Contribution of common risk variants to multiple sclerosis in Orkney and Shetland

Orkney and Shetland, the population isolates that make up the Northern Isles of Scotland, are of particular interest to multiple sclerosis (MS) research. While MS prevalence is high in Scotland, Orkney has the highest global prevalence, higher than more northerly Shetland. Many hypotheses for the excess of MS cases in Orkney have been investigated, including vitamin D deficiency and homozygosity: neither was found to cause the high prevalence of MS. It is possible that this excess prevalence may be explained through unique genetics. We used polygenic risk scores (PRS) to look at the contribution of common risk variants to MS. Analyses were conducted using ORCADES (97/2118 cases/controls), VIKING (15/2000 cases/controls) and Generation Scotland (30/8708 cases/controls) data sets. However, no evidence of a difference in MS-associated common variant frequencies was found between the three control populations, aside from HLA-DRB1*15:01 tag SNP rs9271069. This SNP had a significantly higher risk allele frequency in Orkney (0.23, p value = 8 × 10–13) and Shetland (0.21, p value = 2.3 × 10–6) than mainland Scotland (0.17). This difference in frequency is estimated to account for 6 (95% CI 3, 8) out of 150 observed excess cases per 100,000 individuals in Shetland and 9 (95% CI 8, 11) of the observed 257 excess cases per 100,000 individuals in Orkney, compared with mainland Scotland. Common variants therefore appear to account for little of the excess burden of MS in the Northern Isles of Scotland.

Drusenoid Pigment Epithelial Detachment: Genetic and Clinical Characteristics

Few studies report drusenoid pigment epithelial detachment (DPED) in Asians. In this multicenter study, we report the clinical and genetic characteristics of 76 patients with DPED, and, for comparison, 861 patients with exudative age-related macular degeneration (AMD) were included. On the initial presentation, the mean best-corrected visual acuity was 0.087 ± 0.17 (logMAR unit), and mean DPED height and width were 210 ± 132 and 1633 ± 1114 µm, respectively. Fifty-one (67%) patients showed macular neovascularization in the contralateral eye. The risk allele frequency of both ARMS2 A69S and CFH I62V was significantly higher in DPED than in typical AMD and polypoidal choroidal vasculopathy (PCV) (ARMS2 A69S risk allele frequency: DPED 77% vs. typical AMD 66% vs. PCV 57%, CFH I62V risk allele frequency: DPED 87% vs. typical AMD 73% vs. PCV 73%), although the risk allele frequency of both genes was similar between the DPED group and retinal angiomatous proliferation (RAP) group (ARMS2 A69S: p = 0.32, CFH I62V, p = 0.11). The prevalence of reticular pseudodrusen (RPD) was highest in RAP (60%), followed by DPED (22%), typical AMD (20%), and PCV (2%). Although the prevalence of RPD differs between DPED and RAP, these entities share a similar genetic background in terms of ARMS2 and CFH genes.

Risk assessment of FLT3 and PAX5 variants in B-acute lymphoblastic leukemia: a case–control study in a Pakistani cohort

AIMS B-cell acute lymphoblastic leukemia (B-ALL) is amongst the most prevalent cancers of children in Pakistan. Genetic variations in FLT3 are associated with auto-phosphorylation of kinase domain that leads to increased proliferation of blast cells. Paired box family of transcription factor (PAX5) plays a critical role in commitment and differentiation of B-cells. Variations in PAX5 are associated with the risk of B-ALL. We aimed to analyze the association of FLT3 and PAX5 polymorphisms with B cell leukemia in Pakistani cohort. METHODS We collected 155 B-ALL subject and 155 control blood samples. For analysis, genotyping was done by tetra ARMS-PCR. SPSS was used to check the association of demographic factors of SNPs present in the population with the risk of B-ALL. RESULTS Risk allele frequency A at locus 13q12.2 (rs35958982, FLT3) was conspicuous and showed positive association (OR = 2.30, CI [1.20–4.50], P = 0.005) but genotype frequency (OR = 3.67, CI [0.75–18.10], P = 0.088) failed to show any association with the disease. At locus 9p13.2 (rs3780135, PAX5), the risk allele frequency was significantly higher in B-ALL subjects than ancestral allele frequency (OR = 2.17, CI [1.37–3.43], P = 0.000). Genotype frequency analysis of rs3780135 polymorphism exhibited the protective effect (OR = 0.55, CI [0.72–1.83], P = 0.029). At locus 13q12.2 (rs12430881, FLT3), the minor allele frequency G (OR = 1.15, CI [1.37–3.43], P = 0.043) and genotype frequency (OR = 2.52, P = 0.006) reached significance as showed p < 0.05. CONCLUSION In the present study, a strong risk of B-cell acute lymphoblastic leukemia was associated with rs35958982 and rs12430881 polymorphisms. However, rs3780135 polymorphism showed the protective effect. Additionally, other demographic factors like family history, smoking and consanguinity were also found to be important in risk assessment. We anticipate that the information from genetic variations in this study can aid in therapeutic approach in the future.

