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Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.

Patients with inherited retinal dystrophies (IRDs) were recruited from two understudied populations: Mexico and Pakistan as well as a third well-studied population of European Americans to define the genetic architecture of IRD by performing whole-genome sequencing (WGS). Whole-genome analysis was performed on 409 individuals from 108 unrelated pedigrees with IRDs. All patients underwent an ophthalmic evaluation to establish the retinal phenotype. Although the 108 pedigrees in this study had previously been examined for mutations in known IRD genes using a wide range of methodologies including targeted gene(s) or mutation(s) screening, linkage analysis and exome sequencing, the gene mutations responsible for IRD in these 108 pedigrees were not determined. WGS was performed on these pedigrees using Illumina X10 at a minimum of 30X depth. The sequence reads were mapped against hg19 followed by variant calling using GATK. The genome variants were annotated using SnpEff, PolyPhen2, and CADD score; the structural variants (SVs) were called using GenomeSTRiP and LUMPY. We identified potential causative sequence alterations in 61 pedigrees (57%), including 39 novel and 54 reported variants in IRD genes. For 57 of these pedigrees the observed genotype was consistent with the initial clinical diagnosis, the remaining 4 had the clinical diagnosis reclassified based on our findings. In seven pedigrees (12%) we observed atypical causal variants, i.e. unexpected genotype(s), including 4 pedigrees with causal variants in more than one IRD gene within all affected family members, one pedigree with intrafamilial genetic heterogeneity (different affected family members carrying causal variants in different IRD genes), one pedigree carrying a dominant causative variant present in pseudo-recessive form due to consanguinity and one pedigree with a de-novo variant in the affected family member. Combined atypical and large structural variants contributed to about 20% of cases. Among the novel mutations, 75% were detected in Mexican and 50% found in European American pedigrees and have not been reported in any other population while only 20% were detected in Pakistani pedigrees and were not previously reported. The remaining novel IRD causative variants were listed in gnomAD but were found to be very rare and population specific. Mutations in known IRD associated genes contributed to pathology in 63% Mexican, 60% Pakistani and 45% European American pedigrees analyzed. Overall, contribution of known IRD gene variants to disease pathology in these three populations was similar to that observed in other populations worldwide. This study revealed a spectrum of mutations contributing to IRD in three populations, identified a large proportion of novel potentially causative variants that are specific to the corresponding population or not reported in gnomAD and shed light on the genetic architecture of IRD in these diverse global populations.

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

Juvenile dermatomyositis (JDM) is a chronic inflammatory myopathy and vasculopathy driven by genetic and environmental influences. Here, we investigated the genetic underpinnings of an analogous, spontaneous disease of dogs also termed dermatomyositis (DMS). As in JDM, we observed a significant association with a haplotype of the major histocompatibility complex (MHC) (DLA-DRB1*002:01/-DQA1*009:01/-DQB1*001:01), particularly in homozygosity (P-val = 0.0001). However, the high incidence of the haplotype among healthy dogs indicated that additional genetic risk factors are likely involved in disease progression. We conducted genome-wide association studies in two modern breeds having common ancestry and detected strong associations with novel loci on canine chromosomes 10 (P-val = 2.3X10-12) and 31 (P-val = 3.95X10-8). Through whole genome resequencing, we identified primary candidate polymorphisms in conserved regions of PAN2 (encoding p.Arg492Cys) and MAP3K7CL (c.383_392ACTCCACAAA>GACT) on chromosomes 10 and 31, respectively. Analyses of these polymorphisms and the MHC haplotypes revealed that nine of 27 genotypic combinations confer high or moderate probability of disease and explain 93% of cases studied. The pattern of disease risk across PAN2 and MAP3K7CL genotypes provided clear evidence for a significant epistatic foundation for this disease, a risk further impacted by MHC haplotypes. We also observed a genotype-phenotype correlation wherein an earlier age of onset is correlated with an increased number of risk alleles at PAN2 and MAP3K7CL. High frequencies of multiple genetic risk factors are unique to affected breeds and likely arose coincident with artificial selection for desirable phenotypes. Described herein is the first three-locus association with a complex canine disease and two novel loci that provide targets for exploration in JDM and related immunological dysfunction.

Gliomas are the most common form of malignant primary brain tumors in humans and second most common in dogs, occurring with similar frequencies in both species. Dogs are valuable spontaneous models of human complex diseases including cancers and may provide insight into disease susceptibility and oncogenesis. Several brachycephalic breeds such as Boxer, Bulldog and Boston Terrier have an elevated risk of developing glioma, but others, including Pug and Pekingese, are not at higher risk. To identify glioma-associated genetic susceptibility factors, an across-breed genome-wide association study (GWAS) was performed on 39 dog glioma cases and 141 controls from 25 dog breeds, identifying a genome-wide significant locus on canine chromosome (CFA) 26 (p = 2.8 x 10-8). Targeted re-sequencing of the 3.4 Mb candidate region was performed, followed by genotyping of the 56 SNVs that best fit the association pattern between the re-sequenced cases and controls. We identified three candidate genes that were highly associated with glioma susceptibility: CAMKK2, P2RX7 and DENR. CAMKK2 showed reduced expression in both canine and human brain tumors, and a non-synonymous variant in P2RX7, previously demonstrated to have a 50% decrease in receptor function, was also associated with disease. Thus, one or more of these genes appear to affect glioma susceptibility.

