High-resolution African HLA resource uncovers HLA-DRB1 expression effects underlying vaccine response.

How human genetic variation contributes to vaccine effectiveness in infants is unclear, and data are limited on these relationships in populations with African ancestries. We undertook genetic analyses of vaccine antibody responses in infants from Uganda (n = 1391), Burkina Faso (n = 353) and South Africa (n = 755), identifying associations between human leukocyte antigen (HLA) and antibody response for five of eight tested antigens spanning pertussis, diphtheria and hepatitis B vaccines. In addition, through HLA typing 1,702 individuals from 11 populations of African ancestry derived predominantly from the 1000 Genomes Project, we constructed an imputation resource, fine-mapping class II HLA-DR and DQ associations explaining up to 10% of antibody response variance in our infant cohorts. We observed differences in the genetic architecture of pertussis antibody response between the cohorts with African ancestries and an independent cohort with European ancestry, but found no in silico evidence of differences in HLA peptide binding affinity or breadth. Using immune cell expression quantitative trait loci datasets derived from African-ancestry samples from the 1000 Genomes Project, we found evidence of differential HLA-DRB1 expression correlating with inferred protection from pertussis following vaccination. This work suggests that HLA-DRB1 expression may play a role in vaccine response and should be considered alongside peptide selection to improve vaccine design.

Transcriptomics and chromatin accessibility in multiple African population samples.

Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.

Africa-specific human genetic variation near CHD1L associates with HIV-1 load.

HIV-1 remains a global health crisis1, highlighting the need to identify new targets for therapies. Here, given the disproportionate HIV-1 burden and marked human genome diversity in Africa2, we assessed the genetic determinants of control of set-point viral load in 3,879 people of African ancestries living with HIV-1 participating in the international collaboration for the genomics of HIV3. We identify a previously undescribed association signal on chromosome 1 where the peak variant associates with an approximately 0.3 log10-transformed copies per ml lower set-point viral load per minor allele copy and is specific to populations of African descent. The top associated variant is intergenic and lies between a long intergenic non-coding RNA (LINC00624) and the coding gene CHD1L, which encodes a helicase that is involved in DNA repair4. Infection assays in iPS cell-derived macrophages and other immortalized cell lines showed increased HIV-1 replication in CHD1L-knockdown and CHD1L-knockout cells. We provide evidence from population genetic studies that Africa-specific genetic variation near CHD1L associates with HIV replication in vivo. Although experimental studies suggest that CHD1L is able to limit HIV infection in some cell types in vitro, further investigation is required to understand the mechanisms underlying our observations, including any potential indirect effects of CHD1L on HIV spread in vivo that our cell-based assays cannot recapitulate.

Targeting hepatitis B vaccine escape using immunogenetics in Bangladeshi infants.

Hepatitis B virus (HBV) vaccine escape mutants (VEM) are increasingly described, threatening progress in control of this virus worldwide. Here we studied the relationship between host genetic variation, vaccine immunogenicity and viral sequences implicating VEM emergence. In a cohort of 1,096 Bangladeshi children, we identified human leukocyte antigen (HLA) variants associated with response vaccine antigens. Using an HLA imputation panel with 9,448 south Asian individuals DPB1*04:01 was associated with higher HBV antibody responses (p=4.5×10-30). The underlying mechanism is a result of higher affinity binding of HBV surface antigen epitopes to DPB1*04:01 dimers. This is likely a result of evolutionary pressure at the HBV surface antigen 'a-determinant' segment incurring VEM specific to HBV. Prioritizing pre-S isoform HBV vaccines may tackle the rise of HBV vaccine evasion.

Distinct genetic architectures and environmental factors associate with host response to the γ2-herpesvirus infections.

Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr Virus (EBV) establish life-long infections and are associated with malignancies. Striking geographic variation in incidence and the fact that virus alone is insufficient to cause disease, suggests other co-factors are involved. Here we present epidemiological analysis and genome-wide association study (GWAS) in 4365 individuals from an African population cohort, to assess the influence of host genetic and non-genetic factors on virus antibody responses. EBV/KSHV co-infection (OR = 5.71(1.58-7.12)), HIV positivity (OR = 2.22(1.32-3.73)) and living in a more rural area (OR = 1.38(1.01-1.89)) are strongly associated with immunogenicity. GWAS reveals associations with KSHV antibody response in the HLA-B/C region (p = 6.64 × 10-09). For EBV, associations are identified for VCA (rs71542439, p = 1.15 × 10-12). Human leucocyte antigen (HLA) and trans-ancestry fine-mapping substantiate that distinct variants in HLA-DQA1 (p = 5.24 × 10-44) are driving associations for EBNA-1 in Africa. This study highlights complex interactions between KSHV and EBV, in addition to distinct genetic architectures resulting in important differences in pathogenesis and transmission.

Genome-Wide Association Study of Cryptosporidiosis in Infants Implicates PRKCA.

Diarrhea is a major cause of both morbidity and mortality worldwide, especially among young children. Cryptosporidiosis is a leading cause of diarrhea in children, particularly in South Asia and sub-Saharan Africa, where it is responsible for over 200,000 deaths per year. Beyond the initial clinical presentation of diarrhea, it is associated with long-term sequelae such as malnutrition and neurocognitive developmental deficits. Risk factors include poverty and overcrowding, and yet not all children with these risk factors and exposure are infected, nor do all infected children develop symptomatic disease. One potential risk factor to explain these differences is their human genome. To identify genetic variants associated with symptomatic cryptosporidiosis, we conducted a genome-wide association study (GWAS) examining 6.5 million single nucleotide polymorphisms (SNPs) in 873 children from three independent cohorts in Dhaka, Bangladesh, namely, the Dhaka Birth Cohort (DBC), the Performance of Rotavirus and Oral Polio Vaccines in Developing Countries (PROVIDE) study, and the Cryptosporidiosis Birth Cohort (CBC). Associations were estimated separately for each cohort under an additive model, adjusting for length-for-age Z-score at 12 months of age, the first two principal components to account for population substructure, and genotyping batch. The strongest meta-analytic association was with rs58296998 (P = 3.73 × 10-8), an intronic SNP and expression quantitative trait locus (eQTL) of protein kinase C alpha (PRKCA). Each additional risk allele conferred 2.4 times the odds of Cryptosporidium-associated diarrhea in the first year of life. This genetic association suggests a role for protein kinase C alpha in pediatric cryptosporidiosis and warrants further investigation.IMPORTANCE Globally, diarrhea remains one of the major causes of pediatric morbidity and mortality. The initial symptoms of diarrhea can often lead to long-term consequences for the health of young children, such as malnutrition and neurocognitive developmental deficits. Despite many children having similar exposures to infectious causes of diarrhea, not all develop symptomatic disease, indicating a possible role for human genetic variation. Here, we conducted a genetic study of susceptibility to symptomatic disease associated with Cryptosporidium infection (a leading cause of diarrhea) in three independent cohorts of infants from Dhaka, Bangladesh. We identified a genetic variant within protein kinase C alpha (PRKCA) associated with higher risk of cryptosporidiosis in the first year of life. These results indicate a role for human genetics in susceptibility to cryptosporidiosis and warrant further research to elucidate the mechanism.

Uganda Genome Resource Enables Insights into Population History and Genomic Discovery in Africa.

Genomic studies in African populations provide unique opportunities to understand disease etiology, human diversity, and population history. In the largest study of its kind, comprising genome-wide data from 6,400 individuals and whole-genome sequences from 1,978 individuals from rural Uganda, we find evidence of geographically correlated fine-scale population substructure. Historically, the ancestry of modern Ugandans was best represented by a mixture of ancient East African pastoralists. We demonstrate the value of the largest sequence panel from Africa to date as an imputation resource. Examining 34 cardiometabolic traits, we show systematic differences in trait heritability between European and African populations, probably reflecting the differential impact of genes and environment. In a multi-trait pan-African GWAS of up to 14,126 individuals, we identify novel loci associated with anthropometric, hematological, lipid, and glycemic traits. We find that several functionally important signals are driven by Africa-specific variants, highlighting the value of studying diverse populations across the region.

