Archives

  • 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-07
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • 2024-04
  • 2024-05

    • It was noted elsewhere that

    2022-01-13

    It was noted elsewhere that several of the variants (i.e., Indel19 and SNP63) that make up the risk haplotypes had unusually large differences in allele frequencies between Africans and non-Africans, as compared with a set of likely neutral loci (Fullerton et al. 2002). This finding was interpreted as possibly resulting from population-specific selective pressures on these SNPs. However, the evidence of a partial selective sweep that emerges from the present survey suggests an alternative explanation. Theoretical work showed that, if a selective sweep occurs in geographically subdivided populations and migration rates are low relative to the selection coefficient, then linked neutral alleles may exhibit unusually large differences in allele frequencies across subpopulations (Slatkin and Wiehe 1998). Thus, if the selective event that drove up the frequency of the SNP44-haplotype class occurred in African and non-African populations after their separation, then the observed degree of differentiation of allele frequencies at linked SNPs would not be unexpected and would not reflect a selective advantage of the highly differentiated SNPs. A haplotype test performed on segments centered on either Indel19 or SNP63 in each population did not yield a significant result; this is consistent with the idea that these SNPs were not the target of directional selection. The possibility of an ancestral allele, such as would be the case for SNP44 or Thr504Ala, that increases risk of diabetes is particularly interesting in light of other findings about risk variants for common diseases. The common polymorphism Pro12Ala at the PPARG gene was shown to increase risk of type 2 diabetes, with an OR of 1.25 (Altshuler et al. 2000). On the basis of an alignment with the orthologous chimpanzee sequence, we determined that the risk allele (Pro12) is ancestral, whereas the protective and less common allele is derived. If the parallel with SNP44 (or Thr504Ala) holds, then one might expect a signature of positive selection around the derived Ala12 allele. However, given its relatively low frequency (∼15%), the power to detect the signature of selection, if present, may be low. The ɛ4 allele at the APOE gene is another pertinent example: this allele is defined by the presence of the ancestral allele at two common amino SID 26681509 polymorphisms and was shown to increase risk of coronary artery disease (Davignon et al. 1988; de Knijff et al. 1994; Stengard et al. 1995) and Alzheimer disease (Corder et al. 1993; Strittmatter et al. 1993). Interestingly, an analysis of polymorphism data in human populations showed that the haplotype class defined by the derived allele ɛ3—the most common in all populations—was associated with an excess of low-frequency variants, which was interpreted as evidence of the action of positive selection on this haplotype (Fullerton et al. 2000). The hypothesis that the ɛ3 allele increased in frequency as a result of positive selection had been independently proposed on the basis of its distribution across populations with different subsistence strategies, as well as on the basis of functional considerations (Corbo and Scacchi 1999). Within the same context, it was postulated that the ancestral ɛ4 allele was a “thrifty” allele in ancient human populations and that it had become deleterious under more recent environmental conditions. The second deviation from the standard neutral model is the one observed in intron 13 of CAPN10. This signal consists of a significantly large peak in polymorphism that overlaps, in the Hausa, with a significant peak in the decay of LD. One possible source of deviation from the null model is the action of natural selection. We performed coalescent simulations to explore a biallelic model of long-standing balancing selection, and, when we rejected it, we turned to a more complex model in which selection maintains more than two alleles. Under the multiallelic balancing selection model, the data are consistent with a limited range of combinations of parameter values. In this model, which was originally developed to explain variation at the MHC, a fixed number of alleles is maintained through a process of turnover, whereby selected alleles are lost by chance and are replaced by new selected alleles (Takahata 1990). Although this long-standing diversifying selection model is not easily reconciled with diabetes susceptibility, it is possible that other selective models (e.g., long-term fluctuations of environmental conditions and selective pressures [Gillespie 1991]) result in patterns of variation that are similar to those predicted by the model of Takahata (Takahata 1990; Hedrick 2002) and may be more relevant to the evolution of genes involved in energy metabolism.