Finngen提供了良好的孤立人群的遗传见解

　　为了基于我们的基于寄存器的表型并探索芬兰孤立设置的价值，我们在Finngen中选择了15种疾病，其中有1,000多个病例，并为其发表了众多的GWAS数据。我们通过将遗传相关性和效应大小与先前的GWAS结果进行比较（补充表6）来评估表型的准确性。遗传相关性均未显着低于1（最低遗传相关性为0.89（标准误差= 0.07），年龄相关的黄斑变性（AMD）；补充表6）。对于Finngen病例大量病例的疾病，在Finngen和先前发表的荟萃分析之间，已知基因座的铅变体的效应大小在很大程度上是一致的。该结果表明，我们的基于寄存器的表型与现有的特异性GWASS相当（图1E，补充信息和补充表6）。在某些疾病中，效果大小差异更大，而在FinnGen中含量较小的疾病（例如，强直性脊柱炎，n = 1462，R2 = 0.62）。 　　这15种疾病的GWA鉴定出235个基因座（即选择用于细映射的区域；方法）和275个独立的全基因组显着关联（此处，“关联”是一个独立的信号），是在人类白细胞抗原（HLA）区域（GRCH38，GRCH38，GRCH38，6：25-34 MB）之外的。以前已经报道了Finngen估算的经典HLA基因等位基因的全面关联研究（PHEWAS）8。总体而言，44个非HLA关联是由低频铅变体驱动的（我们将“低频”定义为 <5% in non-Finnish, Swedish or Estonian European (NFSEE) individuals in the Genome Aggregation Database (gnomAD; v.2.0.1)9) that were more than twice as frequent in Finnish individuals compared with NFSEE individuals. We use NFSEE as a general continental European reference point, excluding individuals from Finland, Sweden and Estonia. As there were large-scale migrations from Finland to Sweden in the twentieth century, many of the chromosomes from sequencing studies of Swedish individuals are of recent Finnish origin. Moreover, the geographically close and linguistically and genetically similar9 population of Estonia is likely to share elements of the same ancestral founder effect. 　　Replication of many such enriched variant associations in the Finnish population is hindered by low AFs or missingness in other European populations. People from Finland are genetically more similar to people from Estonia than other European countries9. Therefore we first conducted replication using data from 136,724 individuals from the Estonian Biobank (EstBB) and then extended the analysis to individuals from the UKBB (Methods and see Supplementary Table 7 for definitions of end points and case–control numbers). The effect sizes in genome-wide significant hits in FinnGen were mostly concordant with the EstBB (average inverse variance weighted slope of 1.5 (with FinnGen higher) and r2 = 0.69) and the UKBB (slope = 1.1, r2 = 0.84) (Extended Data Fig. 3). FinnGen had a higher case prevalence in the 15 disease diagnoses than in the UKBB, which is probably due to slightly different ascertainment schemes. By contrast, the EstBB had the highest case prevalence in ophthalmic diseases (AMD and glaucoma) and inflammatory skin conditions (atopic dermatitis and psoriasis) (Fig. 2a). 　　After a meta-analysis of the EstBB and UKBB data, 241 of the 275 associations remained genome-wide significant (Supplementary Table 8). We performed a further meta-analysis of 232 associations that did not meet the genome-wide significance threshold in FinnGen (5 × 10−8 < P < 1 × 10−6), and 57 of those were genome-wide significant after meta-analysis. This meta-analysis resulted in 298 genome-wide significant associations (see also Supplementary Table 8 for results after multiple testing correction for 15 end points). 　　To determine whether the observed associations have been previously reported, we queried the GWAS Catalog association database (and largest recent relevant GWAS) for genome-wide significant (P < 5 × 10−8) variants that are in linkage disequilibrium (LD) (r2 > 0.1 in the FinnGen imputation panel) with observed lead variants in FinnGen. As the lowest AF of the new findings was low (0.15%), in addition to published GWASs, we checked whether credible set variants in these loci have also been previously reported in ClinVar. We observed six known pathogenic or likely pathogenic variants, such as a frameshift variant in PALB2 (p.Leu531fs; AF of 0.1%, not observed outside Finland in gnomAD; Supplementary