort. The current study was performed in 3,426 Swedish individuals and utilizes of a set of genetic and environmental risk data. We first validated our environmental risk model by comparing it to both the Finnish Diabetes Risk Score and the T2D risk model derived from the Framingham Offspring Study. The area under the curve (AUC) for our environmental model was 0.72 [95% CI, 0.69-0.74], which was significantly better than both the Finnish (0.64 [95% CI, 0.61-0.66], p-value < 1 x 10-4) and Framingham (0.69 [95% CI, 0.66-0.71], p-value = 0.0017) risk scores. We then verified that the genetic data has a statistically significant positive correlation with incidence of T2D in the studied population. We also verified that adding genetic data slightly but statistically increased the AUC of a model based only on environmental risk factors (RFs, AUC shift +1.0% from 0.72 to 0.73, p-value = 0.042). To study the dependence of the results on the environmental RFs, we divided the population into two equally sized risk groups based only on their environmental risk and repeated the same analysis within each subpopulation. While there is a statistically significant positive correlation between the genetic data and incidence of T2D in both environmental risk categories, the positive shift in the AUC remains statistically significant only in the category with the lower environmental risk. These results demonstrate that genetic data can be used to increase the accuracy of T2D prediction. Also, the data suggests that genetic data is more valuable in improving T2D prediction in populations with lower environmental risk. This suggests that the impact of genetic data depends on the environmental risk of the studied population and thus genetic association studies should be performed in light of the underlying environmental risk of the population.

Pyrosequencing, a non‐electrophoretic method for DNA sequencing, is emerging as a popular platform for analysis of single nucleotide polymorphisms (SNPs). This technology has the advantage of accuracy, ease‐of‐use, and high flexibility for different applications. Here, we review the methodology and the use of this technique for SNP genotyping, SNP discovery, haplotyping, and allelic frequency studies. In addition, we describe new schemes for template preparation and multiplexing as an effort for cost reduction in large‐scale studies. Hum Mutat 19:479–485, 2002. © 2002 Wiley‐Liss, Inc.

One intradermal injection of incomplete Freund's adjuvant-oil induces a T cell-mediated inflammatory joint disease in DA rats. Susceptibility genes for oil-induced arthritis (OIA) are located both within and outside the major histocompatibility complex (MHC, Oia1). We have searched for disease-linked non-MHC loci in an F2intercross between DA rats and MHC-identical but arthritis-resistant LEW.1AV1 rats. A genome-wide scan with microsatellite markers revealed two major chromosome regions that control disease incidence and severity: Oia2 on chromosome 4 (P = 4 × 10-13) and Oia3 on chromosome 10 (P = 1 × 10-6). All animals homozygous for DA alleles at both loci developed severe arthritis, whereas all those homozygous for LEW.1AV1 alleles were resistant. These results have general implications for situations where nonspecific activation of the immune system (e.g., incomplete Freund's adjuvant-oil) causes inflammation and disease, either alone or in conjunction with specific antigens. They may also provide clues to the etiology of inflammatory diseases in humans, including rheumatoid arthritis.

Identification of the genetic basis of common disease may require comprehensive sequence analysis of coding regions and regulatory elements in patients and controls to find genetic effects caused by rare or heterogeneous mutations. In this study, we demonstrate how mismatch repair detection on tag arrays can be applied in a case-control study. Mismatch repair detection allows > 1,000 amplicons to be screened for variations in a single laboratory reaction. Variation scanning in 939 amplicons, mostly in coding regions within a linkage peak, was done for 372 patients and 404 controls. In total, >180 Mb of DNA was scanned. Several variants more prevalent in patients than in controls were identified. This study demonstrates an approach to the discovery of susceptibility genes for common disease: large-scale direct sequence comparison between patients and controls. We believe this approach can be scaled up to allow sequence comparison in the whole-genome coding regions among large sets of cases and controls at a reasonable cost in the near future.

We report on the development of molecular inversion probe (MIP) genotyping, an efficient technology for large-scale single nucleotide polymorphism (SNP) analysis. This technique uses MIPs to produce inverted sequences, which undergo a unimolecular rearrangement and are then amplified by PCR using common primers and analyzed using universal sequence tag DNA microarrays, resulting in highly specific genotyping. With this technology, multiplex analysis of more than 1,000 probes in a single tube can be done using standard laboratory equipment. Genotypes are generated with a high call rate (95%) and high accuracy (>99%) as determined by independent sequencing.

