PRINCIPALES SNPs QUE MODULAN EL METABOLISMO …€¦ · Ten paciencia con todo aquello que no se ha resuelto en tu corazón e intenta amar las preguntas por sí mismas, como si fueran

PRINCIPALES SNPs QUE MODULAN EL METABOLISMO LIPÍDICO EN PACIENTES CON

SÍNDROME METABÓLICO

Estudiante: Juan Francisco Alcalá Díaz

MÁSTER EN BIOINFORMÁTICA Y BIOLOGÍA COMPUTACIONAL ESCUELA NACIONAL DE SALUD-

INSTITUTO DE SALUD CARLOS III

2013-2014

Centro: Instituto Maimónides de Investigación Biomédica de CórdobaDirector de la tesis: Javier Delgado ListaFecha: Febrero de 2015

Ten paciencia con todo aquello que no se ha resuelto en tu corazón

e intenta amar las preguntas por sí mismas,como si fueran habitaciones cerradas,

o libros escritos en una lengua extranjera.No busques ahora las respuestas

que no estés preparado para vivir.Pues la clave es vivirlo todo.

Vive las preguntas ahora.

-Rainer María Rilke-

AGRADECIMIENTOS,

Sirvan las presentes palabras para agradecer a todas aquellas personas que han hecho posible, de alguna u otra manera, el desarrollo de este trabajo.

En primer lugar agradecer a mi director, el Dr. Javier Delgado Lista, por el apoyo y la cercanía mostrada para la elaboración de este trabajo. Su constancia en el trabajo y la ilusión que desprende por mejorar cada día representan un ejemplo a seguir.

Al Prof. Francisco Pérez Jiménez por compartir su sabiduría y grandes consejos, así como por saber transmitir a las nuevas generaciones el germen de la verdadera ciencia.

Al Prof. José López Miranda y todo el equipo que conforman el grupo de Nutrigenómica y Síndrome Metabólico del Instituto Maimónides de Investigación de Córdoba, por

Al los profesores del Máster de Bioinformática y Biología Computacional y en especial a su director, D. Alfonso Valencia, por su dedicación y generosidad a la hora de compartir sus conocimientos.

A Miguel Ponce de León, coordinador académico del Máster por su carácter y amabilidad y por haber constituido siempre nuestro punto de apoyo.

A la Escuela Nacional de Sanidad del Instituto de Salud Carlos III por ofrecer luz en tiempos de oscuridad.

A mis compañeros del Máster por encarnar a la perfección el espíritu del compañerismo, por su trabajo incansable, su cariño y por hacer posible lo imposible. Especialmente a mis compañer@s de tupla, gracias por todo lo que habéis compartido conmigo.

A Luis, por enseñarme lo que significa no rendirse nunca.

A mi familia y muy especialmente a María José, mi mujer, por constituir los pilares fundamentales de mi vida.

A todos, gracias.

3

ÍNDICE

Índice Pág. 4

Objetivo Principal Pág. 5

Introducción Pág. 6

Material y Métodos Pág. 8

Resultados Pág. 11

Discusión Pág. 24

Conclusión Pág. 27

Bibliografía Pág. 28

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OBJETIVO PRINCIPAL

Identificar las variaciones genéticas (SNPs) más relacionadas con diferentes características

fenotípicas del metabolismo lipídico entre 904 variaciones de genes candidatos sobre una

cohorte de 450 pacientes con síndrome metabólico.

5

INTRODUCCIÓN

Las nuevas estrategias científicas de aproximación a las bases fisiopatológicas de las

enfermedades y a la búsqueda de métodos diagnósticos y terapéuticos implican el estudio

multidisciplinar de las poblaciones sometidas a distintos modelos de intervención, realizando a

las mismas las diferentes determinaciones necesarias para la identificación de parámetros

biológicos, bioquímicos, genéticos, entre otros, y la integración de todos estos datos. En este

escenario complejo, la utilización del análisis computacional permite la utilización de grandes

bases de datos y la generación de un conocimiento integrador, así como la posibilidad de

identificar marcadores pronósticos de evolución de enfermedad o nuevas dianas terapéuticas que

permitan la traslación de estos hallazgos a la práctica clínica de una manera más precoz y segura.

El síndrome metabólico (SM) es una entidad clínica de alta prevalencia asociada con un aumento

muy marcado en el riesgo de desarrollar enfermedad cardiovascular (ECV) y diabetes mellitus

tipo 2 (DM2)1. Su diagnóstico, según la definición de la NCEP2, viene establecido por la

presencia de al menos 3 de los siguientes criterios: obesidad central, hipertensión,

hipertrigliceridemia, bajos niveles de c-HDL y alteración en los niveles de glucosa en ayunas.

Aunque la etiología precisa de este síndrome no ha sido aclarada completamente, se ha propuesto

que puede ser debido a una combinación de factores ambientales (principalmente de la dieta y

actividad física) y factores genéticos3,4.

Numerosas variaciones genéticas individuales se han relacionado con los diferentes rasgos del

SM5. Sin embargo, los estudios de asociación que evalúan los polimorfismos genéticos de un

solo nucleótido (SNPs) pueden no ser totalmente representativos de los efectos genéticos

generales en los sujetos con síndrome metabólico. Algunos estudios de asociación del genoma

6

completo (en inglés, GWAS, Genome-wide association study) se han centrado en las

asociaciones con la prevalencia del SM o de sus criterios diagnósticos6,7. Una revisión reciente

agrupó todos los datos disponibles en relación con el metabolismo lipídico en pacientes con

síndrome metabólico y señaló al cluster de genes formado por APOA1/C3/A4/A5, APOB,

APOE, CETP, TCF7L2 y GKRP como posibles genes candidatos8, mientras que otro estudio

GWAS identificó adicionalmente otros genes como ETFB y TMX27.

Un enfoque intermedio, incluyendo un conjunto de SNPs implicados en vías específicas,

previamente seleccionadas en términos de plausibilidad biológica, podría constituir un buen

modelo para testar la contribución de los factores genéticos con el metabolismo energético

humano. En concreto, la vía del metabolismo lipídico constituiría una diana interesante, dado su

papel clave en el desarrollo de enfermedades crónicas, como la aterosclerosis, la DM2 y el

envejecimiento.

El estudio LIPGENE, proyecto integrado del sexto programa marco de investigación y desarrollo

tecnológico de la UE, contaba con la participación de 25 centros de 14 países europeos, y tiene

por objetivo inicial el analizar y comprender mejor el síndrome metabólico, aportando un

enfoque multidisciplinar para lograr un tratamiento y prevención efectivos.

El objetivo del presente trabajo es evaluar la asociación de 904 SNPs seleccionados por su

potencial contribución al metabolismo de los lípidos en 450 participantes incluidos dentro de la

cohorte de pacientes del estudio LIPGENE.

7

MATERIAL Y MÉTODOS

El diseño del estudio LIPGENE y las características de sus participantes han sido plubicadas

previamente9,10 y son resumidas más abajo.

