Association Between SNPs in 1q32.2 and NSCL ± P in Han Chinese Population

Objectives Non-syndromic cleft lip with or without cleft palate (NSCL ± P) is one of the most common birth malformations. Currently, numerous susceptibility SNPs have been reported by GWA studies, however, the replications of them among NSCL ± P from Han Chinese were very limited. Design In this study, we selected 16 SNPs around 1q32.2 based on the published GWA studies and replicated them among 302 trios with NSCL ± P from Han Chinese Population. The genotypic data was analyzed with FBAT, PLINK and R package. Setting The study was conducted in a tertiary medical center. Patients, participants 302 patients with CL ± P and their parents. Main outcome measures To ascertain the genetic variants in 1q32.2 in patients with CL ± P in Han Chinese Population. Interventions Blood samples were collected. Results We found T allele ( Z = 4.26, p = 0.00002) and T/T homozygotes ( Z = 4.4, p = 0.000011) at rs12063989 was significantly over-transmitted among non-syndromic cleft lip with or without cleft palate (NSCL ± P). Conclusions We found rs12063989 exhibited significant association with the occurrence of NSCL ± P, which would provide new evidence for the future study in the etiology of NSCL ± P.

Generalizability of GWA-Identified Genetic Risk Variants for Metabolic Traits to Populations from the Arabian Peninsula

The Arabian Peninsula, located at the nexus of Africa, Europe, and Asia, was implicated in early human migration. The Arab population is characterized by consanguinity and endogamy leading to inbreeding. Global genome-wide association (GWA) studies on metabolic traits under-represent the Arab population. Replicability of GWA-identified association signals in the Arab population has not been satisfactorily explored. It is important to assess how well GWA-identified findings generalize if their clinical interpretations are to benefit the target population. Our recent study from Kuwait, which performed genome-wide imputation and meta-analysis, observed 304 (from 151 genes) of the 4746 GWA-identified metabolic risk variants replicable in the Arab population. A recent large GWA study from Qatar found replication of 30 GWA-identified lipid risk variants. These complementing studies from the Peninsula increase the confidence in generalizing metabolic risk loci to the Arab population. However, both the studies reported a low extent of transferability. In this review, we examine the observed low transferability in the context of differences in environment, genetic correlations (allele frequencies, linkage disequilibrium, effect sizes, and heritability), and phenotype variance. We emphasize the need for large-scale GWA studies on deeply phenotyped cohorts of at least 20,000 Arab individuals. The review further presents GWA-identified metabolic risk variants generalizable to the Arab population.

Pathfinder: a gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences

Genome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier to finding more of this ‘missing heritability’ is assessment––the use of diverse measures across GWA studies as well as time and the cost of assessment. In a series of four studies, we created a 15-min (40-item), online, gamified measure of g that is highly reliable (alpha = 0.78; two-week test-retest reliability = 0.88), psychometrically valid and scalable; we called this new measure Pathfinder. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields reliable verbal and nonverbal scores, correlated substantially with standard measures of g collected at previous ages (r ranging from 0.42 at age 7 to 0.57 at age 16). Pathfinder showed substantial twin heritability (0.57, 95% CIs = 0.43, 0.68) and SNP heritability (0.37, 95% CIs = 0.04, 0.70). A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical, and behavioural sciences.

Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies

Genetic correlations between traits may cause correlated responses to selection. Previous models described the conditions under which genetic correlations are expected to be maintained. Selection, mutation and migration are all proposed to affect genetic correlations, regardless of whether the underlying genetic architecture consists of pleiotropic or tightly linked loci affecting the traits. Here, we investigate the conditions under which pleiotropy and linkage have different effects on the genetic correlations between traits by explicitly modeling multiple genetic architectures to look at the effects of selection strength, degree of correlational selection, mutation rate, mutational variance, recombination rate, and migration rate. We show that at mutation-selection(-migration) balance, mutation rates differentially affect the equilibrium levels of genetic correlation when architectures are composed of pairs of physically linked loci compared to architectures of pleiotropic loci. Even when there is perfect linkage (no recombination within pairs of linked loci), a lower genetic correlation is maintained than with pleiotropy, with a lower mutation rate leading to a larger decrease. These results imply that the detection of causal loci in multi-trait association studies will be affected by the type of underlying architectures, whereby pleiotropic variants are more likely to be underlying multiple detected associations. We also confirm that tighter linkage between non-pleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.

The Polygenic Risk Score Knowledge Base: A Centralized Online Repository for Calculating and Contextualizing Polygenic Risk Scores

Introduction: Genome-wide association (GWA) studies identify correlation between genetic variants and phenotypes. GWA findings can be used to calculate polygenic risk scores, which represent the aggregate genetic risk across all associated loci. Methods: We developed a centralized polygenic risk score calculator containing over 2,300 GWA studies from the NHGRI-EBI GWAS Catalog. Polygenic risk scores are calculated from user-uploaded data using various user-defined parameters across any disease(s) or studies. Results: The Polygenic Risk Score Knowledge Base (https://prs.byu.edu) and command-line interface facilitate user-specific polygenic risk score calculations. We report study-specific polygenic risk scores across the U.K. Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and identify potentially confounding genetic risk factors in ADNI.Discussion: We introduce the first streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies. We anticipate that the PRSKB will facilitate a wider adaptation and innovative use of polygenic risk scores in disease research. Data Availability: This project is documented online at https://polyriskscore.readthedocs.io/en/latest/, and all programs are publicly available at https://github.com/kauwelab/PolyRiskScore. A web interface is also available at https://prs.byu.edu/.