Table 8) associated with breast cancer. Thirty out of the 298 associations have not been previously reported in the largest published meta-analysis so far (Supplementary Table 6), in a manual literature search, the GWAS Catalog or in ClinVar (Table 1). As expected, we observed that lead variants in novel loci were mostly of low frequency and enriched in Finland compared with known loci from previous GWASs. Specifically, 27 lead variants had minor allele frequency (MAF) values of <5% in gnomAD NFSEE individuals, and 88% of novel and 11% of known loci (after LD pruning, see below) had gnomAD NFSEE MAF values of <5% (Fisher’s exact test, P = 4.29 × 10−17). In most cases, the AFs of lower frequency variants (MAF < 5% in gnomAD NFSEE population) were the highest in FinnGen followed by the EstBB and lowest in NFSEE individuals in gnomAD (Fig. 2d). 　　Next we performed statistical fine-mapping (Methods) on all 298 genome-wide significant associations (each association is independent; that is, 298 credible sets). Coding variants (missense, frameshift, canonical splice site, stop gained, stop lost or inframe deletion) with posterior inclusion probability (PIP) values of ≥0.05 were observed in 44 (18.7%) out of the 95% credible sets (17 coding variants had PIP > 0.5). Here onwards, we report coding variants with PIP >0.05作为预科因果。我们认识到，可能有时可能会在编码变体中分配因果变体（有关精细映射校准和可复制性的讨论），请参见我们的纸张10。除了确定推定的因果编码变体外，我们还试图通过将可信度集与从EQTL目录（方法）共定位定量性状基因座（EQTL）数据集来确定潜在的基因表达调节机制。 　　然后，我们想描述风险变体作用的AF谱和推定的机制。为此，我们LD修剪了298个基因组的显着关联，并优先考虑同一命中中最重要的表型，以代表单个推定的因果变体（LD R2值之间的LD R2值 <0.2). This process resulted in 281 previously unknown associations (27 new). 　　Most of the 281 previously unknown associations were common variant associations. However, 53 of these had a lead variant frequency of less than 5% in NFSEE individuals, and 38 of them were enriched by more than two times in the Finnish population compared with the NFSEE population. We observed a coding variant more often in the credible sets of associations that were enriched by more than twofold (19 out of 38; 50%) than in non-enriched associations (6 out of 15; 40%) at lower frequencies (MAF < 5%). 　　Following the discovery of 27 new associations, we sought to determine potential mechanisms of action through the identification of coding variants in their credible sets and potential regulatory effects by colocalization with eQTL associations from the eQTL Catalogue. We identified putative causal coding variants in 9 out of 27 loci and eQTL colocalization in 4 out of 27 loci. In two out of the four eQTL loci, we observed a coding variant in credible sets (IL4R and MYH14; the eQTLs point to different genes than the coding variants). The two remaining eQTL colocalizations were breast cancer loci colocalizing with H2BP2 eQTL in lung tissue and type 2 diabetes colocalizing with PRRG4 in lipopolysaccharide-stimulated monocytes. The disease relevance of these eQTLs is currently not evident. 　　No credible coding variants or eQTLs were identified in 16 out of 27 loci (Supplementary Table 8). The fraction of associations in which we observed eQTLs was small (14.8%). Most of the new associations were driven by variants with low AFs in NFSEE populations (Table 1 and Fig. 2b,d). The low fraction of observed eQTL colocalizations is probably explained by the low AF of 25 out of the 27 of the variants in available eQTL studies (such as GTEx), for which the majority of the samples do not have Finnish or Estonian ancestry. 　　