Pacientes

Sujetos con edades comprendidas entre los 35 y los 70 años, con un índice de masa corporal

(IMC=Peso/Talla2) de 20 a 40 kg/m2, fueron reclutados en centros de 8 países europeos (Irlanda,

Reino Unido, Noruega, Francis, Holanda, España, Polonia y Sucia). Toda la metodología cumple

normativa según la Declaración de Helsinki de 1975 (revisada en 1983). Aquellos sujetos que

cumplían al menos 3 de los criterios del National Cholesterol Education Program para el

díagnóstico de SM fueron incluidos: perímetro de cintura >102 cm (hombres) o >88cm

(mujeres); glucosa plasmática en ayunas >100 mg/dL; triglicéridos >150mg/dL; c-HDL<1.0

mmol/L (hombres) o <1.3 mmol/L (mujeres); presión arterial >130/85 mmHg o tratamiento o

diagnóstico previo de hipertensión. Se utilizaron los datos preintervención de 486 sujetos. Más

detalles de las características de esta cohorte han sido publicados en (9,10).

Determinaciones bioquímicas

La preparación y medidas de las variables bioquímicas fueron desarrolladas mediante la

metodología estandar descrita en publicaciones previas(9,10). Un total de 20 variables/

relacionadas con el metabolismo lipídico fueron analizadas: grasa total (Plasma Total Fat), grasa

saturada (Plasma SFA), grasa monoinsaturada (Plasma MUFA), grasa poliinsaturada (Plasma

PUFA), grasa n-6 (Plasma n-6), grasa n-3 (Plasma n-3), colesterol total (Cholesterol), colesterol

LDL (LDLC), ácidos grasos no esterificados (NEFA), colesterol HDL (THDL), triglicéridos

(TAG), quilomicrones (TRLc), triglicéridos transportados por partículas TRL (TRLTG),

8

ApoB48, ApoA1, ApoB, ApoCII, ApoCIII, ApoE, y concentraciones de ApoB transportadas en

TRL.

Extracción de DNA y genotipado

Los SNPs analizados en este trabajo fueron extraídos mediante sistema automatizado Autopure

LS (Gentra Systems Inc), amplificados con kit REPLI-g kit (Qiagen Ltd), y genotipados

mediante Illumina Inc. Los modelos dominates, recesivos o aditivos de cada SNP fueron

establecidos según datos reportados previamente en la literatura. Cuando no se encontró

información sobre el tipo de modelo, se seleccionaron los modelos según la base de frecuencias

de los fenotipos para las variables estudiadas.

Analísis estadístico

Todos los análisis estadísticos fueron realizados mediante el lenguaje y entorno de programación

para análisis estadístico y gráfico R (R version 3.0.2). Todas las variables bioquímicas descritas

previamente fueron testadas para normalidad y aquellas que no cumplían criterios de distribución

normal fueron normalizadas mediante transformación logarítma (log10) o cálculo de raíz

cuadrada.

Se ha desarrollado un modelo basado en el ANOVA (con corrección de Bonferroni) para testar

asociaciones con el SNP como factor fijo, y la edad, IMC, género y centro de origen del paciente

como covariables. Los efectos de la medicación, incluyendo los grupos farmacológicos (por

ejemplo, fármacos antihipertensivos), sobre las variables cuantitativas fueron evaluados como

una covariable en los diferentes modelos de ANOVA. Si éstos mostraban influencia estadística,

con p<0,05, se mantenían en el modelo.

9

Para minimizar la tasa de falsos positivos, se realizó técnica de bootstrapping para aquellas

pruebas de ANOVA positiva, con 10000 repeticiones, configurando como umbral de

significación la presencia de al menos un 80% de los valores de p<0.05 tras bootstrapping . Por

último, los SNPs fueron ordenados dependiendo de sus valores de p.

Para facilitar la lectura de los resultados obtenidos, se han desarrollado gráficos tipo Manhattan

que representan la localización cromosómica de las variables analizadas y los niveles de

significación obtenidos en el modelo tras las técnicas de remuestreo.

Las principales paquetes que han sido utilizados para la realización del modelado dentro del

programa ‘R’ han sido:

• library(car) - Companion to Applied Regression, Versión 2.0-22.

• library(effects) - Graphical and tabular effect displays, e.g., of interactions, for various

statistical models with linear predictors, Versión 3.0-3.

• library(multcomp) - Simultaneous Inference in General Parametric Models, Versión 1.3-8.

• library(ggplot2) - An implementation of the Grammar of Graphics, Versión 1.0.0.

• library(boot) - Bootstrap Functions, Versión 1.3-14.

• library(epicalc) - Epidemiological calculator, Versión 2.15.1.0.

• library(plotrix) - Various plotting functions, Versión 3.5-10.

• library(intervals) - Weighted Logrank Tests and NPMLE for interval censored data, Versión

1.1-0.1.

• library(gdata) - Various R programming tools for data manipulation, Versión 2.13.3.

10

RESULTADOS

La media de las tasas de éxito de genotipado (call rates) fue del 98.2%.

Un total de 18080 tests ANOVA fueron realizados, de los cuales 965 mostraron asociaciones

significativas entre los SNPs y las variables fenotípicas. Las técnicas de bootstrapping se

aplicaron a estas asociaciones, descartando 811 de estas asociaciones identificadas a priori

(83.6%), resultando finalmente en un set de 154 asociaciones de SNPs significativas con

marcadores del metabolismo lipídico.

La Tabla 1, a continuación, muestra un resumen de los SNPs que influyen sobre una variable

fenotípica dada, identificando los SNPs por orden de valor p.

Así mismo, se obtuvieron gráficos tipo Manhattan-plot para representar la influencia de las

diferentes variaciones analizadas sobre los parámetros del metabolismo lipídico estudiados (ver a

continuación).

11

Tabla 1. SNPs identificados con significación estadística que influyen en variables relacionadas con el metabolismo lipídico.

Variable

Genotype SNPs significativas tras

bootstrapping Gen MAF MM Mm mm Mm/mm P

Plasma Total Fat (mg/mL)