Macrophages display flexible activation states that range between pro-inflammatory (classical activation) and anti-inflammatory (alternative activation). These macrophage polarization states contribute to a variety of organismal phenotypes such as tissue remodeling and susceptibility to infectious and inflammatory diseases. Several macrophage- or immune-related genes have been shown to modulate infectious and inflammatory disease pathogenesis. However, the potential role that differences in macrophage activation phenotypes play in modulating differences in susceptibility to infectious and inflammatory disease is just emerging. We integrated transcriptional profiling and linkage analyses to determine the genetic basis for the differential murine macrophage response to inflammatory stimuli and to infection with the obligate intracellular parasite Toxoplasma gondii. We show that specific transcriptional programs, defined by distinct genomic loci, modulate macrophage activation phenotypes. In addition, we show that the difference between AJ and C57BL/6J macrophages in controlling Toxoplasma growth after stimulation with interferon gamma and tumor necrosis factor alpha mapped to chromosome 3, proximal to the Guanylate binding protein (Gbp) locus that is known to modulate the murine macrophage response to Toxoplasma. Using an shRNA-knockdown strategy, we show that the transcript levels of an RNA helicase, Ddx1, regulates strain differences in the amount of nitric oxide produced by macrophage after stimulation with interferon gamma and tumor necrosis factor. Our results provide a template for discovering candidate genes that modulate macrophage-mediated complex traits.

Modulating natural killer cell functions in human immunity and reproduction are diverse interactions between the killer cell immunoglobulin-like receptors (KIR) of Natural Killer (NK) cells and HLA class I ligands on the surface of tissue cells. Dominant interactions are between KIR2DL1 and the C2 epitope of HLA-C and between KIR2DL2/3 and the C1 epitope of HLA-C. KhoeSan hunter-gatherers of Southern Africa represent the earliest population divergence known and are the most genetically diverse indigenous people, qualities reflected in their KIR and HLA genes. Of the ten KhoeSan KIR2DL1 alleles, KIR2DL1*022 and KIR2DL1*026 likely originated in the KhoeSan, and later were transmitted at low frequency to the neighboring Zulus through gene flow. These alleles arose by point mutation from other KhoeSan KIR2DL1 alleles that are more widespread globally. Mutation of KIR2DL1*001 gave rise to KIR2DL1*022, causing loss of C2 recognition and gain of C1 recognition. This makes KIR2DL1*022 a more avid and specific C1 receptor than any KIR2DL2/3 allotype. Mutation of KIR2DL1*012 gave rise to KIR2DL1*026, causing premature termination of translation at the end of the transmembrane domain. This makes KIR2DL1*026 a membrane-associated receptor that lacks both a cytoplasmic tail and signaling function. At higher frequencies than their parental allotypes, the combined effect of the KhoeSan-specific KIR2DL1*022 and KIR2DL1*026 is to reduce the frequency of strong inhibitory C2 receptors and increase the frequency of strong inhibitory C1 receptors. Because interaction of KIR2DL1 with C2 is associated with risk of pregnancy disorder, these functional changes are potentially advantageous. Whereas all other KhoeSan KIR2DL1 alleles are present on a wide diversity of centromeric KIR haplotypes, KIR2DL1*026 is present on a single KIR haplotype and KIR2DL1*022 is present on two very similar haplotypes. The high linkage disequilibrium across their haplotypes is consistent with a recent emergence for these KIR2DL1 alleles that have distinctive functions.

Children affected by Specific Language Impairment (SLI) fail to acquire age appropriate language skills despite adequate intelligence and opportunity. SLI is highly heritable, but the understanding of underlying genetic mechanisms has proved challenging. In this study, we use molecular genetic techniques to investigate an admixed isolated founder population from the Robinson Crusoe Island (Chile), who are affected by a high incidence of SLI, increasing the power to discover contributory genetic factors. We utilize exome sequencing in selected individuals from this population to identify eight coding variants that are of putative significance. We then apply association analyses across the wider population to highlight a single rare coding variant (rs144169475, Minor Allele Frequency of 4.1% in admixed South American populations) in the NFXL1 gene that confers a nonsynonymous change (N150K) and is significantly associated with language impairment in the Robinson Crusoe population (p = 2.04 × 10-4, 8 variants tested). Subsequent sequencing of NFXL1 in 117 UK SLI cases identified four individuals with heterozygous variants predicted to be of functional consequence. We conclude that coding variants within NFXL1 confer an increased risk of SLI within a complex genetic model.

Recent studies are starting to show that genetic control over stochastic variation is a key evolutionary solution of single celled organisms in the face of unpredictable environments. This has been expanded to show that genetic variation can alter stochastic variation in transcriptional processes within multi-cellular eukaryotes. However, little is known about how genetic diversity can control stochastic variation within more non-cell autonomous phenotypes. Using an Arabidopsis reciprocal RIL population, we showed that there is significant genetic diversity influencing stochastic variation in the plant metabolome, defense chemistry, and growth. This genetic diversity included loci specific for the stochastic variation of each phenotypic class that did not affect the other phenotypic classes or the average phenotype. This suggests that the organism's networks are established so that noise can exist in one phenotypic level like metabolism and not permeate up or down to different phenotypic levels. Further, the genomic variation within the plastid and mitochondria also had significant effects on the stochastic variation of all phenotypic classes. The genetic influence over stochastic variation within the metabolome was highly metabolite specific, with neighboring metabolites in the same metabolic pathway frequently showing different levels of noise. As expected from bet-hedging theory, there was more genetic diversity and a wider range of stochastic variation for defense chemistry than found for primary metabolism. Thus, it is possible to begin dissecting the stochastic variation of whole organismal phenotypes in multi-cellular organisms. Further, there are loci that modulate stochastic variation at different phenotypic levels. Finding the identity of these genes will be key to developing complete models linking genotype to phenotype.