We next aimed to explore the benefits of the FinnGen dataset in GWAS discovery. We extrapolated observed meta-analysis results in FinnGen, the UKBB and the EstBB to match the sample size of the UKBB in 14 demonstration diseases (excluding Alzheimer’s disease;  Supplementary Methods). The distribution of extrapolated P values was shifted towards greater significance in FinnGen compared with those of the UKBB and the EstBB in a matched total sample size scenario for the 14 demonstration diseases ( Supplementary Methods and Supplementary Fig. 11). Moreover, frequency enrichment was a major driver in the gain of power in low-frequency variants (Supplementary Fig. 12). In individual end points with similar sample prevalence in FinnGen and the UKBB, similar for inflammatory bowel disease (IBD), the greatest gain in power was in variants in which the AFs are <0.5% in the UKBB (see Supplementary Fig. 13 for a comparison for each end point and biobank). 　　The identification of a new signal for IBD mapping to a single variant in an intron of TNRC18 highlights the value of FinnGen for discovery, even when the case sample size is below that of existing meta-analyses. This variant has a strong risk-increasing effect (AF = 3.6%, odds ratio (OR) = 3.2, P = 2.4 × 10−61), which eclipses the significance of signals at IL23R, NOD2 and the major histocompatibility complex. The variant is enriched by 114-fold in the Finnish population compared with the NFSEE population, in whom the AF is too low (0.04%) to have been identified in previous GWASs (this FinnGen association was also reported in ref. 11). We were, however, able to replicate this association in the EstBB (AF = 1.3%, OR = 3.9, P = 2.8 × 10−6) owing to the relatively higher frequency in the genetically related Estonian population. This variant was also associated with risk for multiple other inflammatory conditions evaluated in FinnGen, including interstitial lung disease (OR = 1.43, P = 6.3 × 10−26), ankylosing spondylitis (OR = 4.2, P = 1.8 × 10−34), iridocyclitis (OR = 2.3, P = 1.2 × 10−27) and psoriasis (OR = 1.6, P = 1.1 × 10−13). However, the same allele appears to be protective for an end point that combines multiple autoimmune diseases (https://r5.risteys.finngen.fi/phenocode/AUTOIMMUNE) (OR = 0.84, P = 6.2 × 10−12; for example, type 1 diabetes (OR = 0.64, P = 2.7 × 10−7) and hypothyroidism (OR = 0.85, P = 7.8 × 10−7). 　　The highest number (eight loci) of new and enriched low-frequency associations were identified in type 2 diabetes, which is probably due to the large number of patients with type 2 diabetes in FinnGen release 5 (29,193). Other noteworthy observations from this set of 30 findings for 15 well-studied diseases are described in Supplementary Note 1. 　　Motivated by the identification of high-effect coding variant associations within the selected 15 diseases, we performed a PheWAS followed by fine-mapping to identify putative causal coding variants enriched in the Finnish population. 　　In a GWAS of 1,932 distinct end points and 16,387,711 variants (Supplementary Table 4; case overlap < 50% and n cases > 80), we identified 2,733 independent associations in 2,496 loci across 807 end points (Supplementary Table 9) at a genome-wide significance threshold (P < 5 × 10−8). Moreover, 893 signals in 771 loci across 247 end points at PWS thresholds (P < 2.6 × 10−11) were identified. The HLA region was excluded here, and a PheWAS of imputed classical HLA gene alleles in FinnGen is reported in ref. 8. 　　