Phenotyping of Individual Pancreatic Islets Locates Genetic Defects in Stimulus Secretion Coupling to Niddm1i Within the Major Diabetes Locus in GK Rats Jian-Man Lin 1 , Henrik Ortsäter 1 , Hossein Fakhrai-Rad 2 , Joakim Galli 2 , Holger Luthman 2 and Peter Bergsten 1 1 Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden 2 Karolinska Institute, Center for Molecular Medicine, Department of Molecular Medicine, Karolinska Hospital, Stockholm, Sweden Abstract The major diabetes quantitative trait locus ( Niddm1 ), which segregates in crosses between GK rats affected with spontaneous type 2–like diabetes and normoglycemic F344 rats, encodes at least two different diabetes susceptibility genes. Congenic strains for the two subloci ( Niddm1f and Niddm1i ) have been generated by transfer of GK alleles onto the genome of F344 rats. Whereas the Niddm1f phenotype implicated insulin resistance, the Niddm1i phenotype displayed diabetes related to insulin deficiency. Individual islets from 16-week-old congenic rats were characterized for insulin release and oxygen tension (pO 2 ). In the presence of 3 mmol/l glucose, insulin release from Niddm1f and Niddm1i islets was ∼5 pmol · g −1 · s −1 and pO 2 was 120 mmHg. Similar recordings were obtained from GK and F344 islets. When glucose was raised to 11 mmol/l, insulin release increased significantly in Niddm1f and F344 islets but was essentially unchanged in islets from GK and Niddm1i. The high glucose concentration lowered pO 2 to the same extent in islets from all strains. Addition of 1 mmol/l tolbutamide to the perifusion medium further increased pulsatile insulin release threefold in all islets. The pulse frequency was ∼0.4 min −1 . α-Ketoisocaproate (11 mmol/l) alone increased pulsatile insulin release eightfold in islets from Niddm1f, Niddm1i, and control F344 rats but had no effect on insulin release from GK islets. These secretory patterns in response to α-ketoisocaproate were paralleled by reduction of pO 2 in Niddm1f, Niddm1i, and control F344 islets and no change of pO 2 in GK islets. The results demonstrate that Niddm1i carries alleles of gene(s) that reduce glucose-induced insulin release and that are amenable to molecular identification by genetic fine mapping. Footnotes Address correspondence and reprint requests to Dr. Peter Bergsten, Department of Medical Cell Biology, Uppsala University, Box 571, SE-751 23 Uppsala, Sweden. E-mail: peter.bergsten{at}medcellbiol.uu.se . Received for publication 19 April 2001 and accepted in revised form 11 September 2001. H.L. is a consultant for Arexis and Gemini Genomics and receives grant support from and is on the board of directors of Arexis. H.L. holds stock in Arexis, Gemini Genomics, and AstraZeneca. BCKDH, branched-chain 2-ketoacid dehydrogenase; BSA, bovine serum albumin; KIC, α-ketoisocaproate; QTL, quantitative trait locus.

Niddm1i, a 16-Mb locus within the major diabetes QTL in the diabetic GK rat, causes impaired glucose tolerance in the congenic NIDDM1I strain. Niddm1i is homologous to both human and mouse regions linked with type 2 diabetes susceptibility. We employed multiple QTL analyses of congenic F2 progeny selected for one recombination event within Niddm1i combined with characterization of subcongenic strains. Fine mapping located one hyperglycemia locus within 700 kb (Niddm1i4, P = 5 × 10−6). Two adjacent loci were also detected, and the GK allele at Niddm1i2 (500 kb) showed a glucose-raising effect, whereas it had a glucose-lowering effect at Niddm1i3 (400 kb). Most proximally, Niddm1i1 (800 kb) affecting body weight was identified. Experimental data from subcongenics supported the four loci. Sorcs1, one of the two known diabetes susceptibility genes in the region, resides within Niddm1i3, while Tcf7l2 maps outside all four loci. Multiple-marker QTL analysis incorporating the effect of cosegregating QTL as cofactors together with genetically selected progeny can remarkably enhance resolution of QTL. The data demonstrate that the species-conserved Niddm1i is a composite of at least four QTL affecting type 2 diabetes susceptibility and that two adjacent QTL (Niddm1i2GK and Niddm1i3GK) act in opposite directions.