The ability to taste bitterness evolved to safeguard most animals, including humans, against potentially toxic substances, thereby leading to food rejection. Nonetheless, bitter perception is subject to individual variations due to the presence of genetic functional polymorphisms in bitter taste receptor (TAS2R) genes, such as the long-known association between genetic polymorphisms in TAS2R38 and bitter taste perception of phenylthiocarbamide. Yet, due to overlaps in specificities across receptors, such associations with a single TAS2R locus are uncommon. Therefore, to investigate more complex associations, we examined taste responses to six structurally diverse compounds (absinthin, amarogentin, cascarillin, grosheimin, quassin, and quinine) in a sample of the Caucasian population. By sequencing all bitter receptor loci, inferring long-range haplotypes, mapping their effects on phenotype variation, and characterizing functionally causal allelic variants, we deciphered at the molecular level how a subjects' genotype for the whole-family of TAS2R genes shapes variation in bitter taste perception. Within each haplotype block implicated in phenotypic variation, we provided evidence for at least one locus harboring functional polymorphic alleles, e.g. one locus for sensitivity to amarogentin, one of the most bitter natural compounds known, and two loci for sensitivity to grosheimin, one of the bitter compounds of artichoke. Our analyses revealed also, besides simple associations, complex associations of bitterness sensitivity across TAS2R loci. Indeed, even if several putative loci harbored both high- and low-sensitivity alleles, phenotypic variation depended on linkage between these alleles. When sensitive alleles for bitter compounds were maintained in the same linkage phase, genetically driven perceptual differences were obvious, e.g. for grosheimin. On the contrary, when sensitive alleles were in opposite phase, only weak genotype-phenotype associations were seen, e.g. for absinthin, the bitter principle of the beverage absinth. These findings illustrate the extent to which genetic influences on taste are complex, yet arise from both receptor activation patterns and linkage structure among receptor genes.

Little is known about how changes in DNA methylation mediate risk for human diseases including dementia. Analysis of genome-wide methylation patterns in patients with two forms of tau-related dementia--progressive supranuclear palsy (PSP) and frontotemporal dementia (FTD)--revealed significant differentially methylated probes (DMPs) in patients versus unaffected controls. Remarkably, DMPs in PSP were clustered within the 17q21.31 region, previously known to harbor the major genetic risk factor for PSP. We identified and replicated a dose-dependent effect of the risk-associated H1 haplotype on methylation levels within the region in blood and brain. These data reveal that the H1 haplotype increases risk for tauopathy via differential methylation at that locus, indicating a mediating role for methylation in dementia pathophysiology.

An important paradigm in evolutionary genetics is that of a delicate balance between genetic variants that favorably boost host control of infection but which may unfavorably increase susceptibility to autoimmune disease. Here, we investigated whether patients with psoriasis, a common immune-mediated disease of the skin, are enriched for genetic variants that limit the ability of HIV-1 virus to replicate after infection. We analyzed the HLA class I and class II alleles of 1,727 Caucasian psoriasis cases and 3,581 controls and found that psoriasis patients are significantly more likely than controls to have gene variants that are protective against HIV-1 disease. This includes several HLA class I alleles associated with HIV-1 control; amino acid residues at HLA-B positions 67, 70, and 97 that mediate HIV-1 peptide binding; and the deletion polymorphism rs67384697 associated with high surface expression of HLA-C. We also found that the compound genotype KIR3DS1 plus HLA-B Bw4-80I, which respectively encode a natural killer cell activating receptor and its putative ligand, significantly increased psoriasis susceptibility. This compound genotype has also been associated with delay of progression to AIDS. Together, our results suggest that genetic variants that contribute to anti-viral immunity may predispose to the development of psoriasis.

Many questions about the genetic basis of complex traits remain unanswered. This is in part due to the low statistical power of traditional genetic mapping studies. We used a statistically powerful approach, extreme QTL mapping (X-QTL), to identify the genetic basis of resistance to 13 chemicals in all 6 pairwise crosses of four ecologically and genetically diverse yeast strains, and we detected a total of more than 800 loci. We found that the number of loci detected in each experiment was primarily a function of the trait (explaining 46% of the variance) rather than the cross (11%), suggesting that the level of genetic complexity is a consistent property of a trait across different genetic backgrounds. Further, we observed that most loci had trait-specific effects, although a small number of loci with effects in many conditions were identified. We used the patterns of resistance and susceptibility alleles in the four parent strains to make inferences about the allele frequency spectrum of functional variants. We also observed evidence of more complex allelic series at a number of loci, as well as strain-specific signatures of selection. These results improve our understanding of complex traits in yeast and have implications for study design in other organisms.

Using ~60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population.

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04-1 × 10(-17)). Except for total HDL particles (r = -0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07-0.17, P = 5 × 10(-5)-1 10(-19)). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE ± 0.22 mg/dl/allele, P = 8 × 10(-5), P(interaction) = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE ± 0.22 mg/dl/allele, P = 0.35) or metformin (β = -0.03, SEE ± 0.22 mg/dl/allele, P = 0.90; P(interaction) = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE ± 0.012 ln nmol/L/allele, P = 0.01, P(interaction) = 0.01) but not in the placebo (β = -0.002, SEE ± 0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE ± 0.008 nmol/L/allele, P = 0.12; P(interaction) = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss.

Most genes in bacteria are experimentally uncharacterized and cannot be annotated with a specific function. Given the great diversity of bacteria and the ease of genome sequencing, high-throughput approaches to identify gene function experimentally are needed. Here, we use pools of tagged transposon mutants in the metal-reducing bacterium Shewanella oneidensis MR-1 to probe the mutant fitness of 3,355 genes in 121 diverse conditions including different growth substrates, alternative electron acceptors, stresses, and motility. We find that 2,350 genes have a pattern of fitness that is significantly different from random and 1,230 of these genes (37% of our total assayed genes) have enough signal to show strong biological correlations. We find that genes in all functional categories have phenotypes, including hundreds of hypotheticals, and that potentially redundant genes (over 50% amino acid identity to another gene in the genome) are also likely to have distinct phenotypes. Using fitness patterns, we were able to propose specific molecular functions for 40 genes or operons that lacked specific annotations or had incomplete annotations. In one example, we demonstrate that the previously hypothetical gene SO_3749 encodes a functional acetylornithine deacetylase, thus filling a missing step in S. oneidensis metabolism. Additionally, we demonstrate that the orphan histidine kinase SO_2742 and orphan response regulator SO_2648 form a signal transduction pathway that activates expression of acetyl-CoA synthase and is required for S. oneidensis to grow on acetate as a carbon source. Lastly, we demonstrate that gene expression and mutant fitness are poorly correlated and that mutant fitness generates more confident predictions of gene function than does gene expression. The approach described here can be applied generally to create large-scale gene-phenotype maps for evidence-based annotation of gene function in prokaryotes.