Using statistical fine-mapping, we observed a coding variant (missense, frameshift, canonical splice site, stop gained, stop lost or inframe deletion; PIP > 0.05) in 369 associations (13.5% of all associations) spanning 202 end points. Full results with all 2,803 end points (including end points with a case overlap of >基于PHEWEB代码库（https://r5.finngen.fi）和作为摘要统计文件（https://wwwwwww.finngen.fi/en/en/en/access_results），在此处排除的50％）从定制浏览器（https://r5.finngen.fi）公开可用。 　　为了将频谱和假定的作用机理在可解释的环境中，我们通过基于LD的合并选择了每个信号的单个最重要的关联（R2> 0.3铅变体），从而在681个终点（补充表10）中导致了1,838个独特的关联。总体而言，112个终点的493个关联是PW（P< 2.6 × 10−11). Although most of the 493 PWS unique associations were driven by common variants, 143 and 97 had a lead variant frequency of <5% and <1%, respectively, in gnomAD NFSEE populations. We observed that 82 (57.3%) of the 143 low-frequency (MAF < 5%) lead variants were enriched by more than twofold in Finland compared with NFSEE populations. To estimate the number of putative new associations, we searched for known significant associations using the Open Targets API platform (GWAS Catalogue and the UKBB) and ClinVar for each of the 1,838 associations. Among these, 864 (47%) were not associated with any phenotype in those databases (75 out of 493 (15%) of the stringent P < 2.6 × 10−11 associations). The fraction of previously unreported associations among genome-wide significant (702 out of 841 (84%)) and stringent (69 out of 143 (48%)) associations were notably higher among low-frequency variants (MAF < 5% in NFSEE individuals). 　　After statistical fine-mapping of the 493 unique PWS associations, we identified a coding variant (PIP > 0.05) in 73 (14.8%) of the credible sets associated with 42 end points (Supplementary Table 10). Most (43) of the fine-mapped coding variants had PIP values of >0.5和28的PIP值> 0.9（图3A）。相关编码变体的最高比例和大多数（73个）具有NFSEE MAF< 10% (Fig. 3b,c). The coding variant associations were more enriched in Finland than noncoding associations in associations driven by variants with AFs of <5% in NFSEE people (Fig. 3d; Wilcoxon rank sum test P = 3.6 × 10−3). For example, we observed a coding variant in 42% (34 out of 89) of the associations with a lead variant that was enriched by more than two times in Finland compared with NFSEE people among low-frequency associations (NFSEE MAF < 5%). By contrast, the proportion of coding variants was lower at 21.7% (13 out of 60) in non-enriched associations (see Extended Data Fig. 4 for enrichment in various NFSEE MAF bins). The higher proportion of coding variants in those that were enriched by more than two times persisted when the PIP threshold was increased to 0.2 (enriched, 30 out of 77 (35.8%); non-enriched, 11 out of 58 (18.9%)). 　　The fine-mapping properties and replicability of 67 FinnGen traits across diverse biobanks (FinnGen, Biobank Japan and the UKBB) are explored in detail in another manuscript10, and functional variant associations in the UKBB and FinnGen are described in ref. 12. 　　We next wanted to quantify the benefits of population isolates such as Finland in GWAS discovery. To this end, we assessed whether lower frequency (MAF < 5% in NFSEE people) variants enriched in the Finnish population were more likely to be associated with a phenotype than would be expected by chance. We randomly sampled 1,000,000 times the number of genome-wide significant variants observed (143) from a set of frequency-matched variants (MAF NFSEE < 5%) that were not associated with any end point (P > 0.001). None of the 1 million random draws had a higher proportion of variants enriched by more than twofold in the Finnish population than was observed in the significant associations (57.3% observed versus 33% expected; P = 1.0 × 10−16). 　　