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Fifty percent of variability in HIV-1 susceptibility is attributable to host genetics. Thus identifying genetic associations is essential to understanding pathogenesis of HIV-1 and important for targeting drug development. To date, however, CCR5 remains the only gene conclusively associated with HIV acquisition. To identify novel host genetic determinants of HIV-1 acquisition, we conducted a genome-wide association study among a high-risk sample of 3,136 injection drug users (IDUs) from the Urban Health Study (UHS). In addition to being IDUs, HIV-controls were frequency-matched to cases on environmental exposures to enhance detection of genetic effects. We tested independent replication in the Women's Interagency HIV Study (N=2,533). We also examined publicly available gene expression data to link SNPs associated with HIV acquisition to known mechanisms affecting HIV replication/infectivity. Analysis of the UHS nominated eight genetic regions for replication testing. SNP rs4878712 in FRMPD1 met multiple testing correction for independent replication (P=1.38x10(-4)), although the UHS-WIHS meta-analysis p-value did not reach genome-wide significance (P=4.47x10(-7) vs. P<5.0x10(-8)) Gene expression analyses provided promising biological support for the protective G allele at rs4878712 lowering risk of HIV: (1) the G allele was associated with reduced expression of FBXO10 (r=-0.49, P=6.9x10(-5)); (2) FBXO10 is a component of the Skp1-Cul1-F-box protein E3 ubiquitin ligase complex that targets Bcl-2 protein for degradation; (3) lower FBXO10 expression was associated with higher BCL2 expression (r=-0.49, P=8x10(-5)); (4) higher basal levels of Bcl-2 are known to reduce HIV replication and infectivity in human and animal in vitro studies. These results suggest new potential biological pathways by which host genetics affect susceptibility to HIV upon exposure for follow-up in subsequent studies.

Cardiomyocyte cell division and replication in mammals proceed through embryonic development and abruptly decline soon after birth. The process governing cardiomyocyte cell cycle arrest is poorly understood. Here we carry out whole-exome sequencing in an infant with evidence of persistent postnatal cardiomyocyte replication to determine the genetic risk factors. We identify compound heterozygous ALMS1 mutations in the proband, and confirm their presence in her affected sibling, one copy inherited from each heterozygous parent. Next, we recognize homozygous or compound heterozygous truncating mutations in ALMS1 in four other children with high levels of postnatal cardiomyocyte proliferation. Alms1 mRNA knockdown increases multiple markers of proliferation in cardiomyocytes, the percentage of cardiomyocytes in G2/M phases, and the number of cardiomyocytes by 10% in cultured cells. Homozygous Alms1 -mutant mice have increased cardiomyocyte proliferation at 2 weeks postnatal compared with wild-type littermates. We conclude that deficiency of Alström protein impairs postnatal cardiomyocyte cell cycle arrest. Cardiomyocyte cell cycle arrest is important for mammalian heart maturation, but the process is poorly understood. Here, the authors use exome sequencing to identify compound heterozygous ALMS1 mutations associated with cardiomyocyte replication and provide evidence that Alström protein deficiency impairs postnatal cardiomyocyte cell cycle arrest.

Karen Mohlke, Markku Laakso, Michael Boehnke and colleagues report the first application of the Illumina HumanExome Beadchip array, examining association with insulin and glycemic traits in 8,229 nondiabetic Finnish males from the population-based Metabolic Syndrome in Men (METSIM) study. They identify low-frequency coding variants at both known and newly associated loci with insulin processing and secretion. Insulin secretion has a crucial role in glucose homeostasis, and failure to secrete sufficient insulin is a hallmark of type 2 diabetes. Genome-wide association studies (GWAS) have identified loci contributing to insulin processing and secretion 1 , 2 ; however, a substantial fraction of the genetic contribution remains undefined. To examine low-frequency (minor allele frequency (MAF) 0.5–5%) and rare (MAF < 0.5%) nonsynonymous variants, we analyzed exome array data in 8,229 nondiabetic Finnish males using the Illumina HumanExome Beadchip. We identified low-frequency coding variants associated with fasting proinsulin concentrations at the SGSM2 and MADD GWAS loci and three new genes with low-frequency variants associated with fasting proinsulin or insulinogenic index: TBC1D30 , KANK1 and PAM . We also show that the interpretation of single-variant and gene-based tests needs to consider the effects of noncoding SNPs both nearby and megabases away. This study demonstrates that exome array genotyping is a valuable approach to identify low-frequency variants that contribute to complex traits.