rs514230 LOC100506810 T:0.45 1.90±0.08a 1.95±0.07a 2.38±0.10b

0.0005 rs2953171 CAPN10 A:0.14 1.98±0.05a

2.33±0.09b 0.0007

rs2286963 ACADL C:0.33 2.09±0.07 1.93±0.07a 2.44±0.13b

0.0026 rs6682411 SLC27A3 A:0.07 2.13±0.05a

1.71±0.13b 0.0033

rs3856806 PPARG A:0.13 2.00±0.05a

2.31±0.09b 0.0033 rs17572019 PPARGC1B A:0.09 2.13±0.05a

1.77±0.11b 0.0035

rs4245232 LIPG A:0.20 2.17±0.06a

1.89±0.08b 0.0038 rs6927072 ESR1 A:0.33 1.95±0.07a 2.22±0.07b 1.89±0.14

0.0077

rs7732671 PPARGC1B C:0.09 2.12±0.05a

1.80±0.11b 0.0083 rs4846914 GALNT2 G:0.39 1.93±0.08a 2.19±0.07b 1.83±0.12a

0.0093

Plasma SFA (%) rs10158450 SLC27A3 G:0.47 31.7±0.5a 33.6±0.4b 31.7±0.5a

0.0017

rs2293889 TRPS1 T:0.45 34.2±0.5a 32.6±0.4 31.1±0.7b

0.0017 rs4783961 CETP A:0.47 33.5±0.5a 31.7±0.4b 33.6±0.6a

0.0042

Plasma MUFA (%) rs10877201 ABCD2 A:0.14 28.6±0.2a

26.8±0.4b 9.93e-05

rs6581228 ABCD2 G:0.14 28.6±0.2a

26.8±0.4b 9.93e-05

rs7968837 ABCD2 A:0.20 28.7±0.2a

27.1±0.3b 0.0003

rs4072006 ABCD2 A:0.09 28.4±0.2a

26.8±0.5b 0.0022

rs1035798 AGER A:0.26 28.2±0.3a 28.5±0.3a 25.8±0.7b

0.0034 rs2267443 SREBF2 A:0.38 29.0±0.3a 27.8±0.3b 27.1±0.6b

0.0035

rs2498429 FRDA A:0.11 27.8±0.2a

29.3±0.4b 0.0042

rs1800624 AGER A:0.26 28.2±0.3a 28.5±0.3a 25.8±0.7b

0.0048 rs831022 LRP2 A:0.33 27.8±0.3 28.7±0.3a 26.6±0.6b

0.0070

Plasma PUFA (%) rs10753929 ADIPOR1 A:0.12 38.6±0.3a

41.6±0.7b 0.0001

rs7968837 ABCD2 A:0.20 38.4±0.4a

40.8±0.5b 0.0004

rs6544718 ABCG8 A:0.20 38.5±0.4a

40.6±0.5b 0.0017

rs10877201 ABCD2 A:0.14 38.7±0.4a

40.9±0.6b 0.0018

rs6581228 ABCD2 G:0.14 38.7±0.4a

40.9±0.6b 0.0018

rs4783961 CETP A:0.47 38.5±0.6 40.3±0.4a 37.9±0.7b

0.0047 rs2293889 TRPS1 T:0.45 37.7±0.6a 39.0±0.5 40.9±0.8b

0.0055

rs2267443 SREBF2 A:0.38 38.0±0.5a 39.9±0.4b 40.7±0.9b

0.0066 Plasma n-6 (%)

rs10753929 ADIPOR1 A:0.12 34.7±0.3a

37.4±0.6b 0.0002

rs7968837 ABCD2 A:0.20 34.5±0.4a

36.8±0.5b 0.0002

rs6544718 ABCG8 A:0.20 34.5±0.4a

36.7±0.5b 0.0005

rs10877201 ABCD2 A:0.14 34.7±0.3a

37.0±0.6b 0.0007

rs6581228 ABCD2 G:0.14 34.7±0.3a

37.0±0.6b 0.0007

rs4783961 CETP A:0.47 34.5±0.6a 36.3±0.4b 34.1±0.6a

0.0030 rs951545 FABP3 A:0.16 34.8±0.3a

36.6±0.5b 0.0040

rs2814944 IGR A:0.15 35.7±0.4a

33.7±0.6b 0.0051

Plasma n-3 (%) rs15763 UCP3 A:0.25 4.23±0.11a

3.69±0.12b 0.0012

rs6453132 CART A:0.40 3.58±0.13a 4.22±0.12b 4.10±0.20

0.0015 rs4660293 PABPC4 G:0.25 4.07±0.11a

3.57±0.13b 0.0034

rs6507931 LIPG G:0.46 4.31±0.15a 3.96±0.12 3.53±0.18b

0.0041 rs659366 UCP2 A:0.36 4.31±0.13a 3.77±0.12b 3.67±0.23

0.0046

rs2276269 LIPG A:0.46 4.30±0.15a 3.96±0.12 3.54±0.18b

0.0054 rs943 HK2 T:0.42 4.27±0.14a 3.87±0.12 3.53±0.19b

0.0063

rs2076310 RXRB G:0.26 4.18±0.11a 3.64±0.14b 4.04±0.29

0.0087 Cholesterol (mmol/L)

rs7412 APOE T:0.08 5.42±0.05a

4.93±0.11b 4.04e-05

rs1799983 NOS3 A:0.33 5.15±0.06a 5.50±0.07b 5.52±0.12b

0.0003 rs1869592 VLDLR A:0.16 5.25±0.05a

5.57±0.08b 0.0008

rs693 APOB G:0.49 5.60±0.08a 5.29±0.06b 5.19±0.09b

0.0016 rs1367117 APOB A:0.32 5.23±0.06a 5.37±0.06a 5.74±0.13b

0.0023

rs11761556 LEP C:0.45 5.39±0.08 5.21±0.06a 5.58±0.09b

0.0026 rs696217 GHRL A:0.08 5.29±0.05a

5.63±0.11b 0.0034

rs701106 SCARB1 A:0.17 5.43±0.05a

5.16±0.08b 0.0035

rs2290465 VLDLR C:0.18 5.26±0.05a

5.52±0.07b 0.0049

rs2297404 ABCA1 G:0.07 5.39±0.05a

5.04±0.12b 0.0057

LDLC (mmol/L) rs7412 APOE T:0.08 3.37±0.05a

2.78±0.12b 7.84e-06

rs743507 NOS3 G:0.27 3.12±0.06a 3.45±0.08b 3.50±0.16

0.0017 rs1869592 VLDLR A:0.16 3.18±0.05a

3.50±0.09b 0.0020

rs7754840 CDKAL1 C:0.31 3.41±0.07a 3.04±0.08b 3.18±0.17

0.0029 rs2290465 VLDLR C:0.18 3.17±0.06a

3.47±0.08b 0.0031

rs17834108 ADD1 C:0.06 3.21±0.05a

3.64±0.13b 0.0031

12

rs11649653 IGR G:0.39 3.15±0.08a 3.16±0.08a 3.62±0.12b

0.0032 rs2000813 LIPG A:0.28 3.22±0.06a 3.22±0.07a 3.78±0.16b

0.0037

NEFA (mmol/L) rs854551 PON1 A:0.21 557.6±11.2a

618.2±14.5b 0.0012

rs829080 ABCC9 A:0.42 623.9±15.5a 565.3±12.4b 540.9±22.1b

0.0019 rs10128711 SPTY2D1 T:0.28 613.7±13.3a 545.1±14.8b 582.5±35.5

0.0033

rs266729 ACDC C:0.27 575.9±11.9a 564.6±14.5a 676.6±30.3b

0.0037 rs1365508 SLC27A2 A:0.45 603.3±15.8a 588.2±13.1a 528.7±19.3b

0.0087

THDL (mmol/L) rs2479409 PCSK9 G:0.35 1.119±0.019a 1.049±0.019b 1.187±0.036a

0.0010

rs2577261 LPIN1 A:0.09 1.089±0.013a

1.187±0.029b 0.0021

rs12691 CEBPA A:0.12 1.125±0.014a

1.041±0.025b 0.0032

rs2814982 IGR T:0.11 1.118±0.014a

1.024±0.028b 0.0032

rs16967952 CEBPA A:0.12 1.125±0.014a

1.041±0.025b 0.0033

rs4988300 LRP5 A:0.47 1.097±0.022 1.142±0.018a 1.051±0.024b

0.0093 TAG (mmol/L)

rs10753929 ADIPOR1 A:0.12 1.86±0.05a

1.55±0.09b 0.0019

rs2247056 IGR T:0.26 1.86±0.06a 1.78±0.07a 1.28±0.15b

0.0027 rs7241918 IGR G:0.17 1.88±0.05a

1.59±0.08b 0.0034

