The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

The genetic architecture of type 2 diabetes

A little over 5 years ago, the U.S. National Institutes of Health (NIH) announced the intention to develop policies to require applicants to report plans to balance male and female cells and animals in preclinical investigations. Soon thereafter, the NIH issued a request for information from the scientific community and consulted with various stakeholders. The feedback received was considered during development of policy requiring the consideration of sex as a biological variable (SABV) in NIH-funded research on vertebrate animals and humans, which went into effect for applications due on or after January 25, 2016. We identified NIH programs related to SABV and reviewed SABV-relevant scientific literature. We find that the application of SABV throughout the research process can serve as a guiding principle to improve the value of biomedical science. The NIH is engaged in ongoing efforts to develop resources to help investigators consider SABV in their research. We also provide an update on lessons learned, highlight ways that different disciplines consider SABV, and describe the opportunities for scientific discovery that applying SABV offers. We call on NIH's various stakeholders to redouble their efforts to integrate SABV throughout the biomedical research enterprise. Sex- and gender-aware investigations are critical to the conduct of rigorous and transparent science and the advancement of personalized medicine. This kind of research achieves its greatest potential when sex and gender considerations are integrated into the biomedical research enterprise in an end-to-end manner, from basic and preclinical investigations, through translational and clinical research, to improved health care delivery.

Sex as a Biological Variable: A 5-Year Progress Report and Call to Action

The International Mouse Phenotyping Consortium (IMPC) is building a catalogue of mammalian gene function by producing and phenotyping a knockout mouse line for every protein-coding gene. To date, the IMPC has generated and characterised 5186 mutant lines. One-third of the lines have been found to be non-viable and over 300 new mouse models of human disease have been identified thus far. While current bioinformatics efforts are focused on translating results to better understand human disease processes, IMPC data also aids understanding genetic function and processes in other species. Here we show, using gorilla genomic data, how genes essential to development in mice can be used to help assess the potentially deleterious impact of gene variants in other species. This type of analyses could be used to select optimal breeders in endangered species to maintain or increase fitness and avoid variants associated to impaired-health phenotypes or loss-of-function mutations in genes of critical importance. We also show, using selected examples from various mammal species, how IMPC data can aid in the identification of candidate genes for studying a condition of interest, deliver information about the mechanisms involved, or support predictions for the function of genes that may play a role in adaptation. With genotyping costs decreasing and the continued improvements of bioinformatics tools, the analyses we demonstrate can be routinely applied.

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The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation.

We are entering a new era of mouse phenomics, driven by large-scale and economical generation of mouse mutants coupled with increasingly sophisticated and comprehensive phenotyping. These studies are generating large, multidimensional gene–phenotype data sets, which are shedding new light on the mammalian genome landscape and revealing many hitherto unknown features of mammalian gene function. Moreover, these phenome resources provide a wealth of disease models and can be integrated with human genomics data as a powerful approach for the interpretation of human genetic variation and its relationship to disease. In the future, the development of novel phenotyping platforms allied to improved computational approaches, including machine learning, for the analysis of phenotype data will continue to enhance our ability to develop a comprehensive and powerful model of mammalian gene–phenotype space. Although the field of functional genomics is increasingly adopting genome-scale approaches, a comprehensive understanding of gene functions requires the parallel development of deep phenotyping platforms. This Review discusses strategies for broad-based mouse phenomics, applied both to gene knockout collections and to diverse strains harbouring natural genetic variation. The authors discuss technical challenges, analysis pipelines and insights into human disease genetics.