Family studies suggest a genetic component to the etiology of chronic kidney disease (CKD) and end stage renal disease (ESRD). Previously, we identified 16 loci for eGFR in genome-wide association studies, but the associations of these single nucleotide polymorphisms (SNPs) for incident CKD or ESRD are unknown. We thus investigated the association of these loci with incident CKD in 26,308 individuals of European ancestry free of CKD at baseline drawn from eight population-based cohorts followed for a median of 7.2 years (including 2,122 incident CKD cases defined as eGFR

Chronic kidney disease (CKD) is an increasing global public health concern, particularly among populations of African ancestry. We performed an interrogation of known renal loci, genome-wide association (GWA), and IBC candidate-gene SNP association analyses in African Americans from the CARe Renal Consortium. In up to 8,110 participants, we performed meta-analyses of GWA and IBC array data for estimated glomerular filtration rate (eGFR), CKD (eGFR 30 mg/g) and interrogated the 250 kb flanking region around 24 SNPs previously identified in European Ancestry renal GWAS analyses. Findings were replicated in up to 4,358 African Americans. To assess function, individually identified genes were knocked down in zebrafish embryos by morpholino antisense oligonucleotides. Expression of kidney-specific genes was assessed by in situ hybridization, and glomerular filtration was evaluated by dextran clearance. Overall, 23 of 24 previously identified SNPs had direction-consistent associations with eGFR in African Americans, 2 of which achieved nominal significance (UMOD, PIP5K1B). Interrogation of the flanking regions uncovered 24 new index SNPs in African Americans, 12 of which were replicated (UMOD, ANXA9, GCKR, TFDP2, DAB2, VEGFA, ATXN2, GATM, SLC22A2, TMEM60, SLC6A13, and BCAS3). In addition, we identified 3 suggestive loci at DOK6 (p-value = 5.3×10(-7)) and FNDC1 (p-value = 3.0×10(-7)) for UACR, and KCNQ1 with eGFR (p = 3.6×10(-6)). Morpholino knockdown of kcnq1 in the zebrafish resulted in abnormal kidney development and filtration capacity. We identified several SNPs in association with eGFR in African Ancestry individuals, as well as 3 suggestive loci for UACR and eGFR. Functional genetic studies support a role for kcnq1 in glomerular development in zebrafish.

The Plasmodium falciparum parasite's ability to adapt to environmental pressures, such as the human immune system and antimalarial drugs, makes malaria an enduring burden to public health. Understanding the genetic basis of these adaptations is critical to intervening successfully against malaria. To that end, we created a high-density genotyping array that assays over 17,000 single nucleotide polymorphisms (∼ 1 SNP/kb), and applied it to 57 culture-adapted parasites from three continents. We characterized genome-wide genetic diversity within and between populations and identified numerous loci with signals of natural selection, suggesting their role in recent adaptation. In addition, we performed a genome-wide association study (GWAS), searching for loci correlated with resistance to thirteen antimalarials; we detected both known and novel resistance loci, including a new halofantrine resistance locus, PF10_0355. Through functional testing we demonstrated that PF10_0355 overexpression decreases sensitivity to halofantrine, mefloquine, and lumefantrine, but not to structurally unrelated antimalarials, and that increased gene copy number mediates resistance. Our GWAS and follow-on functional validation demonstrate the potential of genome-wide studies to elucidate functionally important loci in the malaria parasite genome.

In genetic association analysis of complex traits, permutation testing can be a valuable tool for assessing significance when the distribution of the test statistic is unknown or not well-approximated. This commonly arises, e.g, in tests of gene-set, pathway or genome-wide significance, or when the statistic is formed by machine learning or data adaptive methods. Existing applications include eQTL mapping, association testing with rare variants, inclusion of admixed individuals in genetic association analysis, and epistasis detection among many others. For genetic association testing in samples with population structure and/or relatedness, use of naive permutation can lead to inflated type 1 error. To address this in quantitative traits, the MVNpermute method was developed. However, for association mapping of a binary trait, the relationship between the mean and variance makes both naive permutation and the MVNpermute method invalid. We propose BRASS, a permutation method for binary traits, for use in association mapping in structured samples. In addition to modeling structure in the sample, BRASS allows for covariates, ascertainment and simultaneous testing of multiple markers, and it accommodates a wide range of test statistics. In simulation studies, we compare BRASS to other permutation and resampling-based methods in a range of scenarios that include population structure, familial relatedness, ascertainment and phenotype model misspecification. In these settings, we demonstrate the superior control of type 1 error by BRASS compared to the other 6 methods considered. We apply BRASS to assess genome-wide significance for association analyses in domestic dog for elbow dysplasia (ED) and idiopathic epilepsy (IE). For both traits we detect previously identified associations, and in addition, for ED, we detect significant association with a SNP on chromosome 35 that was not detected by previous analyses, demonstrating the potential of the method., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Mbatchou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Genome-wide association studies (GWAS) are commonly used to identify genomic variants that are associated with complex traits, and estimate the magnitude of this association for each variant. However, it has been widely observed that the association estimates of variants tend to be lower in a replication study than in the study that discovered those associations. A phenomenon known as Winner's Curse is responsible for this upward bias present in association estimates of significant variants in the discovery study. We review existing Winner's Curse correction methods which require only GWAS summary statistics in order to make adjustments. In addition, we propose modifications to improve existing methods and propose a novel approach which uses the parametric bootstrap. We evaluate and compare methods, first using a wide variety of simulated data sets and then, using real data sets for three different traits. The metric, estimated mean squared error (MSE) over significant SNPs, was primarily used for method assessment. Our results indicate that widely used conditional likelihood based methods tend to perform poorly. The other considered methods behave much more similarly, with our proposed bootstrap method demonstrating very competitive performance. To complement this review, we have developed an R package, 'winnerscurse' which can be used to implement these various Winner's Curse adjustment methods to GWAS summary statistics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Forde et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Although prostate cancer typically runs an indolent course, a subset of men develop aggressive, fatal forms of this disease. We hypothesize that germline variation modulates susceptibility to aggressive prostate cancer. The goal of this work is to identify susceptibility genes using the C57BL/6-Tg(TRAMP)8247Ng/J (TRAMP) mouse model of neuroendocrine prostate cancer. Quantitative trait locus (QTL) mapping was performed in transgene-positive (TRAMPxNOD/ShiLtJ) F2 intercross males (n = 228), which facilitated identification of 11 loci associated with aggressive disease development. Microarray data derived from 126 (TRAMPxNOD/ShiLtJ) F2 primary tumors were used to prioritize candidate genes within QTLs, with candidate genes deemed as being high priority when possessing both high levels of expression-trait correlation and a proximal expression QTL. This process enabled the identification of 35 aggressive prostate tumorigenesis candidate genes. The role of these genes in aggressive forms of human prostate cancer was investigated using two concurrent approaches. First, logistic regression analysis in two human prostate gene expression datasets revealed that expression levels of five genes (CXCL14, ITGAX, LPCAT2, RNASEH2A, and ZNF322) were positively correlated with aggressive prostate cancer and two genes (CCL19 and HIST1H1A) were protective for aggressive prostate cancer. Higher than average levels of expression of the five genes that were positively correlated with aggressive disease were consistently associated with patient outcome in both human prostate cancer tumor gene expression datasets. Second, three of these five genes (CXCL14, ITGAX, and LPCAT2) harbored polymorphisms associated with aggressive disease development in a human GWAS cohort consisting of 1,172 prostate cancer patients. This study is the first example of using a systems genetics approach to successfully identify novel susceptibility genes for aggressive prostate cancer. Such approaches will facilitate the identification of novel germline factors driving aggressive disease susceptibility and allow for new insights into these deadly forms of prostate cancer. [ABSTRACT FROM AUTHOR]