Among the genome-wide significant coding variant associations, we identified 13 variant associations (AF range of 0.04–2%) classified as pathogenic or likely pathogenic in ClinVar (Supplementary Table 10). Nine out of the 13 variants were enriched by more than 20-fold in Finland compared with NFSEE populations. Some of these variants have previously been primarily considered recessive. Here, however, we observed that some were a risk variant in the heterozygous state. An example is a rare frameshift variant at NPHS1 associated with nephrotic syndrome, including the congenital form (ICD-10: N04,p.Leu41fs; AF FinnGen = 0.9%; gnomAD NFSEE = 0.009%; OR = 185, P = 4.3 × 10−27). Congenital nephrotic syndrome in Finnish individuals is a recessively inherited rare disease, and is in the Finnish Disease Heritage database4. The pathogenic variant associations listed in ClinVar include a missense variant in XPA (xeroderma pigmentosum) associated with non-melanoma neoplasm of skin (‘other malignant neoplasm of skin’) (p.Arg228Ter; AF FinnGen = 0.02%, gnomAD NFSEE = 0%; OR = 4.4, P = 8.3 × 10−18), and the abovementioned frameshift variant in PALB2 associated with breast cancer (p.Leu531fs, ‘malignant neoplasm of breast’; p.Ala82Pro; AF FinnGen = 0.2%, gnomAD NFSEE = 0%; OR = 28.8, P = 3.7 × 10−33). Furthermore, a known pathogenic recessively acting missense variant in CERKL was associated with hereditary retinal dystrophy (p.Cys125Trp; AF FinnGen = 0.6%, gnomAD NFSEE = 0%; OR = 98,716, P = 5.15 × 10−25). This association is, however, driven by compound heterozygotes, as previously detailed13. These associations demonstrate that imputation using a population-specific genotyping array and an imputation panel combined with national-registry-based phenotyping in the isolated Finnish population can successfully identify associations and fine-map causal variants even in rare variants and phenotypes. An extended study of ClinVar variants and variants with specific biallelic Mendelian effects in FinnGen is provided in a companion paper13. 　　In the remaining 135 genome-wide significant coding variant associations not reported as pathogenic in ClinVar, 77 had NFSEE MAF values of <5%. Of the 77 variants, 54 were more than 5 times more common in Finland than in NFSEE populations, and 19 had not been previously observed in NFSEE people (Supplementary Table 2). Nine out of the 19 variants are in a gene in which other variants are pathogenic for various traits, 3 of which are for the same or related traits. These FinnGen associations include the following variants: a RFX6 frameshift variant associated with type 2 diabetes (p.His293LeufsTer7; AF = 0.15%, OR = 3.7, P = 1.2 × 10−10; ClinVar, ‘monogenic diabetes and others’); a TERT missense variant (AF = 0.15%, OR = 1,032, P = 6.5 × 10−21) associated with idiopathic pulmonary fibrosis (ClinVar, ‘idiopathic pulmonary fibrosis’); a missense in MYH14 associated with sensorineural hearing loss (p.Ala1156Ser; AF = 0.04%, OR = 19.9, P = 1 × 10−15; ClinVar, ‘non-syndromic hearing loss’ and others); and a stop gained variant in TG associated with autoimmune hypothyroidism (p.Gln655Ter; AF = 0.1%, OR = 3.2, P = 3.9 × 10−11). These variants in RFX6, TERT and TG have been previously observed in Finnish and Nordic cohorts14,15,16, but had uncertain significance (single carrier in TG) or conflicting interpretation (TERT) in ClinVar. Pathogenic variants in RFX6 cause Mitchell–Riley syndrome with recessive inheritance (characterized by neonatal diabetes). However, heterozygote enrichment of RFX6-truncating variants have been observed in maturity-onset diabetes of the young14, for which the same variant observed here was identified in a replication in Finnish data. RFX6 is a regulator of transcription factors involved in beta-cell maturation and has a specific role in releasing gastric inhibitory peptide (GIP) and GLP1 in response to meals. Our results propose that around 1:700 individuals in Finland carry a frameshift variant that has been previously shown to reduce incretin levels and to lead to isolated diabetes14. It is tempting to speculate that early administration of GLP1 analogues would benefit carriers of this diabetes-associated variant. 　　