Insights into non-autoimmune type 1 diabetes with 13 novel loci in low polygenic risk score patients

With polygenic risk score (PRS) for autoimmune type 1 diabetes (T1D), this study identified T1D cases with low T1D PRS and searched for susceptibility loci in these cases. Our hypothesis is that genetic effects (likely mediated by relatively rare genetic variants) of non-mainstream (or non-autoimmune) T1D might have been diluted in the previous studies on T1D cases in general. Two cohorts for the PRS modeling and testing respectively were included. The first cohort consisted of 3302 T1D cases and 6181 controls, and the independent second cohort consisted of 3297 T1D cases and 6169 controls. Cases with low T1D PRS were identified using PRSice-2 and compared to controls with low T1D PRS by genome-wide association (GWA) test. Thirteen novel genetic loci with high imputation quality (Quality Score r2 > 0.91) were identified of SNPs/SNVs associated with low PRS T1D at genome-wide significance (P ≤ 5.0 × E−08), in addition to 4 established T1D loci, 3 reported loci by our previous study, as well as 9 potential novel loci represented by rare SNVs, but with relatively low imputation quality (Quality Score r2 < 0.90). For the 13 novel loci, 9 regions have been reported of association with obesity related traits by previous GWA studies. Three loci encoding long intergenic non-protein coding RNAs (lncRNA), and 2 loci involved in N-linked glycosylation are also highlighted in this study.

Three genes associated with anterior and posterior cruciate ligament injury

Aims The aim of this study was to screen the entire genome for genetic markers associated with risk for anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injury. Methods Genome-wide association (GWA) analyses were performed using data from the Kaiser Permanente Research Board (KPRB) and the UK Biobank. ACL and PCL injury cases were identified based on electronic health records from KPRB and the UK Biobank. GWA analyses from both cohorts were tested for ACL and PCL injury using a logistic regression model adjusting for sex, height, weight, age at enrolment, and race/ethnicity using allele counts for single nucleotide polymorphisms (SNPs). The data from the two GWA studies were combined in a meta-analysis. Candidate genes previously reported to show an association with ACL injury in athletes were also tested for association from the meta-analysis data from the KPRB and the UK Biobank GWA studies. Results There was a total of 2,214 cases of ACL and PCL injury and 519,869 controls within the two cohorts, with three loci demonstrating a genome-wide significant association in the meta-analysis: INHBA, AEBP2, and LOC101927869. Of the eight candidate genes previously studied in the literature, six were present in the current dataset, and only COL3A1 (rs1800255) showed a significant association (p = 0.006). Conclusion Genetic markers in three novel loci in this study and one previously-studied candidate gene were identified as potential risk factors for ACL and PCL injury and deserve further validation and investigation of molecular mechanisms. Cite this article: Bone Jt Open 2021;2(6):414–421.

From Genome-Wide to Environment-Wide: Capturing the Environome

Genome-wide association (GWA) studies have shown that genetic influences on individual differences in affect, behavior, and cognition are driven by thousands of DNA variants, each with very small effect sizes. Here, we propose taking inspiration from GWA studies for understanding and modeling the influence of the environment on complex phenotypes. We argue that the availability of DNA microarrays in genetic research is comparable with the advent of digital technologies in psychological science that enable collecting rich, naturalistic observations in real time of the environome, akin to the genome. These data can capture many thousand environmental elements, which we speculate each influence individual differences in affect, behavior, and cognition with very small effect sizes, akin to findings from GWA studies about DNA variants. We outline how the principles and mechanisms of genetic influences on psychological traits can be applied to improve the understanding and models of the environome.

Pathfinder: A gamified measure to integrate general cognitive ability into the biological, medical and behavioural sciences

Genome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier is measurement heterogeneity. In a series of four studies, we created a 15-minute, online, gamified measure of g that is highly reliable, psychometrically valid and scalable. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields verbal and nonverbal scores, showed substantial twin heritability (57%) and SNP heritability (37%). A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical and behavioural sciences.

New Genes and Emerging Mechanisms of Type 1 Diabetes

Type 1 Diabetes susceptibility depends upon the complex interaction between numerous genetic as well as environmental factors. 50% of the familial clustering of T1D is explained by HLA locus alleles. Other multiple loci contribute the rest of the susceptibility, in which very little were known since last few years. Four novel loci were found from the results of stage-I, genome wide association (GWA) studies which were carried out with high-density genotyping arrays. As the stage-II of the Genome Wide Association studies completed, hopefully, most of the genetic reasons of Type 1 Diabetes will be identified.