A Genome-Wide Association Study of Susceptibility to Acute Lymphoblastic Leukemia in Adolescents and Young Adults

Acute lymphoblastic leukemia (ALL) in adolescents and young adults (AYA) is characterized by distinct presenting features and inferior prognosis compared to pediatric ALL. Age, as a continuous variable, is negatively correlated with prognosis, in spite of risk-adapted combination chemotherapy. To better understand the etiology of ALL in this age group, we performed the first genome-wide association study (GWAS) to comprehensively examine germline single nucleotide polymorphisms (SNPs) for their association with susceptibility to B-ALL in AYAs. In the discovery GWAS, we compared genotype frequency at 635,297 SNPs between 308 AYA ALL cases (age 16-39 years, treated on the Children’s Oncology Group [COG], the Alliance-Cancer and Leukemia Group B, Eastern Cooperative Oncology Group, MD Anderson Cancer Center, and St. Jude Children’s Research Hospital trials) vs. 6,661 non-ALL controls. The association between genotypes at each SNP and ALL susceptibility was tested by using a logistic regression model after adjusting for genetic ancestries to control for population stratification. SNPs that reached P≤ 5×10-8 in the discovery GWAS were tested in an independent cohort of 82 AYA ALL cases from the COG protocols and 5,755 non-ALL controls. We identified a single genome-wide significant susceptibility locus on 10p14 signified by two SNPs within the GATA3 gene: rs3824662, P=2.8x10-10, odds ratio (OR)=1.77; rs3781093, P=3.2x10-9, OR=1.73, both of which were validated in the replication cohort (P=1.9x10-8 and P=4.3x10-5, respectively). We also examined the association signals in AYAs for susceptibility loci previously identified in pediatric ALL: ARID5B, IKZF1 and PIP4K2A variants were nominally significant in AYAs in the discovery GWAS and/or in the replication analysis, whereas CEBPE or CDKN2A/CDKN2B were not significant. These results imply both similarities and differences in genetic predisposition to ALL between children and AYAs. At the GATA3 locus, rs3824662 risk variant was over-represented in Philadelphia chromosome (Ph)-like ALL in AYAs (P=0.02), confirming our previous report of Ph-like ALL susceptibility variants in GATA3 (Nat Genet 45:1494). Importantly, even after excluding Ph-like cases, rs3824662 remained associated with the risk of developing ALL in AYAs, suggesting that the influence of the GATA3risk variant on ALL susceptibility in AYAs extends beyond the predisposition to Ph-like subtype. We next examined the relationship between GATA3 risk allele frequency and age at diagnosis in a cohort of unselected childhood and adolescent ALL cases enrolled in the COG P9900 protocols (N=1,827). Dividing patients into four consecutives age groups (<5, 5-10, 10-15 and >15 years), we observed a clear progressive increase in the risk allele frequency at rs3824662 (P=6.29×10-11) with increasing allelic odds ratio (i.e. relative risk of ALL conferred by each copy of the risk allele). This correlation between genotype and age was evident regardless of genetic ancestry, although the risk variant was more common among individuals with higher Native American ancestry. In contrast, the frequency of ALL susceptibility variant in ARID5B decreased progressively with increasing age at diagnosis (P=0.006), whereas PIP4K2A, CDKN2A/CDKN2B, IKZF1 and CEBPE variants were not related to age. Finally, we compared rs3824662 risk variant frequency by age in the COG P9900 protocols after stratifying the ALL cases into TCF3-PBX1, ETV6-RUNX1, hyperdiploid, MLL-rearranged and B-other. There was a trend that the risk allele was more frequent in cases older than 16 years compared to those below 16 in the five subtypes examined. In conclusion, we have identified inherited GATA3 genetic variants that strongly influence ALL susceptibility in adolescent and young adults, indicating potential age-related differences in ALL biology. Disclosures No relevant conflicts of interest to declare.

What can we learn from genetic studies of systemic lupus erythematosus? Implications of genetic heterogeneity among populations in SLE

Recent progress in genetics has expanded the number of the genes associated with SLE to more than 20 in the past 2 years. One might assign these candidate genetic factors into several pre-existing biological pathways: (i) innate immune response including TLR/interferon signaling pathways (IRF5, STAT4, TNFAIP3, and TREX1); (ii) adaptive immune response (HLA-DR, PTPN22, PDCD1, STAT4, LYN, BLK, and BANK1) including B, T cells, and antigen-presenting cells; and (iii) immune complex clearance mechanism (FCGRs, CRP, and ITGAM). In addition, there are also several genes and loci that could not be assigned into previous known pathways (KIAA1542, PXK, XKR6, ATG5, etc), providing possible novel mechanisms in SLE. It has also been evident that there are similarities and differences in SLE susceptibility loci across ethnic groups. Here we categorize the susceptible genes into four groups. The first group is the consistently associated genes with similar risk allele frequency between multiple ethnic populations such as STAT4, TNFAIP3, BANK1, and IRAK1/MECP2. The second group is the genes that are consistently associated but show marked difference in risk allele frequency (BLK, IRF5). The third group is the genes in which different risk variants exist within a gene or genetic loci (allelic heterogeneity) such as HLA-DR, FCGRs, and IRF5. The fourth group is the genes that show consistently discrepancy between populations such as PTPN22 and possibly ITGAM, PXK, and LYN (genetic heterogeneity). The possible explanations for differences of susceptible genetic factors between populations could be different genetic backgrounds, contribution of gene—gene or gene—environment interaction, and the relation between marker and causal variants. Therefore, efforts to identify ethnic-specific genetic factors or disease causing variants should be necessary for individualized therapy for SLE in future.