Among the previously undescribed genome-wide significant coding variant associations without previous associations in Open Targets (GWAS Catalog and the UKBB) or ClinVar, we observed 29 that had NFSEE MAF values of <5% and were 2 times more frequent in Finland, 9 of which had no copies in NFSEE populations (Supplementary Table 11). We summarize selected new discoveries and biological knowledge gained in Supplementary Table 12. A missense variant not observed outside Finland (p.Val70Phe; AF = 0.2%, OR = 3.0, P = 2.1 × 10−9) in PLTP was associated with coronary revascularization (n = 12,271 coronary angioplasty or bypass grafting). PLTP is a lipid-transfer protein in human plasma that transfers phospholipids from triglyceride-rich lipoproteins to high-density lipoprotein, and its activity is associated with atherogenesis in humans and mice17. Noncoding variations near PLTP independent of p.Val70Phe are associated with lipid levels (high-density lipoprotein and triglycerides)18 and coronary artery disease19. The identification of a coding variant in this gene provides support for PLTP as the causal gene for symptomatic atherosclerosis in this locus. Other variants associated with coronary artery disease included a missense variant (p.Gly567Arg; AF = 0.9%, OR = 2.0, P = 5.2 × 10−12) in HHIPL1, which was associated with coronary revascularization (n = 12,271), and a splice acceptor variant (c.7325-2A>G; AF = 0.7%, OR = 2.5, P = 2.9 × 10−08) in NBEAL1, which was associated with coronary artery bypass grafting (n = 5,779). Both genes are susceptibility loci for coronary artery disease19 and have been suggested as causal, although for NBEAL1 the evidence is inconsistent20. HHIPL1 encodes a secreted sonic hedgehog regulator that modulates atherosclerosis-relevant smooth muscle cell phenotypes and promotes atherosclerosis in mice21. NBEAL1 regulates cholesterol metabolism by modulating low-density lipoprotein (LDL) receptor expression, and genetic variants in NBEAL1 are associated with decreased expression of NBEAL1 in arteries22. Our results strengthen the evidence that both these genes are causal in the loci. 　　A missense variant in LAG3 (p.Pro67Thr; AF = 0.08%, gnomAD NFSEE = 0%) was associated with autoimmune hypothyroidism (n = 22,997, OR = 3.2, P = 4.6 × 10–8, lead variant P = 4.57 × 10–8). LAG3 encodes an immune checkpoint protein that is involved in inhibitory signalling of immune response, especially in T cells23. LAG3 has been a target of active immune checkpoint inhibitor cancer immunotherapy development. One such immunotherapy was recently approved by the US Food and Drug Administration as a combination treatment for unresectable or metastatic melanoma24. Immune checkpoint inhibition therapies aim to enhance immune responses against tumour cells. Excessive immune responses, however, can exert deleterious effects on healthy tissue and lead to autoimmune disease. A common side effect of immune checkpoint inhibitors, including those that target LAG3, is hypothyroidism. The p.Pro67Thr variant could be acting as an inhibitor of LAG3 