rs11761556 LEP C:0.45 1.87±0.07 1.65±0.06a 1.99±0.09b

0.0039 rs1113132 EXT2 G:0.26 1.70±0.06a 1.86±0.07 2.21±0.16b

0.0066

rs5051 AGT T:0.42 1.70±0.07a 1.92±0.06b 1.61±0.10a

0.0077 TRLC (mmol/L)

rs10010131 WFS1 A:0.38 0.4198±0.0199a 0.3098±0.0174b 0.3540±0.0322

0.0003 rs7241918 IGR G:0.17 0.3903±0.0146a

0.2981±0.0223b 0.0007

rs2267443 SREBF2 A:0.38 0.4046±0.0187a 0.3510±0.0153 0.2658±0.0323b

0.0008 rs2247056 IGR T:0.26 0.3763±0.0162a 0.3708±0.0203a 0.2188±0.0425b

0.0025

rs10753929 ADIPOR1 A:0.12 0.3780±0.0127a

0.2991±0.0233b 0.0032

TRLTG (mmol/L) rs10753929 ADIPOR1 A:0.12 0.921±0.030a

0.700±0.055b 0.0005

rs7241918 IGR G:0.17 0.928±0.033a

0.726±0.051b 0.0011

rs17610395 CPT1A T:0.09 0.831±0.029a

1.043±0.065b 0.0030

rs1260326 GCKR T:0.42 0.733±0.048a 0.906±0.039b 0.974±0.068b

0.0041 rs2247056 IGR T:0.26 0.913±0.037a 0.850±0.047a 0.570±0.098b

0.0057

rs11761556 LEP C:0.45 0.936±0.047a 0.782±0.038b 0.971±0.057a

0.0059 Apo B48 (mg/L)

rs3759348 GNB3 A:0.29 0.695±0.061a 0.791±0.071a 1.374±0.143b

9.90e-05 rs10457576 ENPP1 G:0.25 0.926±0.060a

0.639±0.066b 0.0016

rs10883689 KCNIP2 A:0.33 0.699±0.066a 0.948±0.067b 0.543±0.128a

0.0045 rs3764913 ACADL G:0.34 0.919±0.068a 0.634±0.067b 0.988±0.134

0.0046

rs12451328 ACE A:0.41 0.661±0.078a 0.806±0.065 1.106±0.110b

0.0052 rs2286963 ACADL C:0.34 0.907±0.068a 0.634±0.067b 1.005±0.133a

0.0052

rs735396 TCF1 G:0.35 0.703±0.067a 0.964±0.070b 0.620±0.118a

0.0076 Apo A1 (g/L)

rs5882 CETP G:0.32 1.39±0.02a 1.38±0.02a 1.52±0.03b

0.0012 rs4783962 CETP A:0.23 1.43±0.01a

1.36±0.02b 0.0014

rs12691 CEBPA A:0.13 1.42±0.01a

1.34±0.02b 0.0034

rs16967952 CEBPA A:0.13 1.42±0.01a

1.34±0.02b 0.0034

rs1137101 LEPR G:0.46 1.44±0.02a 1.37±0.01b 1.42±0.02

0.0037 rs2070667 APOC3 T:0.05 1.39±0.01a 1.49±0.03b

0.0040

rs2479409 PCSK9 G:0.35 1.42±0.02a 1.36±0.02b 1.44±0.03a

0.0054 rs1801224 CUBN C:0.33 1.37±0.02a 1.44±0.02b 1.38±0.03

0.0060

rs4246444 FASN A:0.25 1.41±0.01a 1.41±0.02a 1.29±0.04b

0.0065 rs700550 LRP2 G:0.39 1.36±0.02a 1.43±0.01b 1.40±0.03

0.0085

rs3742023 ACACB A:0.36 1.43±0.02a 1.40±0.02 1.33±0.03b

0.0113 Apo B (g/L)

rs7412 APOE T:0.08 1.046±0.011a

0.879±0.027b 3.30e-08

rs1869592 VLDLR A:0.15 0.996±0.013a

1.082±0.020b 0.0003

rs679899 APOB A:0.46 1.085±0.019a 0.994±0.016b 0.988±0.023b

0.0004 rs693 APOB G:0.49 1.089±0.021a 1.008±0.015b 0.974±0.022b

0.0006

rs11649653 IGR G:0.39 1.026±0.018 0.978±0.017a 1.095±0.028b

0.0015 rs11761556 LEP C:0.45 1.057±0.019a 0.982±0.016b 1.052±0.023a

0.0032

rs2774276 USF1 C:0.25 0.992±0.015a

1.054±0.016b 0.0044

rs1367117 APOB A:0.33 0.991±0.016a 1.030±0.016 1.109±0.034b

0.0051 rs4669781 LPIN1 T:0.04 1.029±0.011a 0.917±0.039b

0.0063

rs894160 PLIN A:0.30 1.042±0.015a 1.017±0.016a 0.919±0.037b

0.0083 Apo CII (mg/L)

rs1260326 GCKR T:0.42 43.1±1.6a 44.6±1.3a 52.8±2.2b

0.0013 rs10753929 ADIPOR1 A:0.12 46.9±0.9a

40.7±1.8b 0.0023

rs1251079 ACADM A:0.41 42.6±1.4a 45.5±1.2a 51.5±2.1b

0.0024 rs2247056 IGR T:0.26 47.2±1.2a 45.0±1.5a 35.0±3.3b

0.0025

rs2267443 SREBF2 A:0.38 47.5±1.4a 45.8±1.2a 38.4±2.4b

0.0044 rs2000813 LIPG A:0.29 45.4±1.2a 43.7±1.3a 53.8±2.8b

0.0064

rs11761556 LEP C:0.45 47.5±1.5a 42.7±1.2b 48.5±1.8a

0.0088 Apo CIII (mg/L)

rs2247056 IGR T:0.26 163.6±3.4a 155.9±4.3a 124.6±9.2b

0.0005 rs2953171 CAPN10 A:0.14 153.6±2.7a

171.2±4.6b 0.0012

rs10753929 ADIPOR1 A:0.12 162.3±2.7a

144.0±5.0b 0.0013

13

rs17368435.rs17231506 CETP A:0.29 159.0±3.4a 152.4±3.7a 185.2±8.5b

0.0021 rs174546 FADS1 T:0.32 167.1±3.8a 148.2±3.8b 158.2±7.9

0.0023

rs2040653 ABCC8 G:0.35 157.6±3.7a 153.7±3.4a 181.3±7.3b

0.0029 rs6927072 ESR1 A:0.33 150.7±3.6a 166.4±3.5b 154.7±7.3

0.0069

Apo E (mg/L) rs7412 APOE T:0.08 39.2±0.8a

53.5±1.9b 1.15e-11

rs17368435.rs17231506 CETP A:0.29 41.2±1.1a 39.4±1.2a 52.5±2.7b

4.97e-05 rs7205804 CETP A:0.41 40.9±1.3a 39.3±1.1a 48.0±1.8b

0.0002

rs5128 APOC3 C:0.08 40.1±0.8a

47.7±1.9b 0.0004

rs405509 APOE A:0.48 46.0±1.4a 39.9±1.0b 39.0±1.6b

0.0009 rs10753929 ADIPOR1 A:0.12 42.7±0.8a

36.7±1.6b 0.0009

rs2040653 ABCC8 G:0.35 38.5±1.2a 42.4±1.1b 47.5±2.3b

0.0011 rs6927072 ESR1 A:0.33 39.4±1.1a 44.2±1.1b 36.9±2.3a

0.0014

rs6557171 ESR1 A:0.32 40.3±1.1 43.8±1.2a 35.9±2.3b

0.0038 rs8107911 C3 G:0.25 39.7±1.0a 44.4±1.2b 37.6±3.1

0.0065

rs7134375 IGR A:0.42 45.0±1.4a 38.9±1.3b 41.0±1.9

0.0075 TRL Apo B (mg/L)

rs4667591 LRP2 A:0.24 44.6±2.4a 59.1±3.0b 45.4±7.3

0.0008 rs4520 APOC3 T:0.26 44.7±2.5a

56.4±2.7b 0.0015

rs4963 ADD1 C:0.18 46.0±2.2a

58.1±3.1b 0.0016

rs16856843 LRP2 A:0.14 46.7±2.1a

59.2±3.5b 0.0027

rs10753929 ADIPOR1 A:0.12 52.9±2.0a

40.0±3.8b 0.0030

rs4961 ADD1 A:0.19 46.2±2.2a

57.4±3.1b 0.0034

rs7134375 IGR A:0.42 55.4±2.9a 42.8±2.6b 50.7±3.9