High-throughput mouse phenomics for characterizing mammalian gene function

Blood pressure (BP) is a heritable trait1 influenced by multiple biological pathways and is responsive to environmental stimuli. Over one billion people worldwide have hypertension (BP ≥140 mm Hg systolic [SBP] or ≥90 mm Hg diastolic [DBP])2. Even small increments in BP are associated with increased risk of cardiovascular events3. This genome-wide association study of SBP and DBP, which used a multi-stage design in 200,000 individuals of European descent, identified 16 novel loci: six of these loci contain genes previously known or suspected to regulate BP (GUCY1A3-GUCY1B3; NPR3-C5orf23; ADM; FURIN-FES; GOSR2; GNAS-EDN3); the other 10 provide new clues to BP physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke, and coronary artery disease, but not kidney disease or kidney function. We also observed associations with BP in East Asian, South Asian, and African ancestry individuals. Our findings provide new insights into the genetics and biology of BP, and suggest novel potential therapeutic pathways for cardiovascular disease prevention. Genetic approaches have advanced the understanding of biological pathways underlying inter-individual variation in BP. For example, studies of rare Mendelian BP disorders have identified multiple defects in renal sodium handling pathways4. More recently two genomewide association studies (GWAS), each of >25,000 individuals of European-ancestry, identified 13 loci associated with SBP, DBP, and hypertension5,6. We now report results of a new meta-analysis of GWAS data that includes staged follow-up genotyping to identify additional BP loci. Primary analyses evaluated associations between 2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and SBP and DBP in 69,395 individuals of European ancestry from 29 studies (Supplementary Materials Sections 1–3, Supplementary Tables 1– 2). Following GWAS meta-analysis, we conducted a three-stage validation experiment that made efficient use of available genotyping resources, to follow up top signals in up to 133,661 additional individuals of European descent (Supplementary Fig. 1 and Supplementary Materials Section 4). Twenty-nine independent SNPs at 28 loci were significantly associated with SBP, DBP, or both in the meta-analysis combining discovery and follow up data (Fig. 1, Table 1, Supplementary Figs 2–3, Supplementary Tables 3–5). All 29 SNPs attained association P <5×10−9, an order of magnitude beyond the standard genome-wide significance level for a single stage experiment (Table 1). Sixteen of these 29 associations were novel (Table 1). Two associations were near the FURIN and GOSR2 genes; prior targeted analyses of variants in these genes suggested they Note added in proof: Since this manuscript was submitted, Kato et al published a BP GWAS in East Asians that identified a SNP highly correlated to the SNP we report at the NPR3-c5orf23 locus28. Author contributions Full author contributions and roles are listed in the Supplementary Materials Section 19. NIH Public Access Author Manuscript Nature. Author manuscript; available in PMC 2012 May 01. Published in final edited form as: Nature. ; 478(7367): 103–109. doi:10.1038/nature10405. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript may be BP loci7,8. At the CACNB2 locus we validated association for a previously reported6 SNP rs4373814 and detected a novel independent association for rs1813353 (pairwise r2 =0.015 in HapMap CEU). Of our 13 previously reported associations5,6, only the association at PLCD3 was not supported by the current results (Supplementary Table 4). Some of the associations are in or near genes involved in pathways known to influence BP (NPR3, GUCY1A3-GUCY1B3, ADM, GNAS-EDN3, NPPA-NPPB, and CYP17A1; Supplementary Fig. 4). Twenty-two of the 28 loci did not contain genes that were a priori strong biological candidates. As expected from prior BP GWAS results, the effects of the novel variants on SBP and DBP were small (Fig. 1 and Table 1). For all variants, the observed directions of effects were concordant for SBP, DBP, and hypertension (Fig. 1, Table 1, Supplementary Fig. 3). Among the genes at the genome-wide significant loci, only CYP17A1, previously implicated in Mendelian congenital adrenal hyperplasia and hypertension, is known to harbour rare variants that have large effects on BP9. We performed several analyses to identify potential causal alleles and mechanisms. First, we looked up the 29 genome-wide significant index SNPs and their close proxies (r2>0.8) among cis-acting expression SNP (eSNP) results from multiple tissues (Supplementary Materials Section 5). For 13/29 index SNPs, we found association between nearby eSNP variants and expression level of at least one gene transcript (10−4 > p > 10−51, Supplementary Table 6). In 5 cases, the index BP SNP and the best eSNP from a genomewide survey were identical, highlighting potential mediators of the SNP-BP associations. Second, because changes in protein sequence are strong