The ferroportin Q248H mutation protects from anemia, but not malaria or bacteremia.

Iron acquisition is critical for life. Ferroportin (FPN) exports iron from mature erythrocytes, and deletion of the Fpn gene results in hemolytic anemia and increased fatality in malaria-infected mice. The FPN Q248H mutation (glutamine to histidine at position 248) renders FPN partially resistant to hepcidin-induced degradation and was associated with protection from malaria in human studies of limited size. Using data from cohorts including over 18,000 African children, we show that the Q248H mutation is associated with modest protection against anemia, hemolysis, and iron deficiency, but we found little evidence of protection against severe malaria or bacteremia. We additionally observed no excess Plasmodium growth in Q248H erythrocytes ex vivo, nor evidence of selection driven by malaria exposure, suggesting that the Q248H mutation does not protect from malaria and is unlikely to deprive malaria parasites of iron essential for their growth.

Genome-wide association study of type 2 diabetes in Africa.

AIMS/HYPOTHESIS: Genome-wide association studies (GWAS) for type 2 diabetes have uncovered >400 risk loci, primarily in populations of European and Asian ancestry. Here, we aimed to discover additional type 2 diabetes risk loci (including African-specific variants) and fine-map association signals by performing genetic analysis in African populations. METHODS: We conducted two type 2 diabetes genome-wide association studies in 4347 Africans from South Africa, Nigeria, Ghana and Kenya and meta-analysed both studies together. Likely causal variants were identified using fine-mapping approaches. RESULTS: The most significantly associated variants mapped to the widely replicated type 2 diabetes risk locus near TCF7L2 (p = 5.3 × 10-13). Fine-mapping of the TCF7L2 locus suggested one type 2 diabetes association signal shared between Europeans and Africans (indexed by rs7903146) and a distinct African-specific signal (indexed by rs17746147). We also detected one novel signal, rs73284431, near AGMO (p = 5.2 × 10-9, minor allele frequency [MAF] = 0.095; monomorphic in most non-African populations), distinct from previously reported signals in the region. In analyses focused on 100 published type 2 diabetes risk loci, we identified 21 with shared causal variants in African and non-African populations. CONCLUSIONS/INTERPRETATION: These results demonstrate the value of performing GWAS in Africans, provide a resource to larger consortia for further discovery and fine-mapping and indicate that additional large-scale efforts in Africa are warranted to gain further insight in to the genetic architecture of type 2 diabetes.

Complimentary Methods for Multivariate Genome-Wide Association Study Identify New Susceptibility Genes for Blood Cell Traits.

Genome-wide association studies (GWAS) have found hundreds of novel loci associated with full blood count (FBC) phenotypes. However, most of these studies were performed in a single phenotype framework without putting into consideration the clinical relatedness among traits. In this work, in addition to the standard univariate GWAS, we also use two different multivariate methods to perform the first multiple traits GWAS of FBC traits in ∼7000 individuals from the Ugandan General Population Cohort (GPC). We started by performing the standard univariate GWAS approach. We then performed our first multivariate method, in this approach, we tested for marker associations with 15 FBC traits simultaneously in a multivariate mixed model implemented in GEMMA while accounting for the relatedness of individuals and pedigree structures, as well as population substructure. In this analysis, we provide a framework for the combination of multiple phenotypes in multivariate GWAS analysis and show evidence of multi-collinearity whenever the correlation between traits exceeds the correlation coefficient threshold of r 2 >=0.75. This approach identifies two known and one novel loci. In the second multivariate method, we applied principal component analysis (PCA) to the same 15 correlated FBC traits. We then tested for marker associations with each PC in univariate linear mixed models implemented in GEMMA. We show that the FBC composite phenotype as assessed by each PC expresses information that is not completely encapsulated by the individual FBC traits, as this approach identifies three known and five novel loci that were not identified using both the standard univariate and multivariate GWAS methods. Across both multivariate methods, we identified six novel loci. As a proof of concept, both multivariate methods also identified known loci, HBB and ITFG3. The two multivariate methods show that multivariate genotype-phenotype methods increase power and identify novel genotype-phenotype associations not found with the standard univariate GWAS in the same dataset.