0.0058 rs10010131 WFS1 A:0.38 55.1±2.9a 44.7±2.5b 40.4±4.7b

0.0060

GE117881_360 TCF1 G:0.41 46.9±3.2 55.7±2.6a 40.3±4.6b

0.0064 Abreviaturas: MAF, minor allele frequency, MM, major allele homozygotes, Mm, heterozygotes; mm, minor allele homozygotes. Los datos son medias±SE. Cuando un efecto recesivo es asumido, los numerous se muestran en la columna de Mm/mm. Dentro de cada fila, los valores que no comparten una letra en superíndice común son diferentes, con niveles de p<0.05.

14

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ApoA1~chr1:rs2479409 (PCSK9)

ApoA1~chr1:rs1137101 (LEPR)

ApoA1~chr2:rs700550 (LRP2)

ApoA1~chr10:rs1801224 (CUBN)

ApoA1~chr11:rs2070667 (APOC3)

ApoA1~chr12:rs3742023 (ACACB)

ApoA1~chr16:rs4783962 (CETP)ApoA1~chr16:rs5882 (CETP)

ApoA1~chr17:rs4246444 (FASN)

ApoA1~chr19:rs12691 (CEBPA)ApoA1~chr19:rs16967952 (CEBPA)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] Apo

A ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: ApoASNPs significativos tras bootstrapping: al menos 80% p<0,05

0

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ApoB~chr1:rs2774276 (USF1)TRLApoB~chr1:rs10753929 (ADIPOR1)

ApoB~chr2:rs4669781 (LPIN1)

ApoB~chr2:rs693 (APOB)ApoB~chr2:rs679899 (APOB)

ApoB~chr2:rs1367117 (APOB)

TRLApoB~chr2:rs4667591 (LRP2)

TRLApoB~chr2:rs16856843 (LRP2)ApoB48~chr2:rs2286963 (ACADL)ApoB48~chr2:rs3764913 (ACADL)

TRLApoB~chr4:rs4961 (ADD1)TRLApoB~chr4:rs4963 (ADD1)

TRLApoB~chr4:rs10010131 (WFS1)

ApoB48~chr6:rs10457576 (ENPP1)ApoB~chr7:rs11761556 (LEP)

ApoB~chr9:rs1869592 (VLDLR)

ApoB48~chr10:rs10883689 (KCNIP2)

TRLApoB~chr11:rs4520 (APOC3)

ApoB48~chr12:rs3759348 (GNB3)

TRLApoB~chr12:rs7134375 (IGR)TRLApoB~chr12:GE117881_360 (TCF1)ApoB48~chr12:rs735396 (TCF1)ApoB~chr15:rs894160 (PLIN)

ApoB~chr16:rs11649653 (IGR)

ApoB48~chr17:rs12451328 (ACE)

ApoB~chr19:rs7412 (APOE)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] Apo

B ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: ApoBSNPs significativos tras bootstrapping: al menos 80% p<0,05

15

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ApoCII~chr1:rs1251079 (ACADM)ApoCII~chr1:rs10753929 (ADIPOR1)

ApoCIII~chr1:rs10753929 (ADIPOR1)ApoCII~chr2:rs1260326 (GCKR)ApoCIII~chr2:rs2953171 (CAPN10)

ApoCII~chr6:rs2247056 (IGR)

ApoCIII~chr6:rs2247056 (IGR)

ApoCIII~chr6:rs6927072 (ESR1)ApoCII~chr7:rs11761556 (LEP)

ApoCIII~chr11:rs2040653 (ABCC8)ApoCIII~chr11:rs174546 (FADS1) ApoCIII~chr16:rs17368435.rs17231506 (CETP)

ApoCII~chr18:rs2000813 (LIPG)

ApoCII~chr22:rs2267443 (SREBF2)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] Apo

C ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: ApoCSNPs significativos tras bootstrapping: al menos 80% p<0,05

0

2

4

6

8

10

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ApoE~chr1:rs10753929 (ADIPOR1)

ApoE~chr6:rs6557171 (ESR1)ApoE~chr6:rs6927072 (ESR1) ApoE~chr11:rs2040653 (ABCC8)

ApoE~chr11:rs5128 (APOC3)

ApoE~chr12:rs7134375 (IGR)

ApoE~chr16:rs17368435.rs17231506 (CETP)

ApoE~chr16:rs7205804 (CETP)

ApoE~chr19:rs8107911 (C3)

ApoE~chr19:rs405509 (APOE)

ApoE~chr19:rs7412 (APOE)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] Apo

E ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: ApoESNPs significativos tras bootstrapping: al menos 80% p<0,05

16

0

1

2

3

4

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CHOL~chr2:rs693 (APOB)CHOL~chr2:rs1367117 (APOB)

CHOL~chr3:rs696217 (GHRL)CHOL~chr7:rs11761556 (LEP)

CHOL~chr7:rs1799983ac (NOS3)

CHOL~chr7:rs1799983gt (NOS3)CHOL~chr9:rs1869592 (VLDLR)

CHOL~chr9:rs2290465 (VLDLR)CHOL~chr9:rs2297404 (ABCA1)

CHOL~chr12:rs701106 (SCARB1)

CHOL~chr19:rs7412 (APOE)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] CH

OL

~ S

NP

+ Pa

rtner

# +

Age

+ Se

x +

BMI[0

]PRETRIAL: CHOL

SNPs significativos tras bootstrapping: al menos 80% p<0,05

0

1

2

3

4

5

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LDLC~chr4:rs17834108 (ADD1)LDLC~chr6:rs7754840 (CDKAL1)

LDLC~chr7:rs1799983gt (NOS3)

LDLC~chr7:rs743507 (NOS3)LDLC~chr9:rs1869592 (VLDLR)LDLC~chr9:rs2290465 (VLDLR) LDLC~chr16:rs11649653 (IGR)LDLC~chr18:rs2000813 (LIPG)

LDLC~chr19:rs7412 (APOE)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] LD

LC ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: LDLCSNPs significativos tras bootstrapping: al menos 80% p<0,05

17

0

1

2

3

4

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C181~chr1:rs4846914 (GALNT2)

C161~chr1:rs514230 (LOC100506810)C181~chr1:rs514230 (LOC100506810)

PlasmaMUFAAbs~chr1:rs514230 (LOC100506810)

PlasmaMUFAPerc~chr2:rs831022 (LRP2)C181~chr2:rs2286963 (ACADL)

C161~chr2:rs2953171 (CAPN10)C181~chr2:rs2953171 (CAPN10)

