Aims/hypothesisIdentifying rare coding variants associated with albuminuria may open new avenues for preventing chronic kidney disease and end-stage renal disease, which are highly prevalent in individuals with diabetes. Efforts to identify genetic susceptibility variants for albuminuria have so far been limited, with the majority of studies focusing on common variants.MethodsWe performed an exome-wide association study to identify coding variants in a two-stage (discovery and replication) approach. Data from 33,985 individuals of European ancestry (15,872 with and 18,113 without diabetes) and 2605 Greenlanders were included.ResultsWe identified a rare (minor allele frequency [MAF]: 0.8%) missense (A1690V) variant in CUBN (rs141640975, =0.27, p=1.3x10(-11)) associated with albuminuria as a continuous measure in the combined European meta-analysis. The presence of each rare allele of the variant was associated with a 6.4% increase in albuminuria. The rare CUBN variant had an effect that was three times stronger in individuals with type 2 diabetes compared with those without (p(interaction)=7.0x10(-4), with diabetes=0.69, without diabetes=0.20) in the discovery meta-analysis. Gene-aggregate tests based on rare and common variants identified three additional genes associated with albuminuria (HES1, CDC73 and GRM5) after multiple testing correction (p(Bonferroni)

It is challenging to identify causal genes and pathways explaining the associations with diseases and traits found by genome-wide association studies (GWASs). To solve this problem, a variety of methods that prioritize genes based on the variants identified by GWASs have been developed. In this thesis, the methods Data-driven Expression Prioritized Integration for Complex Traits (DEPICT) and Multi-marker Analysis of GenoMic Annotation (MAGMA) are used to prioritize causal genes based on the most recently published publicly available schizophrenia GWAS summary statistics. The two methods are compared using the Benchmarker framework, which allows an unbiased comparison of gene prioritization methods. The study has four aims. Firstly, to explain what are the differences between the gene prioritization methods DEPICT and MAGMA and how the two methods work. Secondly, to explain how the Benchmarker framework can be used to compare gene prioritization methods in an unbiased way. Thirdly, to compare the performance of DEPICT and MAGMA in prioritizing genes based on the latest schizophrenia summary statistics from 2018 using the Benchmarker framework. Lastly, to compare the performance of DEPICT and MAGMA on a schizophrenia GWAS with a smaller sample size by using Benchmarker. Firstly, the published results of the Benchmarker analyses using schizophrenia GWAS from 2014 were replicated to make sure that the framework is run correctly. The results were very similar and both the original and the replicated results show that DEPICT and MAGMA do not perform significantly differently. Furthermore, they show that the intersection of genes prioritized by DEPICT and MAGMA outperforms the outersection, which is defined as genes prioritized by only one of these methods. Secondly, Benchmarker was used to compare the performance of DEPICT and MAGMA on prioritizing genes using the schizophrenia GWAS from 2018. The results of the Benchmarker analyses suggest that DEPICT and MAGMA perform similarly with the GWAS from 2018 compared to the GWAS from 2014. Furthermore, an earlier schizophrenia GWAS from 2011 was used to check if the performance of DEPICT and MAGMA differs when a GWAS with lower statistical power is used. The results of the Benchmarker analyses make clear that MAGMA performs better than DEPICT in prioritizing genes using this smaller data set. Furthermore, for the schizophrenia GWAS from 2011 the outersection of genes prioritized by DEPICT and MAGMA outperforms the intersection. To conclude, the Benchmarker framework is a useful tool for comparing gene prioritization methods in an unbiased way. For the most recently published schizophrenia GWAS from 2018 there is no significant difference between the performance of DEPICT and MAGMA in prioritizing genes according to Benchmarker. For the smaller schizophrenia GWAS from 2011, however, MAGMA outperformed DEPICT.

Abstract Background Hip dysplasia and osteoarthritis continue to be prevalent problems in veterinary and human medicine. Canine hip dysplasia is particularly problematic as it massively affects several large-sized breeds and can cause a severe impairment of the quality of life. In Finland, the complex condition is categorized to five classes from normal to severe dysplasia, but the categorization includes several sub-traits: congruity of the joint, Norberg angle, subluxation degree of the joint, shape and depth of the acetabulum, and osteoarthritis. Hip dysplasia and osteoarthritis have been proposed to have separate genetic etiologies. Results Using Fédération Cynologique Internationale -standardized ventrodorsal radiographs, German shepherds were rigorously phenotyped for osteoarthritis, and for joint incongruity by Norberg angle and femoral head center position in relation to dorsal acetabular edge. The affected dogs were categorized into mild, moderate and severe dysplastic phenotypes using official hip scores. Three different genome-wide significant loci were uncovered. The strongest candidate genes for hip joint incongruity were noggin (NOG), a bone and joint developmental gene on chromosome 9, and nanos C2HC-type zinc finger 1 (NANOS1), a regulator of matrix metalloproteinase 14 (MMP14) on chromosome 28. Osteoarthritis mapped to a long intergenic region on chromosome 1, between genes encoding for NADPH oxidase 3 (NOX3), an intriguing candidate for articular cartilage degradation, and AT-rich interactive domain 1B (ARID1B) that has been previously linked to joint laxity. Conclusions Our findings highlight the complexity of canine hip dysplasia phenotypes. In particular, the results of this study point to the potential involvement of specific and partially distinct loci and genes or pathways in the development of incongruity, mild dysplasia, moderate-to-severe dysplasia and osteoarthritis of canine hip joints. Further studies should unravel the unique and common mechanisms for the various sub-traits.

