Genome-wide Discovery for Diabetes-Dependent Triglycerides-Associated Loci

Objective: Discover loci associated with triglyceride (TG) levels in the context of type 2 diabetes (T2D). Research Design and Methods: Genome-wide association study (GWAS) was performed in 424,120 genotyped participants of the UK Biobank (UKB) with T2D status and TG levels. Association analyses of TG levels were performed stratified by T2D status for genetic variants with minor allele count (MAC) at least 20 in each stratum. Effect differences of genetic variants by T2D status were determined by Cochrans Q-test implemented in METAL (p-value<5x10-8). Validation of significant variants was pursued in 25,137 participants of the Mass General Brigham Biobank (MGBB). Results: Among 21,176 T2D and 402,944 non-T2D samples, stratified GWAS identified 19 and 315 genomic risk loci significantly associated with TG levels, respectively. Only chr6p21.32 exhibited genome-wide significant heterogeneity (I2=98.4%; pheterogeneity=2.1x10-15), with log(TG) effect estimates of -0.066 (95%CI: -0.082, -0.050) and 0.002 (95%CI: -0.002, 0.006) for T2D and non-T2D, respectively. The lead variant rs9274619:A (allele frequency 0.095) is located 2Kb upstream of the HLA-DQB1 gene. We replicated this finding among 25,137 participants (6,951 T2D cases) of MGBB (pheterogeneity=9.5x10-3). Phenome-wide interaction association analyses showed that the lead variant was strongly associated with a concomitant diagnosis of type 1 diabetes (T1D) as well as diabetes-associated complications. Conclusion: An intergenic variant near HLA-DQB1 significantly associates with decreased triglycerides only among those with T2D and highlights an immune overlap with T1D.

Gasdermin A Single Nucleotide Polymorphisms and Alopecia Susceptibility in Punjabi Population from North-West India

Human Gasdermin A (GSDMA), a member of gasdermin gene family, is mainly expressed in skin and stomach. Mutations in its mouse counterpart Gsdma3, were found to cause skin diseases characterized by hair loss/ alopecia. As human and mice genes share 75% sequence similarity, present study was designed to check whether natural variability in human GSDMA gene was associated with alopecia. Blood samples of 100 alopecia patients and 100 age matched controls were collected and genomic DNA Isolated. All the samples were genotyped for two GSDMA SNPs, rs7212938 (V128L) and rs200722398 (V253I) for distribution of alleles along with haplotype analysis. Out of the T and G allele of rs7212938, the G allele count was found to be significantly increased (0.29 to 0.39) among alopecia patients and out of G/A alleles at rs200722398, allele A count was found to be significantly increased (0.06 to 0.13) among alopecia patients. Further haplotype analysis revealed that haplotype combination TGTAGG of rs7212938 and rs200722398 enhanced the susceptibility to alopecia significantly among Punjabi men. Studies on large population sample, other interacting genes and mechanism underlying the observed enhanced susceptibility are required to delineate the role of the observed association between GSDMA alleles and relative risk of alopecia.

GATA2 deficiency phenotype associated with tandem duplication GATA2 and over-expression of GATA2-AS1

A 3-year old girl of non-consanguineous healthy parents presented with cervical and mediastinal lymphadenopathy due to Mycobacterium fortuitum infection. Routine blood analysis showed normal hemoglobin, neutrophils and platelets but profound mononuclear cell deficiency (monocytes &lt;0.1x109/L; B cells 78/µL; NK cells 48/µL). A 548,902bp region containing GATA2 was sequenced by targeted capture and deep sequencing. This revealed a de novo 187Kb duplication of the entire GATA2 locus, containing a maternally inherited copy number variation deletion of 25Kb (GRCh37: esv2725896 and nsv513733). Many GATA2-associated phenotypes have been attributed to amino acid substitution, frameshift/deletion, loss of intronic enhancer function or aberrant splicing. Gene deletion has been described but other structural variation has not been reported in the germline configuration. In this case, duplication of the GATA2 locus was paradoxically associated with skewed, diminished expression of GATA2 mRNA and loss of GATA2 protein. Chimeric RNA fusion transcripts were not detected. A possible mechanism involves increased transcription of the anti-sense long-non-coding (lnc)RNA GATA2-AS1 (RP11-472.220) which was increased several-fold. This case further highlights that evaluation of the allele count is essential in any case of suspected GATA2-related syndrome.