PlasmaMUFAAbs~chr2:rs2953171 (CAPN10)

C181~chr3:rs3856806 (PPARG)PlasmaMUFAAbs~chr3:rs3856806 (PPARG)

C201~chr4:rs11734408 (PPARGC1A)

C181~chr5:rs17572019 (PPARGC1B)PlasmaMUFAAbs~chr5:rs17572019 (PPARGC1B)

PlasmaMUFAPerc~chr6:rs1035798 (AGER)PlasmaMUFAPerc~chr6:rs1800624 (AGER)C181~chr6:rs6927072 (ESR1)

PlasmaMUFAAbs~chr6:rs6927072 (ESR1)

PlasmaMUFAPerc~chr9:rs2498429 (FRDA)

C201~chr11:rs4930610 (CPT1A)

C201~chr12:rs3759348 (GNB3)

PlasmaMUFAPerc~chr12:rs7968837 (ABCD2)




C181~chr12:rs6606697 (ACACB)PlasmaMUFAAbs~chr12:rs6606697 (ACACB)C181~chr19:rs4802703 (NR1H2)

PlasmaMUFAAbs~chr19:rs4802703 (NR1H2)

PlasmaMUFAPerc~chr22:rs2267443 (SREBF2)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] MU

FA ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: MUFASNPs significativos tras bootstrapping: al menos 80% p<0,05

0.0

0.5

1.0

1.5

2.0

2.5

3.0

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NEFA~chr3:rs266729 (ACDC)

NEFA~chr7:rs854551 (PON1)

NEFA~chr11:rs10128711 (SPTY2D1)

NEFA~chr12:rs829080 (ABCC9)

NEFA~chr15:rs1365508 (SLC27A2)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] NEF

A ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: NEFASNPs significativos tras bootstrapping: al menos 80% p<0,05

18

0

1

2

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PlasmaTotalFatAbs~chr1:rs6682411 (SLC27A3)

PlasmaTotalFatAbs~chr1:rs4846914 (GALNT2)

PlasmaTotalFatAbs~chr1:rs514230 (LOC100506810)

PlasmaTotalFatAbs~chr2:rs2286963 (ACADL)

PlasmaTotalFatAbs~chr2:rs2953171 (CAPN10)

PlasmaTotalFatAbs~chr3:rs3856806 (PPARG)

PlasmaTotalFatAbs~chr5:rs7732671 (PPARGC1B)

PlasmaTotalFatAbs~chr5:rs17572019 (PPARGC1B)

PlasmaTotalFatAbs~chr6:rs6927072 (ESR1)

PlasmaTotalFatAbs~chr18:rs4245232 (LIPG)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] Pla

sma

Tota

l Fat

~ S

NP

+ Pa

rtner

# +

Age

+ Se

x +

BMI[0

]PRETRIAL: Plasma Total Fat


0

1

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PlasmaPUFAAbs~chr1:rs6682411 (SLC27A3)

PlasmaPUFAPerc~chr1:rs10753929 (ADIPOR1)

PlasmaPUFAPerc~chr2:rs6544718 (ABCG8)

PlasmaPUFAAbs~chr2:rs2286963 (ACADL)

PlasmaPUFAAbs~chr5:rs17572019 (PPARGC1B)

PlasmaPUFAAbs~chr7:rs4731702 (IGR)

PlasmaPUFAPerc~chr8:rs2293889 (TRPS1)

PlasmaPUFAPerc~chr12:rs7968837 (ABCD2)

PlasmaPUFAPerc~chr12:rs10877201 (ABCD2)PlasmaPUFAPerc~chr12:rs6581228 (ABCD2)

PlasmaPUFAPerc~chr16:rs4783961 (CETP)

PlasmaPUFAAbs~chr18:rs4245232 (LIPG)

PlasmaPUFAAbs~chr18:rs2097055 (LIPG)

PlasmaPUFAPerc~chr22:rs2267443 (SREBF2)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] PU

FA ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: PUFASNPs significativos tras bootstrapping: al menos 80% p<0,05

19

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Plasman3Perc~chr1:rs4660293 (PABPC4)C225n3~chr2:rs1042031 (APOB)

C226n3~chr2:rs920418 (NCOA1)Plasman3Abs~chr2:rs920418 (NCOA1)

C204n3~chr2:rs1260326 (GCKR)

C225n3~chr2:rs1260326 (GCKR)

C225n3~chr2:rs4952689 (ABCG8)

Plasman3Perc~chr2:rs943 (HK2)

C184n3~chr2:rs4533467 (ABCA12)C225n3~chr2:rs4533467 (ABCA12)

C225n3~chr2:rs12464205 (ABCA12)

C204n3~chr2:rs2975776 (CAPN10)

C225n3~chr4:rs13107325 (SLC39A8)C225n3~chr4:rs4862417 (ACSL1)

Plasman3Perc~chr5:rs6453132 (CART)

Plasman3Perc~chr6:rs2076310 (RXRB)

C184n3~chr6:rs9488822 (FRK)

C225n3~chr6:rs2234693 (ESR1)

C205n3~chr7:rs4731702 (IGR)

C226n3~chr7:rs4731702 (IGR)Plasman3Abs~chr7:rs4731702 (IGR)

C205n3~chr10:rs1801232 (CUBN)

Plasman3Perc~chr11:rs659366 (UCP2)

Plasman3Perc~chr11:rs15763 (UCP3)

Plasman3Abs~chr12:rs1283816 (ABCC9)C184n3~chr12:rs829080 (ABCC9)

C226n3~chr12:rs829080 (ABCC9)

Plasman3Abs~chr12:rs829080 (ABCC9)

C225n3~chr12:rs2464196 (TCF1)

C225n3~chr15:rs894160 (PLIN)C226n3~chr15:rs894160 (PLIN)

C226n3~chr17:rs1053005 (STAT3)

C205n3~chr17:rs4291 (ACE)Plasman3Abs~chr17:rs4291 (ACE)C205n3~chr17:rs4295 (ACE)Plasman3Abs~chr17:rs4295 (ACE)

C205n3~chr17:rs4305 (ACE)Plasman3Abs~chr17:rs4305 (ACE)

C205n3~chr17:rs4343 (ACE)Plasman3Abs~chr17:rs4343 (ACE)

Plasman3Abs~chr18:rs2097055 (LIPG)


Plasman3Perc~chr18:rs2276269 (LIPG)


Plasman3Perc~chr18:rs6507931 (LIPG)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] PU

FAn3

~ S

NP

+ Pa

rtner

# +

Age

+ Se

x +

BMI[0

]PRETRIAL: PUFAn3


0

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C204n6~chr1:rs951545 (FABP3)Plasman6Perc~chr1:rs951545 (FABP3)

C182n6~chr1:rs6682411 (SLC27A3)

C183n6~chr1:rs6682411 (SLC27A3)

C204n6~chr1:rs6682411 (SLC27A3)Plasman6Abs~chr1:rs6682411 (SLC27A3)

Plasman6Perc~chr1:rs10753929 (ADIPOR1)C183n6~chr1:rs3889728 (AGT)

C203n6~chr1:rs3889728 (AGT)

C182n6~chr1:rs514230 (LOC100506810)Plasman6Abs~chr1:rs514230 (LOC100506810)

Plasman6Perc~chr2:rs6544718 (ABCG8)

C203n6~chr2:rs1143643 (IL1B)C182n6~chr2:rs2286963 (ACADL)