immunoregulatory activity, which in turn leads to susceptibility to hypothyroidism. In a PheWAS of p.Pro67Thr, we observed a nominally increased risk for other immune-related conditions (for example, psoriatic arthropathies (M13_PSORIARTH_ICD10) n = 1,455, OR = 7.8, P = 3.3 × 10−3; urticaria and erythema (L12_URTICARIAERYTHEMA), n = 6,328, OR = 3.7, P = 2.7 × 10−4; and streptococcal septicaemia (AB1_STREPTO_SEPSIS), n = 1,090, OR = 15, P = 2.2 × 10−3), but we did not observe protective effects with any cancers. It should be noted, however, that owing to the rarity of the variant, the data were not sufficiently powered to detect more subtle effects. 　　We found a missense variant (p.Tyr212Phe, rs35937944) in COLGALT2 that was enriched by >芬兰人口20倍。该变体与关节炎的风险降低（OR = 0.79，p = 2.57×10-10），竞争性（OR = 0.68，p = 1.34×10-19）和促性腺炎（OR = 0.80，p = 7.5×10-7）有关。Colgalt2附近的一种非编码变体已被描述为骨关节炎的GWAS位点25。Colgalt2编码胶原二糖基转移酶2，该酶2通过将β-半乳糖转移到羟基乙醇苷残基来启动胶原蛋白的翻译后修饰，这是确保骨骼和结缔组织的结构和功能的重要步骤。使用药物调节Colgalt2酶活性可能是降低关节炎风险的潜在策略。 　　CD63是一种参与嗜碱性粒细胞活化和肥大细胞脱粒的细胞表面蛋白。我们确定了CD63（RS148781286）中的错义变体，在芬兰人口中富含42倍的变体。该变体与儿童哮喘有关（OR = 3.5，p = 3.37×10–9）。在与ESTBB和UKBB的数据的结合分析中，该变体也与特应性皮炎26有关。由嗜碱性粒细胞和肥大细胞分泌的介质与临床中的哮喘严重程度相关，据报道，基于CD63的基于CD63的嗜碱性粒细胞激活测试可以预测发作发作的幼儿的哮喘结果27。观察CD36，嗜碱性激活和儿童哮喘风险和严重程度的遗传变异之间的假定因果关系可能指向靶向哮喘疗法的新干预点。 　　TUBA1C（p.ala331val; af = 0.2％，OR = 35.2，p = 1.4×10-10）中的错义变体与突然的特发性听力损失有关（n = 1,491）。以前尚无相关表型的据报道，在TUBA1C中的变体。Tuba1c编码α-微管蛋白同种型。α-微管蛋白同种型的确切作用尚不清楚，但是其他小管蛋白的突变会引起各种神经发育疾病28。p.ala331val变体也与前庭神经炎（前庭神经的炎症； n = 1,224，OR = 40.9，p = 3.2×10-10）有关。纯粹的前庭神经炎急性呈眩晕，但没有听力损失，并且在急性环境中对眩晕的准确诊断很具有挑战性，并且可能误诊。 　　在ZAP70中，A> 30倍富集的错义变体PTHR155MET（RS145955907）与结节病有关（OR = 2.05，p = 1.03×10-8）。以前，ZAP70中的纯合子或化合物杂合突变已在由T细胞受体受体信号异常引起的细胞介导的联合免疫缺陷中进行了描述。29。到目前为止，杂合子变体的关联尚未与任何疾病有关。鉴于其在细胞信号传导中的关键作用，ZAP70与结节病的关联似乎符合其在免疫中的关键作用。 　　PPP1R26中的75倍富集的错义变体P.Ala777THR（RS199680517）与子宫内膜异位症有关（OR = 1.97，P = 3.41×10-8）。PPP1R26（蛋白质磷酸酶1调节亚基26）与肿瘤形成有关，并且已经观察到在各种恶性肿瘤中被上调。细胞GWAS分析已经确定了一种与卡铂诱导的毒性30相关的变体。在一项研究中，拷贝数变异与子宫内膜异位症有关，但是该基因如何促进子宫内膜异位症的易感性仍然存在投射31。 　　我们还在单独的手稿中报告了其中几个编码关联。这样一个新的观察结果是具有多效性关联的SPDL1中的错义变体（P.Arg20GLN； AF = 3％，GNOMAD NFSEE = 0.7％）。它与特发性肺纤维化的风险大大增加有关（OR = 3.1，p = 1.0×10-15），但具有结合所有癌症的终点（OR = 0.82，p = 2.1×10-15）32。在单独手稿中描述的变体与疾病之间的其他关联包括以下内容：MFGE8和冠状动脉粥样硬化中的插图缺失（P.ASN239DUP； AF = 2.9％，GNOMAD NFSEE = 0％，OR = 0.74，OR = 0.74，P = 5.4×10-15）33;MEPE中的移码变体（p.lys101ilefster26; af = 0.3％，gnomad nfsee = 0.07％，OR = 18.9，p = 1.5×10-11）和耳效性34;Angptl7（P.Arg220Cys; AF = 4.2％，GNOMAD NFSEE = 0.06％，OR = 0.7，P = 7.2×10-16）和Glaucoma35中的错义变体。 　　Finngen中有一个值得注意的注册表是处方药采购注册表（Kela;补充表1），该注册表将自1995年以来自1995年以来的所有Finngen参与者购买的所有处方药链接。使用此注册表的处方记录，我们确定了两个丰富的低频率编码变体，这些变体与statin Chispation of Statin药物的购买相关联（三个或更多购买量表）（三个或更多的个人）（三个或更多的人）（三个或更多）（补充）11（补充）11）（11）。TM6SF2（P.LEU156PRO，rs187429064）中的错义变体与处方毒素的可能性降低（AF = 5.2％，GNOMAD NFSEE = 1.2％= 1.2％; OR = 0.86; or = 0.86; or = 0.86，p = 3.8×10-13），但与iniseri -MEDITION（或= 3.8×10-13）相关，以供inistion Merdication（in Inclion Merdication）（in Incluliniation）（in Incluli Medication）（in Insulion Merdication）（in Insuline Merdication）（或P = 8.2×10-11）和2型糖尿病（OR = 1.15，P = 2.6×10-8）。此外，同一变体与肝细胞癌（ICD-10 C22“肝癌和胆管癌”的风险强烈相关，OR = 3.7，p = 5.9×10-10）。在服用他汀类药物治疗后，肝和胆管癌的关联没有变化（OR = 3.7，p = 7.1×10-10）。与处方他汀类药物的可能性降低，TM6SF2 P.LEU156PRO和另一个独立（R2 = 0.003）的错义变体（P.Gly167lys，RS58542926）先前与LDL和总胆固醇水平降低有关。在小鼠模型中，P.Gly167lys和Leu156Pro均导致蛋白质更新增加，并降低了细胞TM6SF2水平37。TM6SF2 p.gly167lys导致肝大型，非常LDL粒子的分泌下降，并增加细胞内脂质积累38。这些作用可能解释了其与非酒精性脂肪肝病的关联39，与酒精相关的肝硬化40，肝细胞癌41和2型糖尿病的入射42。我们的结果在单个Phewas分析中提供了 以前未知的P.LEU156PRO变体的有力证据，该变体具有相似的后果，即降低脂质水平并增加糖尿病，肝硬化和肝癌的风险，如P.Gly167lys所观察到的那样。可以在自定义PheWeb浏览器（http://r5.finngen.fi/variant/19-19269704-a-g）中探索这种变体的这种多效性。