Antigenic Diversity in Theileria parva Populations From Sympatric Cattle and African Buffalo Analyzed Using Long Read Sequencing

East Coast fever (ECF) in cattle is caused by the Apicomplexan protozoan parasite Theileria parva, transmitted by the three-host tick Rhipicephalus appendiculatus. The African buffalo (Syncerus caffer) is the natural host for T. parva but does not suffer disease, whereas ECF is often fatal in cattle. The genetic relationship between T. parva populations circulating in cattle and buffalo is poorly understood, and has not been studied in sympatric buffalo and cattle. This study aimed to determine the genetic diversity of T. parva populations in cattle and buffalo, in an area where livestock co-exist with buffalo adjacent to the Serengeti National Park, Tanzania. Three T. parva antigens (Tp1, Tp4, and Tp16), known to be recognized by CD8+ and CD4+ T cells in immunized cattle, were used to characterize genetic diversity of T. parva in cattle (n = 126) and buffalo samples (n = 22). Long read (PacBio) sequencing was used to generate full or near-full length allelic sequences. Patterns of diversity were similar across all three antigens, with allelic diversity being significantly greater in buffalo-derived parasites compared to cattle-derived (e.g., for Tp1 median cattle allele count was 9, and 81.5 for buffalo), with very few alleles shared between species (8 of 651 alleles were shared for Tp1). Most alleles were unique to buffalo with a smaller proportion unique to cattle (412 buffalo unique vs. 231 cattle-unique for Tp1). There were indications of population substructuring, with one allelic cluster of Tp1 representing alleles found in both cattle and buffalo (including the TpM reference genome allele), and another containing predominantly only alleles deriving from buffalo. These data illustrate the complex interplay between T. parva populations in buffalo and cattle, revealing the significant genetic diversity in the buffalo T. parva population, the limited sharing of parasite genotypes between the host species, and highlight that a subpopulation of T. parva is maintained by transmission within cattle. The data indicate that fuller understanding of buffalo T. parva population dynamics is needed, as only a comprehensive appreciation of the population genetics of T. parva populations will enable assessment of buffalo-derived infection risk in cattle, and how this may impact upon control measures such as vaccination.

f-statistics estimation and admixture graph construction with Pool-Seq or allele count data using the R package poolfstat

By capturing various patterns of the structuring of genetic variation across populations, f-statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariance of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool-Seq) data. We here propose a reinterpretation of the F (and D) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool-Seq and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three-population test of admixture; (ii) four-population test of treeness; (iii) F4-ratio estimation of admixture rates; and (iv) fitting, visualization and (semi-automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool-Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost-effective Pool-Seq design involving low sequencing coverages. We further analyzed a real Pool-Seq data made of 14 populations of the invasive species Drosophila suzukii which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user-friendly and efficient all-in-one tool to unravel complex population genetic histories from large-size Pool-Seq or allele count SNP data.

Why are rare variants hard to impute? Coalescent models reveal theoretical limits in existing algorithms

Genotype imputation is an indispensable step in human genetic studies. Large reference panels with deeply sequenced genomes now allow interrogating variants with minor allele frequency &lt; 1% without sequencing. While it is critical to consider limits of this approach, imputation methods for rare variants have only done so empirically; the theoretical basis of their imputation accuracy has not been explored. To provide theoretical consideration of imputation accuracy under the current imputation framework, we develop a coalescent model of imputing rare variants, leveraging the joint genealogy of the sample to be imputed and reference individuals. We show that broadly used imputation algorithms includes model misspecifications about this joint genealogy that limit the ability to correctly impute rare variants. We develop closed-form solutions for the probability distribution of this joint genealogy and quantify the inevitable error rate resulting from the model misspecification across a range of allele frequencies and reference sample sizes. We show that the probability of a falsely imputed minor allele decreases with reference sample size, but the proportion of falsely imputed minor alleles mostly depends on the allele count in the reference sample. We summarize the impact of this error on genotype imputation on association tests by calculating the r2 between imputed and true genotype and show that even when modeling other sources of error, the impact of the model misspecification have a significant impact on the r2 of rare variants. To evaluate these predictions in practice, we compare the imputation of the same dataset across imputation panels of different sizes. While this empirical imputation accuracy is substantially lower than our theoretical prediction, modeling misspecification seems to further decrease imputation accuracy for variants with low allele counts in the reference. These results provide a framework for developing new imputation algorithms and for interpreting rare variant association analyses.

DNA Variants Involved in Regulation of Fetal Hemoglobin in Mexican Patients with Sickle Cell Disease. a Preliminary Analysis