C183n6~chr2:rs2286963 (ACADL)

C203n6~chr2:rs2286963 (ACADL)Plasman6Abs~chr2:rs2286963 (ACADL)

C203n6~chr3:rs3856806 (PPARG)

C183n6~chr4:rs2970848 (PPARGC1A)C203n6~chr4:rs2970848 (PPARGC1A)

C224n6~chr4:rs1057613 (MTP)

C182n6~chr5:rs7732671 (PPARGC1B)

C182n6~chr5:rs17572019 (PPARGC1B)Plasman6Abs~chr5:rs17572019 (PPARGC1B)

C183n6~chr6:rs1799724 (TNF)

C203n6~chr6:rs3177928 (HLA−DRA)Plasman6Perc~chr6:rs2814944 (IGR)

C203n6~chr6:rs6927072 (ESR1)C203n6~chr7:rs1800797 (IL6)C203n6~chr7:rs1800795 (IL6)C203n6~chr7:rs2069832 (IL6)

C182n6~chr7:rs217420 (NPC1L1)C203n6~chr7:rs854551 (PON1)

C182n6~chr7:rs4731702 (IGR)C204n6~chr7:rs4731702 (IGR)

Plasman6Abs~chr7:rs4731702 (IGR)

C203n6~chr7:rs9770068 (INSIG1)

C224n6~chr9:rs1454627 (VLDLR)

C203n6~chr11:rs7480010 (LOC387761)

C183n6~chr11:rs174546 (FADS1)

C204n6~chr11:rs174546 (FADS1)

C224n6~chr11:rs638051 (LRP5)C183n6~chr11:rs5104 (APOA4)

Plasman6Perc~chr12:rs7968837 (ABCD2)

Plasman6Perc~chr12:rs10877201 (ABCD2)Plasman6Perc~chr12:rs6581228 (ABCD2)

C204n6~chr15:rs894160 (PLIN)

Plasman6Perc~chr16:rs4783961 (CETP)

C204n6~chr17:rs4291 (ACE)C204n6~chr17:rs4295 (ACE)C204n6~chr18:rs1785282 (LPIN2)

C204n6~chr18:rs4245232 (LIPG)

Plasman6Abs~chr18:rs4245232 (LIPG)C204n6~chr18:rs2097055 (LIPG)

Plasman6Abs~chr18:rs2097055 (LIPG)C204n6~chr18:rs7241918 (IGR)

C224n6~chr19:rs7257062 (C3)C224n6~chr19:rs2230204 (C3)

C203n6~chr19:rs1346490 (INSR)

C203n6~chr20:rs6031596 (HNF4A)

C224n6~chr22:rs9610329 (APOL6)

C183n6~chr22:rs136147 (APOL1)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] PU

FAn6

~ S

NP

+ Pa

rtner

# +

Age

+ Se

x +

BMI[0

]

PRETRIAL: PUFAn6SNPs significativos tras bootstrapping: al menos 80% p<0,05

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PlasmaSFAAbs~chr1:rs6682411 (SLC27A3)

PlasmaSFAPerc~chr1:rs10158450 (SLC27A3)

C180~chr1:rs4846914 (GALNT2)

PlasmaSFAAbs~chr1:rs4846914 (GALNT2)C180~chr1:rs3889728 (AGT)

C140~chr1:rs514230 (LOC100506810)

C160~chr1:rs514230 (LOC100506810)

PlasmaSFAAbs~chr1:rs514230 (LOC100506810)

C160~chr2:rs2286963 (ACADL)

C180~chr2:rs2286963 (ACADL)

PlasmaSFAAbs~chr2:rs2286963 (ACADL)

C160~chr2:rs2953171 (CAPN10)

C180~chr2:rs2953171 (CAPN10)

PlasmaSFAAbs~chr2:rs2953171 (CAPN10)

C180~chr2:rs2953161 (CAPN10)

C160~chr3:rs3856806 (PPARG)PlasmaSFAAbs~chr3:rs3856806 (PPARG)

C140~chr3:rs6788865 (LIPH)

C180~chr4:rs2970848 (PPARGC1A)C140~chr4:rs4580704 (CLOCK)

C140~chr4:rs13107325 (SLC39A8)

C160~chr5:rs7732671 (PPARGC1B)

PlasmaSFAAbs~chr5:rs7732671 (PPARGC1B)

C160~chr5:rs17572019 (PPARGC1B)

PlasmaSFAAbs~chr5:rs17572019 (PPARGC1B)C180~chr6:rs2247056 (IGR)

PlasmaSFAPerc~chr8:rs2293889 (TRPS1)

PlasmaSFAPerc~chr16:rs4783961 (CETP)C180~chr17:rs2293152 (STAT3)

C180~chr20:rs6031596 (HNF4A)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] SFA

~ S

NP

+ Pa

rtner

# +

Age

+ Se

x +

BMI[0

]PRETRIAL: SFA


0.0

0.5

1.0

1.5

2.0

2.5

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TAG~chr1:rs10753929 (ADIPOR1)

TAG~chr1:rs5051 (AGT)

TAG~chr6:rs2247056 (IGR)

TAG~chr7:rs11761556 (LEP)

TAG~chr11:rs1113132 (EXT2)

TAG~chr18:rs7241918 (IGR)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] TAG

~ S

NP

+ Pa

rtner

# +

Age

+ Se

x +

BMI[0

]

PRETRIAL: TAGSNPs significativos tras bootstrapping: al menos 80% p<0,05

21

0.0

0.5

1.0

1.5

2.0

2.5

3.0

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THDL~chr1:rs2479409 (PCSK9)

THDL~chr2:rs2577261 (LPIN1)

THDL~chr6:rs2814982 (IGR)

THDL~chr11:rs4988300 (LRP5)

THDL~chr19:rs12691 (CEBPA)THDL~chr19:rs16967952 (CEBPA)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] TH

DL

~ S

NP

+ Pa

rtner

# +

Age

+ Se

x +

BMI[0

]PRETRIAL: THDL


0

1

2

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TRLC~chr1:rs10753929 (ADIPOR1)

TRLC~chr4:rs10010131 (WFS1)

TRLC~chr6:rs2247056 (IGR)

TRLC~chr18:rs7241918 (IGR)TRLC~chr22:rs2267443 (SREBF2)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] TR

LC ~

SN

P +

Partn

er#

+ Ag

e +

Sex

+ BM

I[0]

PRETRIAL: TRLCSNPs significativos tras bootstrapping: al menos 80% p<0,05

22

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TRLTG~chr1:rs10753929 (ADIPOR1)

TRLTG~chr2:rs1260326 (GCKR)

TRLTG~chr6:rs2247056 (IGR)TRLTG~chr7:rs11761556 (LEP)

TRLTG~chr11:rs17610395 (CPT1A)

TRLTG~chr18:rs7241918 (IGR)

0.05

0.001

1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 18 19 20 22 XChromosome

−Log

10(p

)[0

] TR

LTG

~ S

NP

+ Pa

rtner

# +

Age

+ Se

x +

BMI[0

]PRETRIAL: TRLTG


23

DISCUSIÓN

En nuestro trabajo se han investigado el efecto de 904 SNPs sobre 20 variables diferentes

relacionadas en el metabolismo lipídico en 450 pacientes con síndrome metabólico. La mayoría

de los principales SNPs identificados que afectan a los marcadores en ayunas del metabolismo

de los lípidos son genes que codifican receptores o transportadores relacionados con el

metabolismo energético, como ABCD2, ADIPOR1, CETP, APOB, APOE, LEP, LIPG, VLDLR,

CEBPA, LRP2 o SREBF2, de acuerdo a descripciones realizadas en la literatura previamente8.