Associating genetic variation with quantitative measures of gene regulation offers a way to bridge the gap between genotype and complex phenotypes. In order to identify quantitative trait loci (QTLs) that influence the binding of a transcription factor in humans, we measured binding of the multifunctional transcription and chromatin factor CTCF in 51 HapMap cell lines. We identified thousands of QTLs in which genotype differences were associated with differences in CTCF binding strength, hundreds of them confirmed by directly observable allele-specific binding bias. The majority of QTLs were either within 1 kb of the CTCF binding motif, or in linkage disequilibrium with a variant within 1 kb of the motif. On the X chromosome we observed three classes of binding sites: a minority class bound only to the active copy of the X chromosome, the majority class bound to both the active and inactive X, and a small set of female-specific CTCF sites associated with two non-coding RNA genes. In sum, our data reveal extensive genetic effects on CTCF binding, both direct and indirect, and identify a diversity of patterns of CTCF binding on the X chromosome. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid (CSF) 42 amino acid species of amyloid beta (Aβ42) and tau levels are strongly correlated with the presence of Alzheimer's disease (AD) neuropathology including amyloid plaques and neurodegeneration and have been successfully used as endophenotypes for genetic studies of AD. Additional CSF analytes may also serve as useful endophenotypes that capture other aspects of AD pathophysiology. Here we have conducted a genome-wide association study of CSF levels of 59 AD-related analytes. All analytes were measured using the Rules Based Medicine Human DiscoveryMAP Panel, which includes analytes relevant to several disease-related processes. Data from two independently collected and measured datasets, the Knight Alzheimer's Disease Research Center (ADRC) and Alzheimer's Disease Neuroimaging Initiative (ADNI), were analyzed separately, and combined results were obtained using meta-analysis. We identified genetic associations with CSF levels of 5 proteins (Angiotensin-converting enzyme (ACE), Chemokine (C-C motif) ligand 2 (CCL2), Chemokine (C-C motif) ligand 4 (CCL4), Interleukin 6 receptor (IL6R) and Matrix metalloproteinase-3 (MMP3)) with study-wide significant p-values (p<1.46×10−10) and significant, consistent evidence for association in both the Knight ADRC and the ADNI samples. These proteins are involved in amyloid processing and pro-inflammatory signaling. SNPs associated with ACE, IL6R and MMP3 protein levels are located within the coding regions of the corresponding structural gene. The SNPs associated with CSF levels of CCL4 and CCL2 are located in known chemokine binding proteins. The genetic associations reported here are novel and suggest mechanisms for genetic control of CSF and plasma levels of these disease-related proteins. Significant SNPs in ACE and MMP3 also showed association with AD risk. Our findings suggest that these proteins/pathways may be valuable therapeutic targets for AD. Robust associations in cognitively normal individuals suggest that these SNPs also influence regulation of these proteins more generally and may therefore be relevant to other diseases. [ABSTRACT FROM AUTHOR]

Many of the type 2 diabetes loci identified through genome-wide association studies localize to non-protein-coding intronic and intergenic regions and likely contain variants that regulate gene transcription. The CDC123/CAMK1D type 2 diabetes association signal on chromosome 10 spans an intergenic region between CDC123 and CAMK1D and also overlaps the CDC123 3′UTR. To gain insight into the molecular mechanisms underlying the association signal, we used open chromatin, histone modifications and transcription factor ChIP-seq data sets from type 2 diabetes-relevant cell types to identify SNPs overlapping predicted regulatory regions. Two regions containing type 2 diabetes-associated variants were tested for enhancer activity using luciferase reporter assays. One SNP, rs11257655, displayed allelic differences in transcriptional enhancer activity in 832/13 and MIN6 insulinoma cells as well as in human HepG2 hepatocellular carcinoma cells. The rs11257655 risk allele T showed greater transcriptional activity than the non-risk allele C in all cell types tested. Using electromobility shift and supershift assays we demonstrated that the rs11257655 risk allele showed allele-specific binding to FOXA1 and FOXA2. We validated FOXA1 and FOXA2 enrichment at the rs11257655 risk allele using allele-specific ChIP in human islets. These results suggest that rs11257655 affects transcriptional activity through altered binding of a protein complex that includes FOXA1 and FOXA2, providing a potential molecular mechanism at this GWAS locus. [ABSTRACT FROM AUTHOR]