HbS (HBB:c.20A&gt;T, beta 6 Glu&gt;Val) is a frequent Hb variant in Mexico, in particular in some regions close to the Pacific and Atlantic Oceans, with carrier frequencies from 0.5 to 12.8%, therefore Sickle Cell Disease patients are often seen in those regions. It is well known that increased levels of Fetal Hemoglobin (HbF) ameliorates the clinical complications of sickle cell disease. Several genetic studies have identifiedBCL11A,HBS1L-MYB(intergenic region),HBG2andHBBP1genes, among others, to be involved in HbF regulation; DNA variants in theselociare associated to elevated HbF. The aim of present study was to analyze 15 variants in HbF regulatorylociin Mexican patients with Sickle Cell Disease. About 10 mL of peripheral blood was collected in EDTA for hematological and molecular testing from 24 sickle cell anemia patients (S/S) and 15 sickle cell trait carriers (S/A) from Southern Mexico, 13 were from the state of Guerrero and 26 from Chiapas. All subjects voluntarily agreed to participate in this study and gave signed informed consent; underaged patients' consent was obtained from their parents; all procedures were performed according to the ethical principles of the Declaration of Helsinki. Hemoglobin S genotype was determined by Sanger sequencing; DNA variants genotyping was performed by qPCR using commercial Taqman probes for the following variants: inBCL11Ars11886868, rs4671393, rs7557939, rs1427407, rs766432, rs6706648, rs7599488; inHBS1L-MYBrs7776054, rs28384513, rs9399137, rs4895441, rs9402686, rs1320963, inHBG2rs7482144 and inHBBP1rs10128556. Hematological and clinical data were analyzed by IBM SPSS v24®. A total of 39 sickle cell patients were studied, twenty-one patients were male and 18 were female (53.8% and 46.2% respectively). Thirty-two were pediatric age patients and 7 adults. Anemia was observed in all S/S patients, eight had severe anemia (&lt;8 g/dL); on the other hand, only 5/15 S/A patients had anemia (10 - 12 g/dL); HbF over 5% was observed in 23/24 S/S patients and in 13/15 S/A had HbF from 2% - 5%. Nineteen S/S patients were treated with hydroxyurea and presented less severe phenotype and more elevated HbF, however, the statistical analysis showed no significant differences (12.9%vs8.9%p=0.248) (Table 1). Genotype and allele frequencies are displayed in Table 2. All minor alleles were observed in frequencies over 0.05, the mo2st commonly observed minor allele wasBCL11Ars1427407 (0.69), and the less frequently observed wasHBBP1rs101028556 (0.08). The allele frequencies of four HbF regulating variants were significant different from those reported for Mexican ancestry population (MXL) in 1000 Genome database (rs11886868 C&gt;T, rs4671393 A&gt;G, rs7599488 C&gt;T and rs10128556 C&gt;T), however, the analyzed sample in this study is undersized and is not representative of the global Mexican population. No differences were observed when comparing allele frequencies from GuerrerovsChiapas patients, nor S/SvsS/A. The correlation analysis of minor allele count (MAC) and HbF demonstrated no association when comparing it to HbF levels. Likewise, the analysis was performed onBCL11AandHBS1L-MYBvariants and showed no significant correlations. There was also no correlation between MAC and the other hematological parameters (RBC, Hb, PCV, MCV and MCH). We report the first study of HbF regulating DNA variants in Mexican sickle cell disease patients. The preliminary analysis of HbFvsminor allele count showed no relation of total MAC and increased HbF, neither with the variants ofBCL11AorHBS1L-MYB. Disclosures No relevant conflicts of interest to declare.

IndiGenomes: a comprehensive resource of genetic variants from over 1000 Indian genomes

With the advent of next-generation sequencing, large-scale initiatives for mining whole genomes and exomes have been employed to better understand global or population-level genetic architecture. India encompasses more than 17% of the world population with extensive genetic diversity, but is under-represented in the global sequencing datasets. This gave us the impetus to perform and analyze the whole genome sequencing of 1029 healthy Indian individuals under the pilot phase of the ‘IndiGen’ program. We generated a compendium of 55,898,122 single allelic genetic variants from geographically distinct Indian genomes and calculated the allele frequency, allele count, allele number, along with the number of heterozygous or homozygous individuals. In the present study, these variants were systematically annotated using publicly available population databases and can be accessed through a browsable online database named as ‘IndiGenomes’ http://clingen.igib.res.in/indigen/. The IndiGenomes database will help clinicians and researchers in exploring the genetic component underlying medical conditions. Till date, this is the most comprehensive genetic variant resource for the Indian population and is made freely available for academic utility. The resource has also been accessed extensively by the worldwide community since it's launch.

Why are rare variants hard to impute? Coalescent models reveal theoretical limits in existing algorithms

Genotype imputation is an indispensable step in human genetic studies. Large reference panels with deeply sequenced genomes now allow interrogating variants with minor allele frequency < 1% without sequencing. While it is critical to consider limits of this approach, imputation methods for rare variants have only done so empirically; the theoretical basis of their imputation accuracy has not been explored. To provide theoretical consideration of imputation accuracy under the current imputation framework, we develop a coalescent model of imputing rare variants, leveraging the joint genealogy of the sample to be imputed and reference individuals. We show that broadly used imputation algorithms includes model miss-specifications about this joint genealogy that limit the ability to correctly impute rare variants. We develop closed-form solutions for the probability distribution of this joint genealogy and quantify the inevitable error rate resulting from the model miss-specification across a range of allele frequencies and reference sample sizes. We show that the probability of a falsely imputed minor allele decreases with reference sample size, but the proportion of falsely imputed minor alleles mostly depends on the allele count in the reference sample. We summarize the impact of this error on genotype imputation on association tests by calculating the r2 between imputed and true genotype and show that even when modeling other sources of error, the impact of the model miss-specification have a significant impact on the r2 of rare variants. These results provide a framework for developing new imputation algorithms and for interpreting rare variant association analyses.