{"id":6241,"date":"2026-05-06T10:16:26","date_gmt":"2026-05-06T10:16:26","guid":{"rendered":"https:\/\/agrosynapsis.com\/?p=6241"},"modified":"2026-05-06T10:16:31","modified_gmt":"2026-05-06T10:16:31","slug":"how-do-we-measure-linkage-disequilibrium-ld-in-practice","status":"publish","type":"post","link":"https:\/\/agrosynapsis.com\/es\/how-do-we-measure-linkage-disequilibrium-ld-in-practice\/","title":{"rendered":"How do we measure Linkage Disequilibrium (LD) in practice?"},"content":{"rendered":"<p>In a previous post, I explained that linkage disequilibrium (LD) occurs when alleles at different loci co-occur more or less often than expected by chance.<br><br>Now the question is how we measure this in a way that is useful for GWAS.<br><br>Let\u2019s consider two SNPs:<br>SNP1: alleles A \/ a<br>SNP2: alleles B \/ b<br>We define:<br>p\uab7a: frequency of allele A<br>p\u13fc\u200b: frequency of allele B<br>p\uab7a\u13fc(obs): observed frequency of the haplotype AB<br><br>If the two alleles combine randomly, the expected haplotype frequency is:<br>p\uab7a\u13fc(exp)=p\uab7a x p\u13fc\u200b<br><br>\ud83d\udc49 LD exists when the observed frequency deviates from the expected one. To quantify this deviation, we calculate the difference D:<br>D= p\uab7a\u13fc(obs)\u200b &#8211; p\uab7a\u13fc(exp)<br><br>But the value D is inherently dependent on allele frequencies. As a result, it is not standardized and cannot be reliably used to compare LD between different pairs of loci. Let&#8217;s check two numerical examples:<br>Case 1: Common alleles<br>p\uab7a=0.5<br>p\u13fc\u200b=0.5<br>p\uab7a\u13fc=0.5 x 0.5= 0.25, p\uab7a\u13fc(\u1d0f\u0299s)\u200b=0.35<br>D=0.35\u22120.25=0.10<br><br>Example 2: Rare alleles<br>p\uab7a=0.1<br>p\u13fc\u200b=0.1<br>p\uab7a\u13fc=0.1 x 0.1= 0.01, p\uab7a\u13fc(\u1d0f\u0299s)\u200b=0.11<br>D=0.11\u22120.01=0.10<br><br><br>In both examples, the value of D is exactly the same (D = 0.10). However, the biological interpretation is completely different. In the case of rare alleles, the same numerical value reflects a much stronger association \u2014 the AB haplotype occurs 10X more frequently than expected under random combination.<br><br>Because D depends on allele frequencies and is not directly comparable across loci, it is standardized by dividing it by its maximum possible value (Dmax), resulting in D\u2032, which ranges between 0 and 1. However, even after this normalization, D\u2032 still depends on allele frequencies and can be inflated for rare alleles.<br><br>To avoid this limitation, we use the r\u00b2,the \ud835\ude00\ud835\uddfe\ud835\ude02\ud835\uddee\ud835\uddff\ud835\uddf2\ud835\uddf1 \ud835\uddf0\ud835\uddfc\ud835\uddff\ud835\uddff\ud835\uddf2\ud835\uddf9\ud835\uddee\ud835\ude01\ud835\uddf6\ud835\uddfc\ud835\uddfb between SNPs. The formula is:<br>r\u00b2= D\u00b2 \/ p\uab7a(1-p\uab7a)p\u13fc\u200b(1-p\u13fc)<br><br>If r\u00b2 is close to zero, there is a little association between the two SNPs. if the r\u00b2 is closer to one, the association is high and one SNP can predict the other one.<br><br>Let&#8217;s see how this impacts the two previous cases. In the case of common alleles, r\u00b2 is about 0.16, indicating only moderate linkage disequilibrium. The SNPs are partially correlated. In a GWAS context, this means limited tagging efficiency of causal variants.<br><br>In the case of rare alleles, the same D value translates into r\u00b2 close to 1, meaning near-perfect correlation. Here, knowing one SNP essentially tells you the other, resulting in excellent tagging, and high GWAS power even without genotyping the causal variant.<br><br>\ud83d\udc49 SO, GWAS does not detect causal variants directly \u2014 it detects them through r\u00b2.<br><br>\ud83d\udc49 If you\u2019d like to be informed about the upcoming workshops organized by <a href=\"https:\/\/www.linkedin.com\/company\/agrosynapsis\/\">AgroSynapsis<\/a>, and receive early access and discounts, \ud835\uddf3\ud835\uddf6\ud835\uddf9\ud835\uddf9 \ud835\uddfc\ud835\ude02\ud835\ude01 \ud835\uddfc\ud835\ude02\ud835\uddff \ud835\ude00\ud835\uddf5\ud835\uddfc\ud835\uddff\ud835\ude01 \ud835\ude01\ud835\uddff\ud835\uddee\ud835\uddf6\ud835\uddfb\ud835\uddf6\ud835\uddfb\ud835\uddf4 \ud835\uddf6\ud835\uddfb\ud835\ude01\ud835\uddf2\ud835\uddff\ud835\uddf2\ud835\ude00\ud835\ude01 \ud835\uddf3\ud835\uddfc\ud835\uddff\ud835\uddfa here:<br><br><a href=\"https:\/\/lnkd.in\/g3tApqPz\">https:\/\/lnkd.in\/g3tApqPz<\/a><\/p>\n<div style=\"margin-top: 0px; margin-bottom: 0px;\" class=\"sharethis-inline-share-buttons\" ><\/div>","protected":false},"excerpt":{"rendered":"<p>Discover how linkage disequilibrium is measured and why r\u00b2\u2014not D\u2014is the key metric behind GWAS signal detection.<\/p>","protected":false},"author":2,"featured_media":6220,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[46],"tags":[],"class_list":["post-6241","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-knowledge-bites"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How do we measure Linkage Disequilibrium (LD) in practice? 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