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p<5×10−8) including splice variants in LPA that lowered plasma lipoprotein(a) levels (P = 1.5×10−117). Through accessing the national medical records of these participants, we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease (OR = 0.84, P = 3×10−4), demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases. More generally, this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers. [ABSTRACT FROM AUTHOR]

We carried out an admixture analysis of a sample comprising 1,019 individuals from all the provinces of Cuba. We used a panel of 128 autosomal Ancestry Informative Markers (AIMs) to estimate the admixture proportions. We also characterized a number of haplogroup diagnostic markers in the mtDNA and Y-chromosome in order to evaluate admixture using uniparental markers. Finally, we analyzed the association of 16 single nucleotide polymorphisms (SNPs) with quantitative estimates of skin pigmentation. In the total sample, the average European, African and Native American contributions as estimated from autosomal AIMs were 72%, 20% and 8%, respectively. The Eastern provinces of Cuba showed relatively higher African and Native American contributions than the Western provinces. In particular, the highest proportion of African ancestry was observed in the provinces of Guantánamo (40%) and Santiago de Cuba (39%), and the highest proportion of Native American ancestry in Granma (15%), Holguín (12%) and Las Tunas (12%). We found evidence of substantial population stratification in the current Cuban population, emphasizing the need to control for the effects of population stratification in association studies including individuals from Cuba. The results of the analyses of uniparental markers were concordant with those observed in the autosomes. These geographic patterns in admixture proportions are fully consistent with historical and archaeological information. Additionally, we identified a sex-biased pattern in the process of gene flow, with a substantially higher European contribution from the paternal side, and higher Native American and African contributions from the maternal side. This sex-biased contribution was particularly evident for Native American ancestry. Finally, we observed that SNPs located in the genes SLC24A5 and SLC45A2 are strongly associated with melanin levels in the sample. [ABSTRACT FROM AUTHOR]

Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequency, and effect sizes of mutations that contribute risk to complex traits. Because researchers are performing exome sequencing studies aimed at uncovering the role of low-frequency variants in the risk of complex traits, this topic is of critical importance. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant associations with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant associations detected. These findings suggest recent population history may be an important factor influencing the power of association tests and in accounting for the missing heritability of certain complex traits. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) for type 1 diabetes (T1D) have successfully identified more than 40 independent T1D associated tagging single nucleotide polymorphisms (SNPs). However, owing to technical limitations of copy number variants (CNVs) genotyping assays, the assessment of the role of CNVs has been limited to the subset of these in high linkage disequilibrium with tag SNPs. The contribution of untagged CNVs, often multi-allelic and difficult to genotype using existing assays, to the heritability of T1D remains an open question. To investigate this issue, we designed a custom comparative genetic hybridization array (aCGH) specifically designed to assay untagged CNV loci identified from a variety of sources. To overcome the technical limitations of the case control design for this class of CNVs, we genotyped the Type 1 Diabetes Genetics Consortium (T1DGC) family resource (representing 3,903 transmissions from parents to affected offspring) and used an association testing strategy that does not necessitate obtaining discrete genotypes. Our design targeted 4,309 CNVs, of which 3,410 passed stringent quality control filters. As a positive control, the scan confirmed the known T1D association at the INS locus by direct typing of the 5′ variable number of tandem repeat (VNTR) locus. Our results clarify the fact that the disease association is indistinguishable from the two main polymorphic allele classes of the INS VNTR, class I-and class III. We also identified novel technical artifacts resulting into spurious associations at the somatically rearranging loci, T cell receptor, TCRA/TCRD and TCRB, and Immunoglobulin heavy chain, IGH, loci on chromosomes 14q11.2, 7q34 and 14q32.33, respectively. However, our data did not identify novel T1D loci. Our results do not support a major role of untagged CNVs in T1D heritability. [ABSTRACT FROM AUTHOR]

Migraine can be sub-classified not only according to presence of migraine aura (MA) or absence of migraine aura (MO), but also by additional features accompanying migraine attacks, e.g. photophobia, phonophobia, nausea, etc. all of which are formally recognized by the International Classification of Headache Disorders. It remains unclear how aura status and the other migraine features may be related to underlying migraine pathophysiology. Recent genome-wide association studies (GWAS) have identified 12 independent loci at which single nucleotide polymorphisms (SNPs) are associated with migraine. Using a likelihood framework, we explored the selective association of these SNPs with migraine, sub-classified according to aura status and the other features in a large population-based cohort of women including 3,003 active migraineurs and 18,108 free of migraine. Five loci met stringent significance for association with migraine, among which four were selective for sub-classified migraine, including rs11172113 (LRP1) for MO. The number of loci associated with migraine increased to 11 at suggestive significance thresholds, including five additional selective associations for MO but none for MA. No two SNPs showed similar patterns of selective association with migraine characteristics. At one extreme, SNPs rs6790925 (near TGFBR2) and rs2274316 (MEF2D) were not associated with migraine overall, MA, or MO but were selective for migraine sub-classified by the presence of one or more of the additional migraine features. In contrast, SNP rs7577262 (TRPM8) was associated with migraine overall and showed little or no selectivity for any of the migraine characteristics. The results emphasize the multivalent nature of migraine pathophysiology and suggest that a complete understanding of the genetic influence on migraine may benefit from analyses that stratify migraine according to both aura status and the additional diagnostic features used for clinical characterization of migraine. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a leading cause of global morbidity and mortality and, whilst smoking remains the single most important risk factor, COPD risk is heritable. Of 26 independent genomic regions showing association with lung function in genome-wide association studies, eleven have been reported to show association with airflow obstruction. Although the main risk factor for COPD is smoking, some individuals are observed to have a high forced expired volume in 1 second (FEV1) despite many years of heavy smoking. We hypothesised that these “resistant smokers” may harbour variants which protect against lung function decline caused by smoking and provide insight into the genetic determinants of lung health. We undertook whole exome re-sequencing of 100 heavy smokers who had healthy lung function given their age, sex, height and smoking history and applied three complementary approaches to explore the genetic architecture of smoking resistance. Firstly, we identified novel functional variants in the “resistant smokers” and looked for enrichment of these novel variants within biological pathways. Secondly, we undertook association testing of all exonic variants individually with two independent control sets. Thirdly, we undertook gene-based association testing of all exonic variants. Our strongest signal of association with smoking resistance for a non-synonymous SNP was for rs10859974 (P = 2.34×10−4) in CCDC38, a gene which has previously been reported to show association with FEV1/FVC, and we demonstrate moderate expression of CCDC38 in bronchial epithelial cells. We identified an enrichment of novel putatively functional variants in genes related to cilia structure and function in resistant smokers. Ciliary function abnormalities are known to be associated with both smoking and reduced mucociliary clearance in patients with COPD. We suggest that genetic influences on the development or function of cilia in the bronchial epithelium may affect growth of cilia or the extent of damage caused by tobacco smoke. [ABSTRACT FROM AUTHOR]