Genome-Wide Association Study Reveals Genetic Link between Diarrhea-Associated Entamoeba histolytica Infection and Inflammatory Bowel Disease.

Entamoeba histolytica is the etiologic agent of amebic dysentery, though clinical manifestation of infection is highly variable ranging from subclinical colonization to invasive disease. We hypothesize that host genetics contribute to the variable outcomes of E. histolytica infection; thus, we conducted a genome-wide association study (GWAS) in two independent birth cohorts of Bangladeshi infants monitored for susceptibility to E. histolytica disease in the first year of life. Children with at least one diarrheal episode positive for E. histolytica (cases) were compared to children with no detectable E. histolytica infection in the same time frame (controls). Meta-analyses under a fixed-effect inverse variance weighting model identified multiple variants in a region of chromosome 10 containing loci associated with symptomatic E. histolytica infection. An intergenic insertion between CREM and CCNY (rs58000832) achieved genome-wide significance (P value from meta-analysis [Pmeta] = 6.05 × 10-9), and each additional risk allele of rs58000832 conferred 2.42 increased odds of a diarrhea-associated E. histolytica infection. The most strongly associated single nucleotide polymorphism (SNP) within a gene was in an intron of CREM (rs58468612; Pmeta = 8.94 × 10-8), which has been implicated as a susceptibility locus for inflammatory bowel disease (IBD). Gene expression resources suggest associated loci are related to the lower expression of CREM Increased CREM expression is also observed in early E. histolytica infection. Further, CREM-/- mice were more susceptible to E. histolytica amebic colitis. These genetic associations reinforce the pathological similarities observed in gut inflammation between E. histolytica infection and IBD.IMPORTANCE Diarrhea is the second leading cause of death for children globally, causing 760,000 deaths each year in children less than 5 years old. Amebic dysentery contributes significantly to this burden, especially in developing countries. The identification of host factors that control or enable enteric pathogens has the potential to transform our understanding of disease predisposition, outcomes, and treatments. Our discovery of the transcriptional regulator cAMP-responsive element modulator (CREM) as a genetic modifier of susceptibility to amebic disease has implications for understanding the pathogenesis of other diarrheal infections. Further, emerging evidence for CREM in IBD susceptibility suggests that CREM is a critical regulator of enteric inflammation and may have broad therapeutic potential as a drug target across intestinal inflammatory diseases.

The First Norovirus Longitudinal Seroepidemiological Study From Sub-Saharan Africa Reveals High Seroprevalence of Diverse Genotypes Associated With Host Susceptibility Factors.

Background: Human noroviruses (HuNoVs) are a prominent cause of gastroenteritis, yet fundamental questions remain regarding epidemiology, diversity, and immunity in sub-Saharan African children. We investigated HuNoV seroprevalence and genetic and sociodemographic risk factors in Ugandan children. Methods: We randomly screened 797 participants of a longitudinal birth cohort (Entebbe, EMaBS) and 378 from a cross-sectional survey (rural Lake Victoria, LaVIISWA), for antibodies against HuNoV genotypes by ELISA. We used linear regression modeling to test for associations between HuNoV antibody levels and sociodemographic factors, and with the human susceptibility rs601338 FUT2 secretor SNP and histo-blood group antigens (A/B/O). Results: Of EMaBS participants, 76.6% were seropositive by age 1, rising to 94.5% by age 2 years. Seroprevalence in 1 year olds of the rural LaVIISWA survey was even higher (95%). In the birth cohort, 99% of seropositive 2 year olds had responses to multiple HuNoV genotypes. We identified associations between secretor status and genogroup GII antibody levels (GII.4 P = 3.1 × 10-52), as well as ABO and GI (GI.2 P = 2.1 × 10-12). Conclusions: HuNoVs are highly prevalent in Ugandan children, indicating a substantial burden of diarrhea-associated morbidity with recurrent infections. Public health interventions, including vaccination, and increased surveillance are urgently needed.

