| 1. |
- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 2. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 4. |
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| 5. |
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| 6. |
- Turcot, Valerie, et al.
(författare)
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Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity
- 2018
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 50:1, s. 26-41
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are similar to 10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed similar to 7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.
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| 7. |
- Alharthi, Mohannad, et al.
(författare)
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An Acumen/NS-3 integration for modeling networked Cyber-Physical Systems
- 2014
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Konferensbidrag (refereegranskat)abstract
- Capturing physical phenomena such as node mobility or wave propagation is challenging in current network simulators, and is mostly achieved through crude abstractions. Despite being operationally efficient, such abstractions adversely affect simulation credibility. To realize more accurate modeling, we are currently developing a simulation environment integrating a hybrid modeling language into a mainstream network simulator. This paper gives a preliminary overview of our efforts. For illustration, an example simulation scenario with some basic mobility is described. © 2014 IEEE.
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| 8. |
- Eltayeb-Elsheikh, Nezar, et al.
(författare)
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Association of HLA-DR-DQ alleles, haplotypes, and diplotypes with type 1 diabetes in Saudis
- 2020
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Ingår i: Diabetes/Metabolism Research Reviews. - : John Wiley & Sons. - 1520-7552 .- 1520-7560. ; 36:8
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Tidskriftsartikel (refereegranskat)abstract
- Aims: Type 1 diabetes (T1D) is an autoimmune disease that affects many children worldwide. Genetic factors and environmental triggers play crucial interacting roles in the aetiology. This study aimed to assess the contribution of HLA-DRB1-DQA1-DQB1 alleles, haplotypes, and genotypes to the risk of T1D among Saudis.Methods: A total of 222 children with T1D and 342 controls were genotyped for HLA-DRB1, -DQA1, and -DQB1 using reverse sequence-specific oligonucleotide (rSSO) Lab Type high definition (HD) kits. Alleles, haplotypes, and diplotypes were compared between cases and controls using the SAS statistical package.Results: DRB1*03:01-DQA1*05:01-DQB1*02:01 (32.4%; OR = 3.68; P-c < .0001), DRB1*04:05-DQA1*03:02-DQB1*03:02 (6.6%; OR = 6.76; P-c < .0001), DRB1*04:02-DQA1*03:01-DQB1*03:02 (6.0%; OR = 3.10; P-c = .0194), DRB1*04:01-DQA1*03:01-DQB1*03:02 (3.7%; OR = 4.22; P-c = .0335), and DRB1*04:05-DQA1*03:02-DQB1*02:02 (2.7%; OR = 6.31; P-c = .0326) haplotypes were significantly increased in cases compared to controls, whereas DRB1*07:01-DQA1*02:01-DQB1*02:02 (OR = 0.41; P-c = .0001), DRB1*13:01-DQA1*01:03-DQB1*06:03 (OR = 0.05; P-c < .0001), DRB1*15:01-DQA1*01:02-DQB1*06:02 (OR = 0.03; P-c < .0001), and DRB1*11:01-DQA1*05:05-DQB1*03:01 (OR = 0.07; P-c = .0291) were significantly decreased. Homozygous DRB1*03:01-DQA1*05:01-DQB1*02:01 genotypes and combinations of DRB1*03:01-DQA1*05:01-DQB1*02:01 with DRB1*04:05-DQA1*03:02-DQB1*03:02, DRB1*04:02-DQA1*03:01-DQB1*03:02, and DRB1*04:01-DQA1*03:01-DQB1*03:02 were significantly increased in cases than controls. Combinations of DRB1*03:01-DQA1*05:01-DQB1*02:01 with DRB1*07:01-DQA1*02:01-DQB1*02:02 and DRB1*13:02-DQA1*01:02-DQB1*06:04 showed low OR values but did not remain significantly decreased after Bonferroni correction.Conclusions: HLA-DRB1-DQA1-DQB1 alleles, haplotypes, and diplotypes in Saudis with T1D are not markedly different from those observed in Western and Middle-Eastern populations but are quite different than those of East Asians.
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