Recent origin of HLA-DRB1 alleles and implications for human evolution

Bergström, Tomas F.; Josefsson, Agnetha; Erlich, Henry A.; Gyllensten, Ulf

doi:10.1038/ng0398-237

Cited by 133 publications

(104 citation statements)

References 44 publications

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“…Also when major allele homozygotes of these SNPs (e.g., TT of rs4410767) are associated with a higher variability of a gene (e.g., HLA-DRB5), the minor allele homozygotes of these SNPs (e.g., CC of rs4410767) are associated with a lower variability of the other gene (e.g., HLA-DQA2), or vice versa. These results broadly correspond with expectations from previous findings: (1) the human MHC exhibits extreme genetic diversity and is maintained by balancing selection (Parham and Ohta 1996;Bergstrom et al 1998), (2) haplotype-specific differences in gene expression are common across the MHC (Vandiedonck et al 2011), and (3) this region contains genes (e.g., HLA-DPA1) that show large copy number differences across populations (Sudmant et al 2010). The expression of a gene, e.g., HLA-DQA2, that falls onto two dichotomous parts could be Figure 4 Direction of evQTL and eQTL effects of genotypes in the joint evQTL-eQTL.…”

Section: Functions and Genomic Features Of Cis-acting Evqtl Genessupporting

confidence: 80%

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Genetic Variants Contribute to Gene Expression Variability in Humans

2013

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Expression quantitative trait loci (eQTL) studies have established convincing relationships between genetic variants and gene expression. Most of these studies focused on the mean of gene expression level, but not the variance of gene expression level (i.e., gene expression variability). In the present study, we systematically explore genome-wide association between genetic variants and gene expression variability in humans. We adapt the double generalized linear model (dglm) to simultaneously fit the means and the variances of gene expression among the three possible genotypes of a biallelic SNP. The genomic loci showing significant association between the variances of gene expression and the genotypes are termed expression variability QTL (evQTL). Using a data set of gene expression in lymphoblastoid cell lines (LCLs) derived from 210 HapMap individuals, we identify cis-acting evQTL involving 218 distinct genes, among which 8 genes, ADCY1, CTNNA2, DAAM2, FERMT2, IL6, PLOD2, SNX7, and TNFRSF11B, are cross-validated using an extra expression data set of the same LCLs. We also identify $300 trans-acting evQTL between .13,000 common SNPs and 500 randomly selected representative genes. We employ two distinct scenarios, emphasizing single-SNP and multiple-SNP effects on expression variability, to explain the formation of evQTL. We argue that detecting evQTL may represent a novel method for effectively screening for genetic interactions, especially when the multiple-SNP influence on expression variability is implied. The implication of our results for revealing genetic mechanisms of gene expression variability is discussed.Q UANTITATIVE genetic analysis has long focused on detecting genetic variants that affect organismal phenotypes. This is often done by contrasting mean differences in phenotypes among genotypes. Despite increasing evidence across several species for genetic control of phenotypic variance (Ansel et al. 2008;Hill and Mulder 2010; JimenezGomez et al. 2011), variance differences in phenotypes have been largely ignored. Recently, however, the fact that variance of phenotypes is genotype dependent has inspired the detection of genetic variants associated with phenotypic variability (Pare et al. 2010;Sudmant et al. 2010;Yang et al. 2012).When gene expression level is considered as a heritable, quantitative trait, statistical associations between mean gene expression and genotype can be established to identify those genomic loci associated with or linked to gene expression level (i.e., expression QTL, eQTL) (Montgomery and Dermitzakis 2011). The difference in mean gene expression and its genetic control have been extensively examined in humans (Stranger et al. 2005(Stranger et al. , 2007bPickrell et al. 2010). The difference in variance of gene expression (i.e., gene expression variability) is genetically controlled and likely to be selectable (Raser and O'Shea 2005;Blake et al. 2006;Maheshri and O'Shea 2007;Cheung and Spielman 2009;Zhang et al. 2009). A small number of initial efforts have been m...

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Section: Functions and Genomic Features Of Cis-acting Evqtl Genessupporting

confidence: 80%

“…These genes encode cellsurface proteins that belong to the MHC class II that are essential elements of the immune system. MHC regions are among the most highly polymorphic coding regions in the human genome (Bergstrom et al 1998). …”

Section: Discussionmentioning

confidence: 99%

Genetic Variants Contribute to Gene Expression Variability in Humans

2013

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“…The UPGMA phylogenetic tree of intron 2 sequences was constructed by using the pairwise genetic distances method calculated by using the Jukes-Cantor correction for multiple hits (25) and rooted by the midpoint method. This allows translation of genetic distances into divergence time (26). For the construction, the computer program PAUP* V.4.0B8 for Macintosh was used (44).…”

Section: Methodsmentioning

confidence: 99%

Evidence for an ancient selective sweep in the MHC class I gene repertoire of chimpanzees

Groot

Otting

Doxiadis

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

145

123

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“…This commonality of haplotypes across the major ancestral groups is consistent with the ancient origin of these haplotype families. [15][16][17] We also observed that certain microsatellites exhibited multiple repeat differences within a haplotype group. For example, multiple alleles of the D6S2446 microsatellite are found on DR*07 haploytpes, involving GT repeats between 13 and 16, giving rise to multiple three-marker haplotypes in this group (e.g.…”

Section: Discussionmentioning

confidence: 93%

Microsatellite typing for DRB1 alleles: application to the analysis of HLA associations with rheumatoid arthritis

Lee

et al. 2006

Genes Immun

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The current methods for molecular typing of HLA-DR alleles incur a substantial financial burden when performing large population studies. In the current study, we aimed to provide much less expensive typing approach with high predictability for DRB1 genotype. We have used a panel of three microsatellite markers in the class II region (D6S2666, D6S2665 and D6S2446) for genotyping and haplotype reconstruction in a total of 1687 Caucasian (1313 RA patients and 374 controls) and 1364 Korean individuals (744 RA patients and 620 controls), all of whom were previously genotyped for DRB1. We found that a total of 88.4 and 87.4% of all observed three-marker haplotypes could determine the DR type with a positive predictive value 40.8 with high sensitivity and specificity. There was a high degree of haplotype conservation when comparing Caucasian and Asian populations. Interestingly, we found that the majority of DRB1*09 and DRB1*10 alleles share a common three-marker haplotype in both Caucasian and Asian populations. This is unexpected, since these two alleles are found on very different haplotype families. In addition, these two alleles are both associated with rheumatoid arthritis, making the elucidation of these haplotype relationships potentially important for understanding disease susceptibility.

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Recent origin of HLA-DRB1 alleles and implications for human evolution

Cited by 133 publications

References 44 publications

Genetic Variants Contribute to Gene Expression Variability in Humans

Genetic Variants Contribute to Gene Expression Variability in Humans

Evidence for an ancient selective sweep in the MHC class I gene repertoire of chimpanzees

Microsatellite typing for DRB1 alleles: application to the analysis of HLA associations with rheumatoid arthritis

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