Algunos de los genes identificados se relacionan también con fenómenos de oxidación (NOS3) y

otros se hayan en regiones intergénicas (IGR).

En este trabajo, hemos utilizado 904 SNPs seleccionados sobre una base de plausibilidad

biológica, y se han relacionado con 20 de las variables más significativas que definen el

metabolismo lipídico en el síndrome metabólico. Con este acercamiento, seleccionamos SNPs

que se han descrito como relacionados con el metabolismo de los lípidos en humanos y se han

analizados conjuntamente, tratando de determinar los verdaderos efectos de todos ellos a la vez.

Nuestros resultados preliminares obtenidos previamente mediante pruebas de ANOVA fueron

tratados de forma cautelosa, y se utilizaron técnicas de remuestreo (bootstraping) para reducir al

mínimo la posibilidad de falsos descubrimientos. Los SNPs finales se combinaron para

identificar aquellos que más probablemente pueden ser los responsables de los efectos

observados.

Los SNPs identificados en cada variable son, al menos desde un punto de vista teórico,

verosímiles. Por ejemplo, el rs7412 ApoE ha sido clasificado como el principal SNP que influye

24

en las concentraciones de colesterol, c-LDL, ApoB y ApoE. La proteína codificada por este gen

es la principal apoproteína de los quilomicrones, y es esencial para el catabolismo normal de

partículas ricas en triacilgliceroles. Situado en el cromosoma 19, este gen se ha relacionado con

los genes de la ApoC1 y ApoC2 y más recientemente con la composición corporal grasa11.

Un hallazgo interesante en nuestro trabajo es la asociación encontrada entre rs10158450

SLC27A3 y la concentración de grasa saturada en plasma. Esta mutación en el gen SLC27A3

(solute carrier family 27, member 3), localizado en el cromosoma 1, ha sido relacionada en

estudios previos con el desarrollo de enfermedad mineral ósea en mujeres premenopáusicas12,

pero hasta la fecha no había sido identificado su relación con alteraciones en el metabolismo de

los lípidos.

De igual forma, otro hallazgo interesante derivado de nuestro trabajo lo constituye la relación

detectada entre diferentes variaciones genéticas del gen ABCD2 (ATP-binding cassette, sub-

family D (ALD), member 2) y las concentraciones plasmáticas de ácidos grasos

monoinsaturados (MUFA), poliinsaturados (PUFA) y más específicamente dentro de estos

últimos con los ácidos omega 6. La proteína codificada por el gen ABCD2 pertenece a la

superfamilia de las ATP-binding cassette. Estas proteínas son responsables del transporte

diversas moléculas a través de las membrana extra e intracelular, y están implicadas en el

transporte peroxisomal de ácidos grasos en la organela. Hasta la fecha, mutaciones en este gen se

habían relacionado en la literatura tan sólo con enfermedades desmielinizantes13, constituyendo

nuestros hallazgos un descubrimiento novedoso al relacionarla con las concentraciones

plasmáticas de ácidos grasos.

25

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=11000

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=11000

Aunque estadísticamente significativos, nuestros hallazgos necesitan ser replicados en

poblaciones mayores y en estudios mecanicistas. Una vez identificadas estas asociaciaciones,

podrán ser reproducidas en modelos in vitro e in vivo y examindas en búsqueda de explicaciones

mecanicistas subyacentes.

Por otra parte, otros SNPs que no han sido evaluados en este trabajo pueden ser también de

interés en el metabolismo de los lípidos y podrán ser evalados en futuros trabajos utilizando los

modelos desarrollados en este trabajo.

26

CONCLUSIONES

En conclusión, se han identificado los principales SNPs que influyen en el metabolismo lipídico

de entre 904 variantes génicas seleccionadas en la cohorte LIPGENE de pacientes con síndrome

metabólico. Nuestros resultados apoyan los hallazgos de genes conocidos previamente y

relacionados con el metabolismo de los lípidos, y sugieren la importancia de otros genes

relacionados con el transporte peroxisomal y el metabolsimo energético.

27

BIBLIOGRAFÍA

1. Mottillo S, Filion KB, Genest J, et al. The metabolic syndrome and cardiovascular risk a

systematic review and meta-analysis. Journal of the American College of Cardiology

2010;56:1113-32.

2. Grundy SM. Definition of Metabolic Syndrome: Report of the National Heart, Lung, and

Blood Institute/American Heart Association Conference on Scientific Issues Related to

Definition. 2004;109:433-8.

3. Roche HM, Phillips C, Gibney MJ. The metabolic syndrome: the crossroads of diet and

genetics. The Proceedings of the Nutrition Society 2005;64:371-7.

4. Phillips C, Lopez-Miranda J, Perez-Jimenez F, McManus R, Roche HM. Genetic and

nutrient determinants of the metabolic syndrome. Current opinion in cardiology

2006;21:185-93.

5. Delgado-Lista J, Perez-Martinez P, García-Rios A, et al. Pleiotropic effects of TCF7L2

gene variants and its modulation in the metabolic syndrome: From the LIPGENE study.

2011;214:110-6.

6. Kraja AT, Vaidya D, Pankow JS, et al. A bivariate genome-wide approach to metabolic

syndrome: STAMPEED consortium. Diabetes 2011;60:1329-39.

7. Kristiansson K, Perola M, Tikkanen E, et al. Genome-wide screen for metabolic

syndrome susceptibility Loci reveals strong lipid gene contribution but no evidence for

common genetic basis for clustering of metabolic syndrome traits. Circ Cardiovasc Genet

2012;5:242-9.

8. Phillips CM. Nutrigenetics and metabolic disease: current status and implications for

personalised nutrition. Nutrients 2013;5:32-57.

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9. Delgado-Lista J, Perez-Martinez P, Garcia-Rios A, et al. Pleiotropic effects of TCF7L2

gene variants and its modulation in the metabolic syndrome: from the LIPGENE study.

Atherosclerosis 2011;214:110-6.

10. Shaw DI, Tierney AC, McCarthy S, et al. LIPGENE food-exchange model for alteration

of dietary fat quantity and quality in free-living participants from eight European

countries. The British journal of nutrition 2009;101:750-9.

11. Tejedor MT, Garcia-Sobreviela MP, Ledesma M, Arbones-Mainar JM. The

apolipoprotein E polymorphism rs7412 associates with body fatness independently of

plasma lipids in middle aged men. PloS one 2014;9:e108605.

12. Ichikawa S, Koller DL, Curry LR, et al. Identification of a linkage disequilibrium block

in chromosome 1q associated with BMD in premenopausal white women. Journal of

bone and mineral research : the official journal of the American Society for Bone and

Mineral Research 2008;23:1680-8.

13. Petroni A, Cappa M, Carissimi R, Blasevich M, Uziel G. Effect of testosterone

metabolites on ABC half-transporter relative gene expression in X-linked

adrenoleukodystrophy. Journal of inherited metabolic disease 2007;30:828.

29

Documents

PRINCIPALES SNPs QUE MODULAN EL METABOLISMO …€¦ · Ten paciencia con todo aquello que no se ha resuelto en tu corazón e intenta amar las preguntas por sí mismas, como si fueran