Microarray single-nucleotide polymorphism genotyping, combined with imputation of untyped variants, has been widely adopted as an efficient means to interrogate variation across the human genome. “Genomic coverage” is the total proportion of genomic variation captured by an array, either by direct observation or through an indirect means such as linkage disequilibrium or imputation. We have performed imputation-based genomic coverage assessments of eight current genotyping arrays that assay from ~0.3 to ~5 million variants. Coverage was determined separately in each of the four continental ancestry groups in the 1000 Genomes Project phase 1 release. We used the subset of 1000 Genomes variants present on each array to impute the remaining variants and assessed coverage based on correlation between imputed and observed allelic dosages. More than 75% of common variants (minor allele frequency > 0.05) are covered by all arrays in all groups except for African ancestry, and up to ~90% in all ancestries for the highest density arrays. In contrast, less than 40% of less common variants (0.01 < minor allele frequency < 0.05) are covered by low density arrays in all ancestries and 50–80% in high density arrays, depending on ancestry. We also calculated genome-wide power to detect variant-trait association in a case-control design, across varying sample sizes, effect sizes, and minor allele frequency ranges, and compare these array-based power estimates with a hypothetical array that would type all variants in 1000 Genomes. These imputation-based genomic coverage and power analyses are intended as a practical guide to researchers planning genetic studies.

Kabuki syndrome is a monogenic disorder caused by loss of function variants in either of two genes encoding histone-modifying enzymes. We performed targeted sequencing in a cohort of 27 probands with a clinical diagnosis of Kabuki syndrome. Of these, 12 had causative variants in the two known Kabuki syndrome genes. In 2, we identified presumptive loss of function de novo variants in KMT2A (missense and splice site variants), a gene that encodes another histone modifying enzyme previously exclusively associated with Wiedermann-Steiner syndrome. Although Kabuki syndrome is a disorder of histone modification, we also find alterations in DNA methylation among individuals with a Kabuki syndrome diagnosis relative to matched normal controls, regardless of whether they carry a variant in KMT2A or KMT2D or not. Furthermore, we observed characteristic global abnormalities of DNA methylation that distinguished patients with a loss of function variant in KMT2D or missense or splice site variants in either KMT2D or KMT2A from normal controls. Our results provide new insights into the relationship of genotype to epigenotype and phenotype and indicate cross-talk between histone and DNA methylation machineries exposed by inborn errors of the epigenetic apparatus.

Insulin secretion plays a critical role in glucose homeostasis, and failure to secrete sufficient insulin is a hallmark of type 2 diabetes. Genome-wide association studies (GWAS) have identified loci contributing to insulin processing and secretion1,2; however, a substantial fraction of the genetic contribution remains undefined. To examine low-frequency (minor allele frequency (MAF) 0.5% to 5%) and rare (MAF<0.5%) nonsynonymous variants, we analyzed exome array data in 8,229 non-diabetic Finnish males. We identified low-frequency coding variants associated with fasting proinsulin levels at the SGSM2 and MADD GWAS loci and three novel genes with low-frequency variants associated with fasting proinsulin or insulinogenic index: TBC1D30, KANK1, and PAM. We also demonstrate that the interpretation of single-variant and gene-based tests needs to consider the effects of noncoding SNPs nearby and megabases (Mb) away. This study demonstrates that exome array genotyping is a valuable approach to identify low-frequency variants that contribute to complex traits.

Genotyping arrays have been widely adopted as an efficient means to interrogate variation across the human genome. Genetic variants may be observed either directly, via genotyping, or indirectly, through linkage disequilibrium with a genotyped variant. The total proportion of genomic variation captured by an array, either directly or indirectly, is referred to as genomic coverage. Here we use genotype imputation and Phase 3 of the 1000 Genomes Project to assess genomic coverage of several modern genotyping arrays. We find that in general, coverage increases with increasing array density. However, arrays designed to cover specific populations may yield better coverage in those populations compared to denser arrays not tailored to the given population. Ultimately, array choice involves trade-offs between cost, density, and coverage, and our work helps inform investigators weighing these choices and trade-offs.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

Genome-wide association studies (GWAS) have laid the foundation for many downstream investigations, including the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of human variation and hinders the translation of genetic associations into clinical and public health applications. To demonstrate the benefit of studying underrepresented populations, the Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using novel strategies for multi-ethnic analysis of admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 28 novel loci and 42 residual signals in known loci. Our data show strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, which substantially restricts genetically-guided precision medicine. We advocate for new, large genome-wide efforts in diverse populations to reduce health disparities.