Many existing cohorts contain a range of relatedness between genotyped individuals, either by design or by chance. Haplotype estimation in such cohorts is a central step in many downstream analyses. Using genotypes from six cohorts from isolated populations and two cohorts from non-isolated populations, we have investigated the performance of different phasing methods designed for nominally ‘unrelated’ individuals. We find that SHAPEIT2 produces much lower switch error rates in all cohorts compared to other methods, including those designed specifically for isolated populations. In particular, when large amounts of IBD sharing is present, SHAPEIT2 infers close to perfect haplotypes. Based on these results we have developed a general strategy for phasing cohorts with any level of implicit or explicit relatedness between individuals. First SHAPEIT2 is run ignoring all explicit family information. We then apply a novel HMM method (duoHMM) to combine the SHAPEIT2 haplotypes with any family information to infer the inheritance pattern of each meiosis at all sites across each chromosome. This allows the correction of switch errors, detection of recombination events and genotyping errors. We show that the method detects numbers of recombination events that align very well with expectations based on genetic maps, and that it infers far fewer spurious recombination events than Merlin. The method can also detect genotyping errors and infer recombination events in otherwise uninformative families, such as trios and duos. The detected recombination events can be used in association scans for recombination phenotypes. The method provides a simple and unified approach to haplotype estimation, that will be of interest to researchers in the fields of human, animal and plant genetics. [ABSTRACT FROM AUTHOR]

Bipolar disorder is a common, heritable mental illness characterized by recurrent episodes of mania and depression. Despite considerable effort to elucidate the genetic underpinnings of bipolar disorder, causative genetic risk factors remain elusive. We conducted a comprehensive genomic analysis of bipolar disorder in a large Old Order Amish pedigree. Microsatellite genotypes and high-density SNP-array genotypes of 388 family members were combined with whole genome sequence data for 50 of these subjects, comprising 18 parent-child trios. This study design permitted evaluation of candidate variants within the context of haplotype structure by resolving the phase in sequenced parent-child trios and by imputation of variants into multiple unsequenced siblings. Non-parametric and parametric linkage analysis of the entire pedigree as well as on smaller clusters of families identified several nominally significant linkage peaks, each of which included dozens of predicted deleterious variants. Close inspection of exonic and regulatory variants in genes under the linkage peaks using family-based association tests revealed additional credible candidate genes for functional studies and further replication in population-based cohorts. However, despite the in-depth genomic characterization of this unique, large and multigenerational pedigree from a genetic isolate, there was no convergence of evidence implicating a particular set of risk loci or common pathways. The striking haplotype and locus heterogeneity we observed has profound implications for the design of studies of bipolar and other related disorders. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have revolutionized the field of cancer genetics, but the causal links between increased genetic risk and onset/progression of disease processes remain to be identified. Here we report the first step in such an endeavor for prostate cancer. We provide a comprehensive annotation of the 77 known risk loci, based upon highly correlated variants in biologically relevant chromatin annotations— we identified 727 such potentially functional SNPs. We also provide a detailed account of possible protein disruption, microRNA target sequence disruption and regulatory response element disruption of all correlated SNPs at . 88% of the 727 SNPs fall within putative enhancers, and many alter critical residues in the response elements of transcription factors known to be involved in prostate biology. We define as risk enhancers those regions with enhancer chromatin biofeatures in prostate-derived cell lines with prostate-cancer correlated SNPs. To aid the identification of these enhancers, we performed genomewide ChIP-seq for H3K27-acetylation, a mark of actively engaged enhancers, as well as the transcription factor TCF7L2. We analyzed in depth three variants in risk enhancers, two of which show significantly altered androgen sensitivity in LNCaP cells. This includes rs4907792, that is in linkage disequilibrium () with an eQTL for NUDT11 (on the X chromosome) in prostate tissue, and rs10486567, the index SNP in intron 3 of the JAZF1 gene on chromosome 7. Rs4907792 is within a critical residue of a strong consensus androgen response element that is interrupted in the protective allele, resulting in a 56% decrease in its androgen sensitivity, whereas rs10486567 affects both NKX3-1 and FOXA-AR motifs where the risk allele results in a 39% increase in basal activity and a 28% fold-increase in androgen stimulated enhancer activity. Identification of such enhancer variants and their potential target genes represents a preliminary step in connecting risk to disease process. [ABSTRACT FROM AUTHOR]