a priori candidates to be functional, we sought non-synonymous coding SNPs that were in high LD (r2 >0.8) with the 29 index SNPs. We identified such SNPsat 8 loci (Table 1, Supplementary Materials Section 6, Supplementary Table 7). In addition we performed analyses testing for differences in genetic effect according to body mass index (BMI) or sex, and analyses of copy number variants, pathway enrichment, and metabolomic data, but we did not find any statistically significant results (Supplementary Materials Sections 7–9, Supplementary Tables 8–10). We evaluated whether the BP variants we identified in Europeans were associated with BP in individuals of East Asian (N=29,719), South Asian (N=23,977), and African (N=19,775) ancestries (Table 1, Supplementary Tables 11–13). We found significant associations in individuals of East Asian ancestry for SNPs at 9 loci and in individuals of South Asian ancestry for SNPs at 6 loci; some have been reported previously (Supplementary Tables 12 and 15). The lack of significant association for individual SNPs may reflect small sample sizes, differences in allele frequencies or LD patterns, imprecise imputation for some ancestries using existing reference samples, or a genuinely different underlying genetic architecture. Because of limited power to detect effects of individual variants in the smaller non-European samples, we created genetic risk scores for SBP and DBP incorporating all 29 BP variants weighted according to effect sizes observed in the European samples. In each non-European ancestry group, risk scores were strongly associated with SBP (P=1.1×10−40 in East Asian, P=2.9×10−13 in South Asian, P=9.8×10−4 in African ancestry individuals) and DBP (P=2.9×10−48, P=9.5×10−15, and P=5.3×10−5, respectively; Supplementary Table 13). We also created a genetic risk score to assess association of the variants in aggregate with hypertension and with clinical measures of hypertensive complications including left ventricular mass, left ventricular wall thickness, incident heart failure, incident and prevalent stroke, prevalent coronary artery disease (CAD), kidney disease, and measures of kidney function, using results from other GWAS consortia (Table 2, Supplementary Materials Sections 10–11, Supplementary Table 14). The risk score was weighted using the average of Page 2 Nature. Author manuscript; available in PMC 2012 May 01. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript SBP and DBP effects for the 29 SNPs. In an independent sample of 23,294 women10, an increase of 1 standard deviation in the genetic risk score was associated with a 21% increase in the odds of hypertension (95% CI 19%–28%; Table 2, Supplementary Table 14). Among individuals in the top decile of the risk score, the prevalence of hypertension was 29% compared with 16% in the bottom decile (odds ratio 2.09, 95% CI 1.86–2.36). Similar results were observed in an independent hypertension case-control sample (Table 2). In our study, individuals in the top compared to bottom quintiles of genetic risk score differed by 4.6 mm Hg SBP and 3.0 mm Hg DBP, differences that approach population-averaged BP treatment effects for a single antihypertensive agent11. Epidemiologic data have shown that differences in SBP and DBP of this magnitude, across the population range of BP, are associated with an increase in cardiovascular disease risk3. Consistent with this and in line with findings from randomized trials of BP-lowering medication in hypertensive patients12,13, the genetic risk score was positively associated with left ventricular wall thickness (P=6.0×10−6), occurrence of stroke (P=3.3×10−5) and CAD (P=8.1×10−29). The same genetic risk score was not, however, significantly associated with chronic kidney disease or measures of kidney function, even though these renal outcomes were available in a similar sample size as for the other outcomes (Table 2). The absence of association with kidney phenotypes could be explained by a weaker causal relation of BP with kidney phenotypes than with CAD and stroke. This finding is consistent with the mismatch between observational data that show a positive association of BP with kidney disease, and clinical trial data that show inconsistent evidence of benefit of BP lowering on kidney disease prevention in patients with hypertension14. Thus, several lines of evidence converge to suggest that BP elevation may in part be a consequence rather than a cause of sub-clinical kidney disease. Our discovery meta-analysis (Supplementary Fig. 2) suggests an excess of modestly significant (10−5 <P <10−2) associations likely arising from common BP variants of small effect. By dividing our principal GWAS dataset into non-overlapping discovery (N≈56,000) and validation (N≈14,000) subsets, we found robust evidence for the existence of such undetected common va

Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.

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Robert B. Braun

Papers

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.