Multiplex genomewide association analysis of breast milk fatty acid composition extends the phenotypic association and potential selection of FADS1 variants to arachidonic acid, a critical infant micronutrient.

BACKGROUND: Breast milk is the sole nutrition source during exclusive breastfeeding, and polyunsaturated fatty acids (FAs) are critical micronutrients in infant physical and cognitive development. There has been no prior genomewide association study of breast milk, hence our objective was to test for genetic association with breast milk FA composition. METHODS: We measured the fractional composition of 26 individual FAs in breast milk samples from three cohorts totalling 1142 Bangladeshi mothers whose infants were genotyped on the Illumina MEGA chip and replicated on a custom Affymetrix 30K SNP array (n=616). Maternal genotypes were imputed using IMPUTE. RESULTS: After running 33 separate FA fraction phenotypes, we found that SNPs known to be associated with serum FAs in the FADS1/2/3 region were also associated with breast milk FA composition (experiment-wise significance threshold 4.2×10-9). Hypothesis-neutral comparison of the 33 fractions showed that the most significant genetic association at the FADS1/2/3 locus was with fraction of arachidonic acid (AA) at SNP rs174556, with a very large per major allele effect size of 17% higher breast milk AA level. There was no evidence of independent association at FADS1/2/3 with any other FA or SNP after conditioning on AA and rs174556. We also found novel significant experiment-wise SNP associations with: polyunsaturated fatty acid (PUFA) 6/PUFA3 ratio (sorting nexin 29), eicosenoic (intergenic) and capric (component of oligomeric Golgi complex 3) acids; and six additional loci at genomewide significance (<5×10-8). CONCLUSIONS: AA is the primary FA in breast milk influenced by genetic variation at the FADS1/2/3 locus, extending the potential phenotypes under genetic selection to include breast milk composition, thereby possibly affecting infant growth or cognition. Breast milk FA composition is influenced by maternal genetics in addition to diet and body composition.

Linear mixed model for heritability estimation that explicitly addresses environmental variation.

The linear mixed model (LMM) is now routinely used to estimate heritability. Unfortunately, as we demonstrate, LMM estimates of heritability can be inflated when using a standard model. To help reduce this inflation, we used a more general LMM with two random effects-one based on genomic variants and one based on easily measured spatial location as a proxy for environmental effects. We investigated this approach with simulated data and with data from a Uganda cohort of 4,778 individuals for 34 phenotypes including anthropometric indices, blood factors, glycemic control, blood pressure, lipid tests, and liver function tests. For the genomic random effect, we used identity-by-descent estimates from accurately phased genome-wide data. For the environmental random effect, we constructed a covariance matrix based on a Gaussian radial basis function. Across the simulated and Ugandan data, narrow-sense heritability estimates were lower using the more general model. Thus, our approach addresses, in part, the issue of "missing heritability" in the sense that much of the heritability previously thought to be missing was fictional. Software is available at https://github.com/MicrosoftGenomics/FaST-LMM.

The African Genome Variation Project shapes medical genetics in Africa.

Given the importance of Africa to studies of human origins and disease susceptibility, detailed characterization of African genetic diversity is needed. The African Genome Variation Project provides a resource with which to design, implement and interpret genomic studies in sub-Saharan Africa and worldwide. The African Genome Variation Project represents dense genotypes from 1,481 individuals and whole-genome sequences from 320 individuals across sub-Saharan Africa. Using this resource, we find novel evidence of complex, regionally distinct hunter-gatherer and Eurasian admixture across sub-Saharan Africa. We identify new loci under selection, including loci related to malaria susceptibility and hypertension. We show that modern imputation panels (sets of reference genotypes from which unobserved or missing genotypes in study sets can be inferred) can identify association signals at highly differentiated loci across populations in sub-Saharan Africa. Using whole-genome sequencing, we demonstrate further improvements in imputation accuracy, strengthening the case for large-scale sequencing efforts of diverse African haplotypes. Finally, we present an efficient genotype array design capturing common genetic variation in Africa.