Excess Ni intake has harmful implications on human health, which include chronic bronchitis, reduced lung function, and cancer of lung and nasal sinuses. Like other toxic metals, higher Ni accumulation in grains leads to excess intake by humans when the contaminated grains are consumed as food. There is little information about the genetic factors that regulate Ni uptake in plants. To investigate genetic architecture of Ni uptake in leaf and translocation to grain, we performed a genome-wide association study with genotyping from 90 K array in a historical bread wheat diversity panel from Pakistan. We observed that Ni toxicity caused more than 50 % reductions in biological yield and grain yield, other agronomic traits were also partly or severely affected. Genetic association study helped identify 23 SNP-trait associations involved in Ni uptake in leaf and translocation to grains. These 23 SNPs covered 15 genomic loci at chromosomes 1A, 2D, 3B, 4A and 4B of wheat. The favorable alleles of these SNPs were randomly distributed in subpopulations indicating no selection pressure for this trait during breeding improvement. These regions had 283 low-confidence and 248 high-confidence protein coding genes. Among these, 156 were annotated using databases of wheat and closely related grass species. Since there is no previous report on genetic information of Ni uptake and translocation, these results provide sufficient grounds for further research of candidate genes and varietal development.

Aims Diabetes is a known risk factor for coronary artery disease (CAD). There is accumulating evidence that CAD pathogenesis differs for individuals with type 1 diabetes (T1D). However, the genetic background has not been extensively studied. We aimed to discover genetic loci increasing CAD susceptibility, especially in T1D, to examine the function of these discoveries and to study the role of the known risk loci in T1D. Methods and results We performed the largest genome-wide association study to date for CAD in T1D, comprising 4869 individuals with T1D (cases/controls: 941/3928). Two loci reached genome-wide significance, rs1970112 in CDKN2B-AS1 [odds ratio (OR) =1.32, P = 1.50 x 10(-8)], and rs6055069 on DEFB127 promoter (OR= 4.17, P= 2.35 x 10(-9)), with consistent results in survival analysis. The CDKN2B-AS1 variant replicated (P = 0.04) when adjusted for diabetic kidney disease in three additional T1D cohorts (cases/controls: 434/3123). Furthermore, we explored the function of the lead discoveries with a cardio-phenome-wide analysis. Among the eight suggestive loci (P <1 x 10(-6)), rs70962766 near B3GNT2 associated with central blood pressure, rs1344228 near CNTNAP5 with intima media thickness, and rs2112481 on GRAMD2B promoter with serum leucocyte concentration. Finally, we calculated genetic risk scores for individuals with T1D with the known susceptibility loci. General population risk variants were modestly but significantly associated with CAD also in T1D (P=4.21 x 10(-7)). Conclusion While general population CAD risk loci had limited effect on the risk in T1D, for the first time, variants at the CDKN2B-AS1 locus were robustly associated with CAD in individuals with T1D. The novel finding on beta-defensin DEFB127 promoter provides a link between diabetes, infection susceptibility, and CAD, although pending on future confirmation. [GRAPHICS] .

Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Purpose of Review Diabetic complications affecting the kidneys, retina, nerves, and the cardiovasculature are the major causes of morbidity and mortality in diabetes. This paper aims to review the current understanding of the genetic basis of these complications, based on recent findings especially from genome-wide association studies. Recent Findings Variants in or near AFF3, RGMA-MCTP2, SP3-CDCA7, GLRA3, CNKSR3, and UMOD have reached genome-wide significance (p value <5 x 10(-8)) for association with diabetic kidney disease, and recently, GRB2 was reported to be associated at genome-wide significance with diabetic retinopathy. While some loci affecting cardiovascular disease in the general population have been replicated in diabetes, GLUL affects the risk of cardiovascular disease specifically in diabetic subjects. Summary Genetic findings are emerging for diabetic complications, although the studies remain relatively small compared to those for type 1 and type 2 diabetes. In addition to pinpointing specific loci, the studies also reveal biological information on correlated traits and pathways.