Mitochondrial dysfunction has been implicated in the pathogenesis of biliary atresia (BA). This study aimed to determine whether a specific mitochondrial DNA haplogroup is implicated in the pathogenesis and prognosis of BA. We determined 40 mitochondrial single nucleotide polymorphisms in 15 major mitochondrial haplogroups by the use of 24-plex PCR and fluorescent beads combined with sequence-specific oligonucleotide probes in 71 patients with BA and in 200 controls in the Taiwanese population of ethnic Chinese background. The haplogroup B4 and E prevalence were significantly lower and higher respectively, in the patients with BA than in the controls (odds ratios, 0.82 [p = 0.007] and 7.36 [p = 0.032] respectively) in multivariate logistic-regression analysis. The 3-year survival rate with native liver was significantly lower in haplogroup E than the other haplogroups (P = 0.037). A cytoplasmic hybrid (cybrid) was obtained from human 143B osteosarcoma cells devoid of mtDNA (ρ0 cell) and was fused with specific mtDNA bearing E and B4 haplogroups donated by healthy Taiwanese subjects. Chenodeoxycholic acid treatment resulted in significantly lower free radical production, higher mitochondrial membrane potential, more viable cells, and fewer apoptotic cybrid B4 cells than parental 143B and cybrid E cells. Bile acid treatment resulted in a significantly greater protective mitochondrial reaction with significantly higher mitochondrial DNA copy number and mitofusin 1 and 2 concentrations in cybrid B4 and parental cells than in cybrid E cells. The results of the study suggested that the specific mitochondrial DNA haplogroups B4 and E were not only associated with lower and higher prevalence of BA respectively, in the study population, but also with differential susceptibility to hydrophobic bile acid in the cybrid harboring different haplogroups. [ABSTRACT FROM AUTHOR]

Multiple rare variants either within or across genes have been hypothesised to collectively influence complex human traits. The increasing availability of high throughput sequencing technologies offers the opportunity to study the effect of rare variants on these traits. However, appropriate and computationally efficient analytical methods are required to account for collections of rare variants that display a combination of protective, deleterious and null effects on the trait. We have developed a novel method for the analysis of rare genetic variation in a gene, region or pathway that, by simply aggregating summary statistics at each variant, can: (i) test for the presence of a mixture of effects on a trait; (ii) be applied to both binary and quantitative traits in population-based and family-based data; (iii) adjust for covariates to allow for non-genetic risk factors and; (iv) incorporate imputed genetic variation. In addition, for preliminary identification of promising genes, the method can be applied to association summary statistics, available from meta-analysis of published data, for example, without the need for individual level genotype data. Through simulation, we show that our method is immune to the presence of bi-directional effects, with no apparent loss in power across a range of different mixtures, and can achieve greater power than existing approaches as long as summary statistics at each variant are robust. We apply our method to investigate association of type-1 diabetes with imputed rare variants within genes in the major histocompatibility complex using genotype data from the Wellcome Trust Case Control Consortium. [ABSTRACT FROM AUTHOR]

The primary constituents of plaques (Aβ42/Aβ40) and neurofibrillary tangles (tau and phosphorylated forms of tau [ptau]) are the current leading diagnostic and prognostic cerebrospinal fluid (CSF) biomarkers for AD. In this study, we performed deep sequencing of APP, PSEN1, PSEN2, GRN, APOE and MAPT genes in individuals with extreme CSF Aβ42, tau, or ptau levels. One known pathogenic mutation (PSEN1 p.A426P), four high-risk variants for AD (APOE p.L46P, MAPT p.A152T, PSEN2 p.R62H and p.R71W) and nine novel variants were identified. Surprisingly, a coding variant in PSEN1, p.E318G (rs17125721-G) exhibited a significant association with high CSF tau (p = 9.2×10−4) and ptau (p = 1.8×10−3) levels. The association of the p.E318G variant with Aβ deposition was observed in APOE-ε4 allele carriers. Furthermore, we found that in a large case-control series (n = 5,161) individuals who are APOE-ε4 carriers and carry the p.E318G variant are at a risk of developing AD (OR = 10.7, 95% CI = 4.7–24.6) that is similar to APOE-ε4 homozygous (OR = 9.9, 95% CI = 7.2.9–13.6), and double the risk for APOE-ε4 carriers that do not carry p.E318G (OR = 3.9, 95% CI = 3.4–4.4). The p.E318G variant is present in 5.3% (n = 30) of the families from a large clinical series of LOAD families (n = 565) and exhibited a higher frequency in familial LOAD (MAF = 2.5%) than in sporadic LOAD (MAF = 1.6%) (p = 0.02). Additionally, we found that in the presence of at least one APOE-ε4 allele, p.E318G is associated with more Aβ plaques and faster cognitive decline. We demonstrate that the effect of PSEN1, p.E318G on AD susceptibility is largely dependent on an interaction with APOE-ε4 and mediated by an increased burden of Aβ deposition. [ABSTRACT FROM AUTHOR]

Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website. [ABSTRACT FROM AUTHOR]

Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age2, sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal. [ABSTRACT FROM AUTHOR]

Leprosy is a chronic infectious disease caused by Mycobacterium Leprae, where the host genetic background plays an important role toward the disease pathogenesis. Various studies have identified a number of human genes in association with leprosy or its clinical forms. However, non-replication of results has hinted at the heterogeneity among associations between different population groups, which could be due to differently evolved LD structures and differential frequencies of SNPs within the studied regions of the genome. A need for systematic and saturated mapping of the associated regions with the disease is warranted to unravel the observed heterogeneity in different populations. Mapping of the PARK2 and PACRG gene regulatory region with 96 SNPs, with a resolution of 1 SNP per 1 Kb for PARK2 gene regulatory region in a North Indian population, showed an involvement of 11 SNPs in determining the susceptibility towards leprosy. The association was replicated in a geographically distinct and unrelated population from Orissa in eastern India. In vitro reporter assays revealed that the two significantly associated SNPs, located 63.8 kb upstream of PARK2 gene and represented in a single BIN of 8 SNPs, influenced the gene expression. A comparison of BINs between Indian and Vietnamese populations revealed differences in the BIN structures, explaining the heterogeneity and also the reason for non-replication of the associated genomic region in different populations. [ABSTRACT FROM AUTHOR]