Progress in the prediction of pKa values in proteins.

The pK(a) -cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pK(a) values and protein electrostatics in general. The first round of the pK(a) -cooperative, which challenged computational labs to carry out blind predictions against pK(a) s experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6-10, 2009). This article serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here, we briefly outline existing approaches for pK(a) calculations, emphasizing methods that were used by the participants in calculating the blind pK(a) values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pK(a) calculations.

On the development of protein pKa calculation algorithms.

Protein pK(a) calculation methods are developed partly to provide fast non-experimental estimates of the ionization constants of protein side chains. However, the most significant reason for developing such methods is that a good pK(a) calculation method is presumed to provide an accurate physical model of protein electrostatics, which can be applied in methods for drug design, protein design, and other structure-based energy calculation methods. We explore the validity of this presumption by simulating the development of a pK(a) calculation method using artificial experimental data derived from a human-defined physical reality. We examine the ability of an RMSD-guided development protocol to retrieve the correct (artificial) physical reality and find that a rugged optimization landscape and a huge parameter space prevent the identification of the correct physical reality. We examine the importance of the training set in developing pK(a) calculation methods and investigate the effect of experimental noise on our ability to identify the correct physical reality, and find that both effects have a significant and detrimental impact on the physical reality of the optimal model identified. Our findings are of relevance to all structure-based methods for protein energy calculations and simulation, and have large implications for all types of current pK(a) calculation methods. Our analysis furthermore suggests that careful and extensive validation on many types of experimental data can go some way in making current models more realistic.

Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies.

Large amounts of data are being generated annually on the connection between the sequence, structure and function of proteins using site-directed mutagenesis, protein design and directed evolution techniques. These data provide the fundamental building blocks for our understanding of protein function, molecular biology and living organisms in general. However, much experimental data are never deposited in databases and is thus 'lost' in journal publications or in PhD theses. At the same time theoretical scientists are in need of large amounts of experimental data for benchmarking and calibrating novel predictive algorithms, and theoretical progress is therefore often hampered by the lack of suitable data to validate or disprove a theoretical assumption. We present PEAT (Protein Engineering Analysis Tool), an application that integrates data deposition, storage and analysis for researchers carrying out protein engineering projects or biophysical characterization of proteins. PEAT contains modules for DNA sequence manipulation, primer design, fitting of biophysical characterization data (enzyme kinetics, circular dichroism spectroscopy, NMR titration data, etc.), and facilitates sharing of experimental data and analyses for a typical university-based research group. PEAT is freely available to academic researchers at http://enzyme.ucd.ie/PEAT.

Structural artifacts in protein-ligand X-ray structures: implications for the development of docking scoring functions.

The development of docking scoring functions requires high-resolution 3D structures of protein-ligand complexes for which the binding affinity of the ligand has been measured experimentally. Protein-ligand binding affinities are measured in solution experiments, and high resolution protein-ligand structures can be determined only by X-ray crystallography. Protein-ligand scoring functions must therefore reproduce solution binding energies using analyses of proteins in a crystal environment. We present an analysis of the prevalence of crystal-induced artifacts and water-mediated contacts in protein-ligand complexes and demonstrate the effect that these can have on the performance of protein-ligand scoring functions. We find 36% of ligands in the PDBBind 2007 refined data set to be influenced by crystal contacts and find the performance of a scoring function to be affected by these. A Web server for detecting crystal contacts in protein-ligand complexes is available at http://enzyme.ucd.ie/LIGCRYST .